ORIGINAL_ARTICLE
Multi-objective sequence dependent setup times hybrid flowshop scheduling: A literature review
Multi-criteria sequence dependent setup times scheduling problems exist almost everywhere in real modern manufacturing world environments. Among them, Sequence Dependent Setup Times-Multi-Objective Hybrid Flowshop Scheduling Problem (SDST-MOHFSP) has been an intensifying attention of researchers and practitioners in the last three decades. In this paper, we briefly summarized and classified the current standing of SDST-MOHFSP. All publications are categorized regarding the solution methods, as well as the structure of the hybrid flowshop which helps researcher and practitioner to use/modify proper solution algorithm for solving their specific problem. Furthermore, based on the review of the existing papers, the need for future research is recognized. Accordingly, by recognizing the research gaps, a large number of recommendations for further study have been proposed.
https://www.riejournal.com/article_79915_0bd18bf32f00593d982ccc339a277610.pdf
2018-11-01
254
306
10.22105/riej.2018.145136.1057
Multi-objective algorithms
hybrid flowshop scheduling
Sequence Dependent Setup Times
exact methods
heuristic and metaheuristic algorithms
Literature Review
F.
Ghassemi Tari
ghasemi@sharif.edu
1
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
LEAD_AUTHOR
M.
Rezapour Niari
maryam.rezapour1990@yahoo.com
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
AUTHOR
[1] Abiri, M., Zandieh, M., & Tabriz, A. A. (2009). A tabu search approach to hybrid flow shops scheduling with sequence-dependent setup times. Journal of applied sciences, 9(9), 1740-1745.
1
[2] Abyaneh, S. H., & Zandieh, M. (2012). Bi-objective hybrid flow shop scheduling with sequence-dependent setup times and limited buffers. The Int. J. of Ad. Manuf. Tech, 58(1-4), 309-325.
2
[3] Acosta, J. H. T., González, V. A. P., & Bello C. A. L. (2013). A genetic algorithm for simultaneous scheduling problem in flexible flow shop environments with unrelated parallel machines, setup time and multiple criteria. Int. Conference on Advanced Manuf. Engineering and Tech.
3
[4] Agnetis, A., Pacifici, A., Ross, F., Lucertini, M., Nicoletti, S., Nicolo, F., & Pesaro, E. (1997). Scheduling of flexible flow lines in an automobile assembly plant. Eur. J. of Operational Res. 97(2), 348-362.
4
[5] Alaei, R., & Ghassemi-Tari, F. (2011). Development of a genetic algorithm for advertising time allocation problems. Journal of industrial & systems engineering, 4(4), 245-255.
5
[6] Allahverdi, A., Ng, C., Cheng, T.E., Kovalyov, M.Y. (2008). A survey of scheduling problems with setup times or costs. European journal of operational research, 187(3), 985-1032.
6
[7] Allahverdi, A., & Aydilek, H. (2015). The two stage assembly flowshop scheduling problem to minimize total tardiness. Journal of intelligent manufacturing, 26(2), 225-237.
7
[8] Allahverdi, A, (2015). The third comprehensive survey on scheduling problems with setup times/costs. European journal of operational research, 246(2), 345-378.
8
[9] Alfieri, A, (2009). Workload simulation and optimisation in multi-criteria hybrid flowshop scheduling: a case study. International journal of production research, 47(18), 5129-5145.
9
[10] Amirian, H., Sahraeian, R. (2015). Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect. Int. J. of Production Res. 53(19), 5962-5976.
10
[11] Andrés, C., Albarracı́n, J. M., Tormo, G., Vicens, E., & Garcı́a-Sabater, J. P. (2005.) Group technology in a hybrid flowshop environment: A case study. European J. of Operational Res, 167(1), 272-281.
11
[12] Ashrafi, M., Davoudpour, H., & Abbassi, M. (2014). Investigating the efficiency of GRASP for the SDST HFS with controllable processing times and assignable due dates. Handbook of research on novel soft computing intel. Algorithms: Theory and Pract. Appl (pp. 538-567). IGI Global, 538-567.
12
[13] Attar, S., Mohammadi, M., Tavakkoli-Moghaddam, R., Yaghoubi, S. (2014). Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints. Int. J. of Comp. Integrated Manuf, 27(5), 450-469.
13
[14] Azab, SS., & Hefny, HA. (2017). Swarm intelligence in semi-supervised classification. Data mining and knowledge engineering. 9(5): 99-103.
14
[15] Barabási, B. A. L., & Bonabeau, E. (2003). Scale-free. Scientific american, 288(5), 50-59.
15
[16] Behnamian, J., Zandieh, M., Fatemi Ghomi, S. M. T. (2009a). Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm. Expert systems with applications, 36(6), 9637-9644.
16
[17] Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2009b). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert systems with applications, 36(8), 11057-11069.
17
[18] Behnamian, J, Zandieh, M., & Fatemi Ghomi, S. M. T. (2009c). Due window scheduling with sequence-dependent setup on parallel machines using three hybrid metaheuristic algorithms. The Int. J. of Ad. Man.Tech, 44(7-8):795-808.
18
[19] Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2010a). Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times. International journal of production research, 48(5), 1415-1438.
19
[20] Behnamian, J., Zandieh, M., & Fatemi Ghomi, S. M. T. (2010b). A multi-phase covering Pareto-optimal ront method to multi-objective parallel machine scheduling. Int. J. of Prod. Res, 48(17), 4949-4976.
20
[21] Behnamian, J., & Zandieh, M. (2011). A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Sys. with Appl, 38(12), 14490-14498.
21
[22] Behnamian, J., Zandieh, M., & Fatemi Ghomi, S. M. T. (2011). Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique. Soft computing, 15(7), 1313-1331.
22
[23] Behnamian, J., & Fatemi Ghomi, S.M.T. (2011). Hybrid flowshop scheduling with machine and resource-dependent processing times. Applied mathematical modelling, 35(3), 1107-1123.
23
[24] Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2012). Hybrid flowshop scheduling with sequence‐dependent setup times by hybridizing max–min ant system, simulated annealing and variable neighbourhood search. Expert systems, 29(2), 156-169.
24
[25] Behnamian, J., & Zandieh, M. (2013). Earliness and tardiness minimizing on a realistic hybrid flowshop scheduling with learning effect by advanced metaheuristic. Arabian J. for Sc. & Eng. 1-14.
25
[26] Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2014). Realistic variant of just-in-time flowshop scheduling: Integration of Lp-metric method in PSO-like algorithm. The Int. J. of Ad. Man.Tech, 75(9-12), 1787-1797.
26
[27] Behnamian, J. (2014). Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function. The Int. J. of Ad. Manuf. Tech, 74(1-4), 267-283.
27
[28] Blum, C., Roli, A., & Sampels, M. (2008). Hybrid metaheuristics: an emerging approach to optimization (Vol. 114). Springer.
28
[29] Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied soft computing, 11(6), 4135-4151.
29
[30] Bonabeau, E., Dorigo, M., Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. Oxford university press.
30
[31] Bozorgirad, M. A., & Logendran, R. (2016). A comparison of local search algorithms with population-based algorithms in hybrid flow shop scheduling problems with realistic characteristics. The international journal of advanced manufacturing technology, 83(5-8): 1135-1151.
31
[32] Brintha, N., Benedict, S., & Jappes, J. W. (2017). A Bio-Inspired Hybrid Computation for Managing and Scheduling Virtual Resources using Cloud Concepts. Appl. math, 11(2),565-572.
32
[33] Brown, S., McGarvey, R., & Ventura, J. (2004). Total flowtime and makespan for a no-wait m-machine flowshop with set-up times separated. J. of the Operational Res. Society, 55(6), 614-621.
33
[34] Burtseva, L, Yaurima, V, Parra, RR. (2010). Scheduling methods for hybrid flow shops with setup times. T. Aized, Ed, Future Manufacturing Systems (pp. 137-162). Croatia: Sciyo.
34
[35] Calvet, L., Armas, J. D., Masip, D., & Juan A. A. (2017). Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open mathematics, 15(1), 261-280.
35
[36] Campbell, H. G., Dudek, R. A., & Smith, M. L. (1970). A heuristic algorithm for the n job, m machine sequencing problem. Management science, 16(10), B-630-B-637.
36
[37] Campos, S. C., Arroyo, J. E. C., & Tavares, R. G. (2017). A general VNS heuristic for a three-stage assembly flow shop scheduling problem. In Madureira, A., Abraham, A., Gamboa, D., & Novais, P. (Eds). Intel.Sys. Design and Appl. Advances in Intelligent Systems and Computing. Springer, Cham.
37
[38] Campos, S. C., & Arroyo, J. E. C. (2014). NSGA-II with iterated greedy for a bi-objective three-stage assembly flowshop scheduling problem. Proceedings of the 2014 annual conference on genetic and evolutionary computationly. Vancouver, Canada.
38
[39] Chang, P. C., Hsieh, J. C., Wang, Y. W. (2003). Genetic algorithms applied in BOPP film scheduling problems: minimizing total absolute deviation and setup times. App. Soft Comp, 3(2), 139-148.
39
[40] Chang, J., Yan, W., & Shao, H. (2004). Scheduling a two-stage no-wait hybrid flowshop with separated setup and removal times. American control conference. Boston, MA, USA.
40
[41] Chang, P. C., Chen, S. H., Fan, C. Y., & Chan, C. L. (2008). Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems. Appl. Math. and Comp, 205(2), 550-561.
41
[42] Chiandussi, G., Codegone, M., Ferrero, S., & Varesio, F. E. (2012). Comparison of multi-objective optimization methodologies for engineering applications. Computers & mathematics with applications, 63(5), 912-942.
42
[43] Cho, H. M., & Jeong, I. J. (2017). A two-level method of production planning and scheduling for bi-objective reentrant hybrid flow shops. Computers & industrial engineering, 106, 174-181.
43
[44] Choi, B. C., & Park, M. J. (2016). An ordered flow shop with two agents. Asia-pacific journal of operational research, 33(05).
44
[45] Ciavotta, M., Minella, G., & Ruiz, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. Eur. J. of Operational Res. 227(2), 301-313.
45
[46] Cochran, J. K., Horng, S. M., & Fowler, J. W. (2003). A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comp. & Operations Res, 30(7), 1087-1102.
46
[47] Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5). Springer-Verlag US.
47
[48] Crowder, B. (2006). Minimizing the makespan in a flexible flowshop with sequence dependent setup times, uniform machines, and limited buffers. West Virginia University.
48
[49] Dannenbring, D. G. (1977). An evaluation of flow shop sequencing heuristics. Management science, 23(11), 1174-1182.
49
[50] Davoudpour, H., & Ashrafi, M. (2009). Solving multi-objective SDST flexible flow shop using GRASP algorithm. The International J. of Advanced Manufacturing Tech, 44(7), 737-747.
50
[51] Defersha, F. M. (2015). A simulated annealing with multiple-search paths and parallel computation for a comprehensive flowshop scheduling problem. Int. Trans.in Operations Res. 22(4), 669–691.
51
[52] De Weck, O. L. (2004). Multiobjective optimization: History and promise. The third china-japan-korea joint symposium on optimization of structural and mechanical systems. Kanazawa, Japan.
52
[53] Dhingra, A. K., & Chandna, P. (2015). Hybrid genetic algorithm for SDST flow shop scheduling with due dates: a case study. Int. J. of Advanced Operations Management, 2(3-4), 141-161.
53
[54] Du, X., Ji, M., Li, Z., & Liu, B. (2016). Scheduling of stochastic distributed assembly flowshop under complex constraints. IEEE Symposium Series on Comp. Intel. Athens, Greece.
54
[55] Ebrahimi, M., Fatemi Ghomi, S. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Appl. Math. Modelling, 38(9), 2490-2504.
55
[56] Eren, T. (2010). A bicriteria m-machine flowshop scheduling with sequence-dependent setup times. Applied mathematical modelling, 34(2), 284-293.
56
[57] Eskandari, H., & Hosseinzadeh, A. (2014.) A variable neighbourhood search for hybrid flow-shop scheduling problem with rework and set-up times. Journal of the operational research society, 65(8), 1221-1231.
57
[58] Fadaei, M., & Zandieh, M. (2013). Scheduling a bi-objective hybrid flow Shop with sequence-dependent family setup times using metaheuristics. Arabian J. for Sci. & Eng, 38(8), 2233-2244.
58
[59] Farahmand-Mehr, M., Fattahi, P., Kazemi, M., Zarei, H., & Piri, A. (2014). An efficient genetic algorithm for a hybrid flow shop scheduling problem with time lags and sequence-dependent setup time. Manufacturing review, 1(21), 1-10.
59
[60] Fattahi, P., Hosseini, S., & Jolai, F. (2013a). A mathematical model and extension algorithm for assembly flexible flow shop scheduling problem. The Int. J. of Advanced Manuf. Tech, 65(5-8), 787–802.
60
[61] Fattahi, P., Hosseini, S., & Jolai, F. (2013b). Some heuristics for the hybrid flow shop scheduling problem with setup and assembly operations. Int. J. of Industrial Eng. Computations, 4(3), 393-416.
61
[62] Fattahi, P., Hosseini, S., Jolai, F., & Tavakkoli-Moghaddam, R. (2014). A branch and bound algorithm for hybrid flow shop scheduling problem with setup time and assembly operations. Appl. Math. Mod, 38(1),119-134.
62
[63] Frisch, A. M., Hnich, B., Kiziltan, Z., Miguel, I., & Walsh, T. (2006). Propagation algorithms for lexicographic ordering constraints. Artificial intelligence, 170(10), 803-834.
63
[64] Fu, Y., Wang, H., Huang, M., Ding, J., & Tian, G. (2017). Multiobjective flow shop deteriorating scheduling problem via an adaptive multipopulation genetic algorithm. Proceedings of the institution of mechanical engineers part b journal of engineering manufacture. SAGE Publishing.
64
[65] Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of np-completeness, computers and intractability: A guide to the theory of NP-Comp. WH Freeman & Co. New York, USA.
65
[66] Ghafari, E., & Sahraeian, R. (2014). A two-stage hybrid flowshop scheduling problem with serial batching. International journal of industrial engineering and production research, 25(1), 55-63.
66
[67] Ghassemi-Tari, F., & Olfat, L. (2004). Two COVERT based algorithms for solving the generalized flow shop problems. Proceedings of the 34th Int. Con. on Comp. and Industrial Eng. San Francisco, CA, USA.
67
[68] Ghassemi-Tari, F., & Olfat, L. (2007). Development of a set of algorithms for the multi-project scheduling problems. Journal of industrial and systems engineering, 1(1), 11-17.
68
[69] Ghassemi-Tari, F., & Olfat, L. (2008). Covert based algorithms for solving the generalized tardiness flow shop problems. Journal of industrial and systems engineering, 2(3), 197-213.
69
[70] Ghassemi-Tari, F., & Olfat, L. (2010). A set of algorithms for solving the generalized tardiness flowshop problems. Journal of industrial and systems engineering, 4(3),156-166.
70
[71] Ghassemi-Tari, F. & Alaei, R. (2013). Scheduling TV commercials using genetic algorithms. International journal of production research, 51(16), 4921-4929.
71
[72] Ghassemi-Tari, F., & Olfat, L. (2014). Heuristic rules for tardiness problem in flow shop with intermediate due dates. The Int. J. of Advanced Manuf. Tech, 71(1-4), 381-393.
72
[73] Ghassemi Tari, F. & Hashemi Z. (2016). A priority based genetic algorithm for nonlinear transportation costs problems. Computers and industrial engineering, 96(c).
73
[74] Ghassemi-Tari, F., & Meshkinfam, S. (2017). Improving performance of GAs by use of selective breading evolutionary process. British J. of Math. & Computer Sci, 22(3), 1-21.
74
[75] Gicquel, C., Hege, L.,. Minoux, M., & Van, W. (2012). A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints. Computers and operations research, 39(3), 629-636.
75
[76] Glover, F. (1977). Heuristics for integer programming using surrogate constraints. Dec. Sc, 8(1), 156-166.
76
[77] Gholami, M., Zandieh, M., & Alem-Tabriz, A. (2009). Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. The int. j. of ad. manuf. Tech, 42(1-2), 189–201.
77
[78] Gholami S., & Rajaee Abyaneh F. (2016). Efficient algorithms for solving flexible flow shop scheduling problem with unrelated parallel machines and sequence-dependent setup times considering earliness/tardiness minimization.
78
[79] Gómez-Gasquet, P., Andrés, C., & Lario, F. C. (2012). An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Exp. Sys.with Appl, 39(9), 8095-8107.
79
[80] Guinet, A. (1991) Textile production systems: a succession of non-identical parallel processor shops. Journal of the operational research society, 655-671.
80
[81] Gupta, J. N. (1971). A functional heuristic algorithm for the flowshop scheduling problem. Journal of the operational research society, 22(1), 39-47.
81
[82] Gupta J. N. (1979). A review of flowshop scheduling research. In Ritzman, L. P., Krajewski, L.J., Berry, W.L., Goodman, S.H., Hardy, S.T., & Vitt, L.D. (Eds), Disaggregation. Springer, Dordrecht, 363-388.
82
[83] Gupta, J. N., & Darrow, W. P. (1986). The two-machine sequence dependent flowshop scheduling problem. European journal of operational research, 24(3), 439-446.
83
[84] Gupta, D., Sharma, S., & Nailwal, K. K. (2012). A bicriteria two machine flowshop scheduling with sequence dependent setup time. International journal of mathematical sciences, 11(3-4), 183-196.
84
[85] Ha, B. B., & Duc, N. N. (2013). Multiple trajectory search for large scale global optimization search-based software testing: past, present and future Wolf search algorithm with ephemeral memory. Int. Conf. on Comp. and Commun. Tech. Res. Innov. and Vision for the Future. Hanoi, Vietnam.
85
[86] Haddad, M. N., Cota, L. P., Souza, M. J. F., & Maculan, N. (2014). AIV: A heuristic algorithm based on iterated local search and variable neighborhood descent for solving the unrelated parallel machine scheduling problem with setup times. 16th international conference on enterprise information systems. Lisboa, Portugal.
86
[87] Hakimzadeh Abyaneh, S., & Zandieh, M. (2012). Bi-objective hybrid flow shop scheduling with sequence-dependent setup times and limited buffers. Int J Adv Manuf Tech, 58(1–4), 309–325.
87
[88] Harbaoui, H., Bellenguez-Morineau, O., & Khalfallah, S. (2016). Scheduling a two-stage hybrid flow shop with dedicated machines, time lags and sequence-dependent family setup times. IEEE Int. Conference on Systems, Man, and Cybernetics (SMC). Budapest, Hungary.
88
[89] Hashemi, Z., & Tari, F. G. (2018). A Prufer-based genetic algorithm for allocation of the vehicles in a discounted transportation cost system. International journal of systems science: Operations & logistics, 5(1), 1-15.
89
[90] Hatami, S., Ebrahimnejad, S., Tavakkoli-Moghaddam, R., & Maboudian, Y. (2010). Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times. The international journal of advanced manufacturing technology, 50(9), 1153-1164.
90
[91] Hatami, S., García, R. R., Romano, C. A. (2015). The distributed assembly parallel machine scheduling problem with eligibility constraints. Int. J. of Prod. Manag. and Eng, 3(1), 13-23.
91
[92] He, D.W., Kusiak, A., & Artiba A. (1996). A scheduling problem in glass manufacturing. IIE transactions 28(2), 129-139.
92
[93] Hecker, F. T., Hussein, W. B., Paquet-Durand, O., Hussein, M. A., & Becker, T. (2013). A case study on using evolutionary algorithms to optimize bakery production planning. Expert systems with applications, 40 (17), 6837-6847.
93
[94] Hecker, F. T., Stanke, M., Becker, T., & Hitzmann, B. (2014). Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery. Expert systems with applications, 41 (13), 5882-5891.
94
[95] Hekmatfar, M., Ghomi, S. M. T., & Karimi, B. (2011). Two stage reentrant hybrid flow shop with setup times and the criterion of minimizing makespan. Applied soft computing, 11(8), 4530-4539.
95
[96] Hendizadeh, S. H., ElMekkawy, T. Y., & Wang, G. G. (2007). Bi-criteria scheduling of a flowshop manufacturing cell with sequence dependent setup times. European journal of industrial engineering, 1(4), 391-413.
96
[97] Hidri, L., & Gharbi, A. (2017). New efficient lower bound for the Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks. IEEE access, 5, 6121-6133.
97
[98] Hosseini, S. M. H. (2016). Modeling the hybrid flow shop scheduling problem followed by an assembly stage considering aging effects and preventive maintenance activities. International Journal of Supply and Operations management, 3(1), 1215-1233.
98
[99] Javadian, N., Amiri-Aref, M., Hadighi, A., Kazemi, M., & Moradi, A. (2010). Flexible flow shop with sequence-dependent setup times and machine availability constraints. International Journal of management science and engineering management, 5(3), 219-226.
99
[100] Javadian, N., Fattahi, P., Farahmand-Mehr, M., Amiri-Aref, M., & Kazemi, M. (2012). An immune algorithm for hybrid flow shop scheduling problem with time lags and sequence-dependent setup times. The international journal of advanced manufacturing technology, 63(1), 337-348.
100
[101] Jin, Z., Ohno, K., Ito, T., & Elmaghraby, S. (2002). Scheduling hybrid flowshops in printed circuit board assembly lines. Production and operations management, 11(2), 216-230.
101
[102] Johnson, S. M. (1954) Optimal two‐and three‐stage production schedules with setup times included. Naval research logistics (NRL), 1(1), 61-68.
102
[103] Jolai, F., Sheikh, S., Rabbani, M., & Karimi, B. (2009). A genetic algorithm for solving no-wait flexible flow lines with due window and job rejection. The Int. J. of Ad. Man. Tech, 42(5), 523-532.
103
[104] Jolai, F., Rabiee, M., & Asefi, H. (2012). A novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times. Int. J. of Prod. Res. 50(24), 7447-7466.
104
[105] Juan, A. A., Lourenço, H. R., Mateo, M., Luo, R., & Castella, Q. (2014). Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues. International transactions in operational research, 21(1), 103-126.
105
[106] Jungwattanaki, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2005). An evaluation of sequencing heuristics for flexible flowshop scheduling problems with unrelated parallel machines and dual criteria. Otto-von-guericke-universitat magdeburg, 28(05), 1-23.
106
[107] Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2006). Sequencing algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. INTAS
107
[108] Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2008). Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. The international journal of advanced manufacturing technology, 37(3-4), 354-370.
108
[109] Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2009). A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Computers & operations research, 36(2), 358-378.
109
[110] Kangarloo, N., Rezaeian, J., & Khosrawi, X. (2016). JIT scheduling problem on flexible flow shop with machine break down, machine eligibility and setup times. J. of Math. & Com. Sc, 16(1), 50-68.
110
[111] Karimi, N., Zandieh, M., & Karamooz, H. (2010). Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach. Expert systems with applications, 37(6), 4024-4032.
111
[112] Wang, H. (2005). Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions. Expert systems, 22(2), 78-85.
112
[113] Karmakar, S., & Mahanty, B. (2010). Minimizing makespan for a flexible flow shop scheduling problem in a paint company. Preceding IEOM. Dhaka, Bangladesh.
113
[114] Kayvanfar, V., Komaki, G. M., Aalaei, A., & Zandieh, M. (2014). Minimizing total tardiness and earliness on unrelated parallel machines with controllable processing times. Comp. & Operations Res, 41, 31-43.
114
[115] Khalili, M., & Tavakkoli-Moghaddam, R. (2012). A multi-objective electromagnetism algorithm for a bi-objective flowshop scheduling problem. Journal of manufacturing systems, 31(2), 232-239.
115
[116] Khalili, M., & Naderi, B. (2015). A bi-objective imperialist competitive algorithm for no-wait flexible flow lines with sequence dependent setup times. The Int. J. of Ad. Man. Tech, 76(1-4), 461-469.
116
[117] Kia, H, Davoudpour, H, Zandieh, M. (2010). Scheduling a dynamic flexible flow line with sequence-dependent setup times: a simulation analysis. Int. J. of Production Res. 48(14): 4019-4042.
117
[118] Kia, H., Ghodsypour, SH., Davoudpour, H. (2017). New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times. J. of Industrial Eng. Int. 1-10.
118
[119] Kianfar, K., Ghomi, S. M. T., & Jadid, A. O. (2012). Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA. Eng. Appl. of Artificial Intel, 25(3), 494-506.
119
[120] Komaki, M., Sheikh, S., Teymourian, E., & Malakooti, B. (2015). Cuckoo search algorithm for hybrid flow shop scheduling problem with multi-layer assembly operations. International conference on operations excellence and service engineering. Orlando, Florida, USA.
120
[121] Komaki, M., & Malakooti, B. (2017). General variable neighborhood search algorithm to minimize makespan of the distributed no-wait flow shop scheduling problem. Prod. Eng. 11(3), 1-15.
121
[122] Kumar, A., & Dhingra, A. (2010). Minimization of total weighted tardiness and makespan for SDST flow shop scheduling using genetic algorithm. Int. J. of Applied Engineering Res, 3(2), 483-494.
122
[123] Kurz, M. E. (2001). Scheduling flexible flow lines with sequence dependent setup Times. A Dissertation (Doctoral thesis, University of Arizona, USA).
123
[124] Kurz, M. E., & Askin, R. G. (2003). Comparing scheduling rules for flexible flow lines. International journal of production economics, 85(3), 371-388.
124
[125] Kurz, M. E., & Askin, R. G. (2004). Scheduling flexible flow lines with sequence-dependent setup times. European journal of operational research, 159(1), 66-82.
125
[126] Lee, S. M., & Asllani, A. A. (2004). Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming. Omega, 32(2), 145-153.
126
[127] Lee, G. C., Hong, J. M., & Choi, S. H. (2015). Efficient heuristic algorithm for scheduling two-stage hybrid flowshop with sequence-dependent setup times. Mathematical Problems in Eng.
127
[128] Lee, J. Y., & Bang, J. Y. (2016). A two-stage assembly-type flowshop scheduling problem for minimizing total tardiness. Mathematical problems in engineering.
128
[129] Li, L, Wang, L, Huo, J.(2010). Hybrid flowshop scheduling with setup times for cold treating process in Baoshan Iron & Steel Complex. Int. Conference on Logistics Sys. and Intelligent Management.
129
[130] Li, X., & Li, M. (2015). Multiobjective local search algorithm-based decomposition for multiobjective permutation flow shop scheduling problem. IEEE Trans. on Eng. Manag, 62(4), 544-557.
130
[131] Li, X., Ma, S (2016) Multi-objective memetic search algorithm for multi-objective permutation flow shop scheduling problem. IEEE access, 4, 2154-2165.
131
[132] Li, X, & Ma, S. (2017). Multiobjective discrete artificial bee colony algorithm for multiobjective permutation flow shop scheduling problem with sequence dependent setup times. IEEE Trans. on Eng. Manag, 64(2), 149-165.
132
[133] Lin, H. T., & Liao, C. J. (2003). A case study in a two-stage hybrid flow shop with setup time and dedicated machines. International journal of production economics, 86(2), 133-143.
133
[134] Lin, S. W., & Ying, K. C. (2012). Scheduling a bi-criteria flowshop manufacturing cell with sequence-dependent family setup times. European journal of industrial engineering, 6(4), 474-496.
134
[135] Liou, C. D., & Hsieh, Y. C. (2015). A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times. Int. J. of Prod. Eco, 170, 258-267.
135
[136] Liu, C. Y., & Chang, S. C. (2000). Scheduling flexible flow shops with sequence-dependent setup effects. IEEE transactions on robotics and automation, 16(4), 408-419.
136
[137] Lu, D., & Logendran, R. (2013). Bi-criteria group scheduling with sequence-dependent setup time in a flow shop. Journal of the operational research society, 64(4), 530-546.
137
[138] Lu, C., Xiao, S., Li, X., & Gao, L. (2016). An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Advances in Eng. Soft, 99, 161-176.
138
[139] Lu, C., Gao, L., Li, X., Pan, Q.K., & Wang, Q. (2017a). Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm. J. of Cleaner Production, 144, 228-238.
139
[140] Lu, C., Gao, L., Li, X., & Xiao, S. (2017b). A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Eng. Appl.of Artificial Intelligence, 57, 61-79.
140
[141] Luo, H., Huang, G. Q., Zhang, Y., Dai, Q., & Chen, X. (2009). Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm. Robotics and computer-integrated manufacturing, 25(6), 962-971.
141
[142] Luo, H., Zhang, A., & Huang, G. Q. (2015). Active scheduling for hybrid flowshop with family setup time and inconsistent family formation. Journal of intelligent manufacturing, 26(1), 169-187.
142
[143] Lv, Y., Zhang, J., & Qin, W. (2017.) A genetic regulatory network-based method for dynamic hybrid flow shop scheduling with uncertain processing times. Applied sciences, 7(1), 23-42.
143
[144] Maboudian, Y., & Shafaei, R. (2009). Modeling a bi-criteria two stage assembly flow shop scheduling problem with sequence dependent setup times. IEEE international conference on industrial engineering and engineering management. Hong Kong, china.
144
[145] Majazi Dalfard, V., Ardakani, A., & Banihashemi, T. N. S. (2011). Hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times. The. Vjesnik, 18(4), 497-504.
145
[146] Maleki-Darounkolaei, A., Modiri, M., Tavakkoli-Moghaddam, R., & Seyyedi, I. (2012). A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times. Journal of industrial engineering international, 8(1), 26.
146
[147] Maleki-Daronkolaei, A., & Seyedi, I. (2013). Taguchi method for three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times. J. of Eng. Sc.and Tech, 8(5), 603-622.
147
[148] Mansouri, S. A., Hendizadeh, S. H., & Salmasi, N. (2009). Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times. The Int. J. of Ad. Man.Tech, 40(11), 1216-1226.
148
[149] Mastrolilli, M., & Svensson, O. (2011). Hardness of approximating flow and job shop scheduling problems. Journal of the ACM (JACM), 58(5),1-32.
149
[150] Mehravaran, Y., & Logendran, R. (2012). Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times. Int. Journal of Production Economics, 135(2), 953-963.
150
[151] Meshkinfam, S., Ghassemi, Tari F. (2016). A genetic algorithm for generalized tardiness flowshop problems. International journal of engineering science and technology, 8(9), 219-228.
151
[152] Michalewicz, Z. (1996). Heuristic methods for evolutionary computation techniques. Journal of heuristics, 1(2), 177-206.
152
[153] Miettinen, K., Mäkelä, M. M., & Kaario, K. (2006). Experiments with classification-based scalarizing functions in interactive multiobjective optimization. Eur. J. of Operational Res, 175(2), 931-947.
153
[154] Miettinen, K., Molina, J., González, M., Hernández-Díaz, A., & Caballero, R. (2009). Using box indices in supporting comparison in multiobjective optimization. Eur. J. of Operational Res, 197(1), 17-24.
154
[155] Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted iterated pareto greedy algorithm for multi-objective flowshop scheduling problems. Computers & operations research, 38(11), 1521-1533.
155
[156] Mirsanei, H., Zandieh, M., Moayed, M. J., & Khabbazi, M. R. (2011). A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times. J. of Int. Manuf, 22(6), 965-978.
156
[157] Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Com. & Op. Res, 24(11), 1097-1100.
157
[158] Hansen, P., Oğuz, C., & Mladenović, N. (2008). Variable neighborhood search for minimum cost berth allocation. European journal of operational research, 191(3), 636-649.
158
[159] Mohammadi, G. (2015). Multi-objective flow shop production scheduling via robust genetic algorithms optimization technique. Int. J. of Service Sci., Manag. & Eng, 2(1), 1-8.
159
[160] Mousavi, S., Zandieh, M., & Amiri, M. (2011). An efficient bi-objective heuristic for scheduling of hybrid flow shops. The Int. Journal of Advanced Manufacturing Technology, 54(1), 287-307.
160
[161] Mousavi, S., Zandieh, M., & Amiri, M. (2012). Comparisons of bi-objective genetic algorithms for hybrid flowshop scheduling with sequence-dependent setup times. Int. J. of Prod. Res, 50(10), 2570-2591.
161
[162] Mousavi, S., & Zandieh, M. (2016). An Efficient Hybrid Algorithm for a Bi-objectives Hybrid Flow Shop Scheduling. Intelligent automation & soft computing, 1-8.
162
[163] Mousavi, S., Mahdavi, I., Rezaeian, J., & Zandieh, M. (2016). An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times. Operational research, 1-36.
163
[164] Mousavi, S.M., Mahdavi, I., Rezaeian J., & Zandieh, M. (2018). Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times. Scientia Iranica E, 25(4), 2233{2253.
164
[165] Mozdgir, A., Fatemi Ghomi, S. M. T., Jolai, F., & Navaei, J. (2013). Two-stage assembly flow-shop scheduling problem with non-identical assembly machines considering setup times. Int. J. of Prod. Res, 51(12), 3625-3642.
165
[166] Naderi, B., Khalili, M., Taghavifard, M., & Roshanaei, V. (2008). A variable neighborhood search for hybrid flexible flowshops with setup times minimizing total completion time. J. of Appl. Sci, 8(16), 2843-2850.
166
[167] Naderi, B., Zandieh, M., & Fatemi Ghomi, S. M. T. (2009d). A study on integrating sequence dependent setup time flexible flow lines and preventive maintenance scheduling. J. of Intelligent Manuf, 20(6), 683-694.
167
[168] Naderi, B., Zandieh, M., Fatemi Ghomi, S. M. T. (2009c). An iterated greedy algorithm for flexible flow lines with sequence dependent setup times to minimize total weighted completion time. Journal of optimization in industrial engineering, 2(3), 33-37.
168
[169] Naderi, B., Zandieh, M., Balagh, A. K. G., & Roshanaei, V. (2009b). An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert systems with applications, 36(6), 9625-9633.
169
[170] Naderi, B., Zandieh, M., & Roshanaei, V. (2009a). Scheduling hybrid flowshops with sequence dependent setup times to minimize makespan and maximum tardiness. The Int. J. of Ad. Manf.Tech, 41(11-12), 1186-1198.
170
[171] Naderi, B., Ruiz, R., & Zandieh, M. (2010). Algorithms for a realistic variant of flowshop scheduling. Computers & operations research, 37(2), 236-246.
171
[172] Naderi, B., Gohari, S., & Yazdani, M. (2014). Hybrid flexible flowshop problems: Models and solution methods. Applied mathematical modelling, 38(24), 5767-5780.
172
[173] Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95.
173
[174] Nayak A., Fang K., & Lee S. (2017). Demand response in flow shop with job due dates using genetic algorithm approach. Smart and Sustainable Manufacturing Systems 1(1), 100-120.
174
[175] Nejati, M., Mahdavi, I., Hassanzadeh, R., & Mahdavi-Amiri, N. (2016). Lot streaming in a two-stage assembly hybrid flow shop scheduling problem with a work shift constraint. J. of Ind. & Prod. Eng, 33(7), 459-471.
175
[176] Pan, Q. K., Gao, L., Li X. Y., & Gao, K. Z. (2017). Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times. Appl. Mathematics and Comp, 303, 89-112.
176
[177] Panahi, H. (2014). Two-stage flexible flow shop scheduling with blocking constraint and batching machines (Doctoral thesis, Oregon State University, Oregon, USA).
177
[178] Palmer, D. (1965). Sequencing jobs through a multi-stage process in the minimum total time-a quick method of obtaining a near optimum. J. of the Operational Res. Society, 16(1), 101-107.
178
[179] Patnaik, S., Yang, X. S., & Nakamatsu, K. (2017). Nature-inspired computing and optimization: theory and applications, Vol. 10. Springer International Publishing.
179
[180] Pargar, F., & Zandieh, M. (2012). Bi-criteria SDST hybrid flow shop scheduling with learning effect of setup times: water flow-like algorithm approach. Int. J. of Prod. Res, 50(10), 2609-2623.
180
[181] Parveen, S., & Ullah, H. (2011). Review on job-shop and flow-shop scheduling using multi criteria decision making. Journal of mechanical engineering, 41(2), 130-146.
181
[182] Pearn, W., Chung, S., Yang, M., & Chen, C. (2005). The integrated circuit packaging scheduling problem (icpsp): A case study. Int. J. of Industrial Eng.-Theory Appl. and Practice 12(3), 296-307.
182
[183] Perez-Gonzalez, P., & Framinan, F. M. (2015). Assessing scheduling policies in a permutation flowshop with common due dates. International journal of production research, 53(19), 5742-5754.
183
[184] Ponnambalam, S., Jagannathan, H., Kataria, M., & Gadicherla, A. (2004) A TSP-GA multi-objective algorithm for flow-shop scheduling. The Int J. of Advanced Manuf. Tech, 23(11-12), 909-915.
184
[185] Qian, B., Wang, L., Huang, D. X., Wang, W. I., & Wang, X. (2009) An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Comp. & Op. Res, 36(1), 209-233.
185
[186] Rabiee, M., Rad, R. S., Mazinani, M., & Shafaei, R. (2014). An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines. The Int. J. of Ad. Man. Tech, 71(5-8), 1229-1245.
186
[187] Rahimi-Vahed, A., & Mirzaei, A. H. (2007). A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Comp. & Industrial Eng, 53(4), 642-666.
187
[188] Rahmanidoust, M., Zheng, J., & Rabiee, M. (2017). Simultaneous considering of machine availability constraint, sequence dependent setup time and ready time in a no-wait hybrid flow shop scheduling problem to minimize mean tardiness. International journal of computer applications, 169(7), 30-37.
188
[189] Rajaee Abyaneh, F., & Gholami, S. (2015). A comparison of algorithms for minimizing the sum of earliness and tardiness in hybrid flow-shop scheduling problem with unrelated parallel machines and sequence-dependent setup times. Industrial and systems engineering, 8(2), 67-85.
189
[190] Ramezani, P., Rabiee, M., & Jolai, F. (2015). No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach. J. of Int. Man, 26(4), 731-744.
190
[191] Ramezanian, R., Aryanezhad, M., & Heydari M. (2010). A mathematical programming model for flow shop scheduling problems for considering just in time production. Int. J. of Industrial Eng, 21(2), 97-104.
191
[192] Ramezanian, R., Fallah Sanami, S., & Mahmoodian, V. (2017). A new mathematical model for integrated production planning and scheduling problem in capacitated flexible flow shop with sequence-dependent setups. Scientia iranica, 24(5), 2501-2514.
192
[193] Rashidi, E., Jahandar, M., & Zandieh, M. (2010). An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. The Int. J. of Ad. Manuf. Tech, 49(9), 1129-1139.
193
[194] Rezaeian, J., Seidgar, H., & Kiani, M. (2013). Scheduling of a flexible flow shop with multiprocessor task by a hybrid approach based on genetic and imperialist competitive algorithms. J. of Opt. in Ind. Eng, 6(13), 1-11.
194
[195] Ribas, I., Companys, R., & Tort-Martorell, X. (2017). Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert systems with applications, 74, 41-54.
195
[196] Ruiz, R., & Maroto, C. (2006). A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. European journal of operational research, 169(3), 781-800.
196
[197] Ruiz, R., Şerifoğlu, F. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers & operations research, 35(4), 1151-1175.
197
[198] Saluja, V., & Jain, A. (2014). Optimization of flexible flow shop scheduling with sequence dependent setup time and lot splitting. 5th international & 26th all india manufacturing technology, design and research conference. IIT Guwahati, Assam, India
198
[199] Samarghandi, H., & Behroozi, M. (2017). On the exact solution of the no-wait flow shop problem with due date constraints. Computers & operations research, 81, 141-159.
199
[200] Saravanan, M., Vijayakumar, S. J. D., & Srinivasan, R. (2014). A multicriteria permutation flowshop scheduling problem with setup times. Int. J. of Eng. and Tech, 6(3), 1329-1339.
200
[201] Satyanarayana, D., & Pramiladevi, M. (2016). Multi-criteria m-machine SDST flow shop scheduling using modified heuristic genetic algorithm. Int. J. of Industrial and Sys. Eng, 22(4), 409-422.
201
[202] Satyanarayana, D., & Pramiladevi, M. (2017). Special heuristics for flowshop scheduling based on hybrid genetic algorithm under SDST environment. Int. J. of Mech. Eng. & Tech, 8(4), 327-336.
202
[203] Seidgar, H., Ezzati, M., Kiani, M., & Tavakkoli-Moghaddam, R. (2013). An efficient genetic algorithm for two-stage hybrid flow shop scheduling with preemption and sequence dependent setup time. Journal of mathematics and computer science, 6, 251 – 259.
203
[204] Seidgar, H., Abedi, M., & Rad, S. T. (2015). A new mathematical model for scheduling flexible flow shop problem with learning and forgetting effects of workers. Int. J.of Industrial and Sys. Eng, 21(4), 534-549.
204
[205] Seyedi, I., Maleki-Daronkolaei, A., & Kalashi, F. (2012). Tabu search and simulated annealing for new three-stage assembly flow shop scheduling with blocking. Int. J. of Cont. Res. In Business, 4(8), 394-402.
205
[206] Shabtay, D., & Oron, D. (2016). Proportionate flow-shop scheduling with rejection. Journal of the operational research society, 67(5), 752-769.
206
[207] Shahvari, O., Salmasi, N., & Logendran, R. (2009). A meta-heuristic algorithm for flexible flow shop sequence dependent group scheduling problem. Proceedings of the 2009 Int. Conf. on Value Chain Sustainability. Kentucky, USA.
207
[208] Sharma, S., Gupta, D., & Nailwal, K. K. (2017a). Bi-criteria multistage flow shop scheduling with sequence-dependent setup times. International journal of operational research, 29(1), 127-147.
208
[209] Sharma, S., Gupta, D., & Nailwal, K. K. (2017b). Sequence dependent flow shop scheduling with job block criteria. Global journal of pure and applied mathematics, 13(5), 1401-1414.
209
[210] Sheikh, S., Komaki, M., Teymourian, E., & Malakooti, B. (2015). Multi-objective non-permutation flowshop with dependent setup times and missing operations. 7th Int. Joint Conf. on Comp. Intel. (IJCCI).
210
[211] Sioud, A., Gagné, C., & Gravel, M. (2014a). Metaheuristics for solving a hybrid flexible flowshop problem with sequence-dependent setup times. 1st international conference on swarm intelligence based optimization, first international conference, ICSIBO. Mulhouse, France.
211
[212] Sioud, A., Gagné, C., & Gravel, M. (2014). Minimizing total tardiness in a hybrid flexible flowshop with sequence dependent setup times. The fourth international conference on advanced communications and computation. Paris, France.
212
[213] Sioud, A., Gagné, C., & Dort, J. (2015). A GISMOO algorithm for a multi-objective permutation flowshop with sequence-dependent setup times. 7th international joint conference on computational intelligence. Lisbon, Portugal.
213
[214] Sivapragasam, S., & Suppiah, Y. (2017). Minimizing Total Weighted Tardiness in Identical Parallel Machine with Sequence Dependent Setup Time Using Genetic Algorithm. Journal of telecommunication, electronic and computer engineering (JTEC), 9(1-4), 89-93.
214
[215] Song, J., Tang, J., Luo, X., & Liu, S. (2008). Scheduling model and heuristic algorithm for roller annealing. Chinese controland decision conference, CCDC (pp. 1052-1055).
215
[216] Sukkerd, W., & Wuttipornpun, T. (2017). Non-population search algorithms for capacitated material Requirement Planning in multi-stage assembly flow shop with alternative machines. World academy of science, engineering and technology, Int. J. of Mech., Aer., Ind., Mech. and Man. Eng, 11(3), 636-643.
216
[217] Tabrizi, A. A., Zandieh, M., & Vaziri, Z. (2009). A novel simulated annealing algorithm to hybrid flow shops scheduling with sequence-dependent setup times. J. of Applied Sciences, 9(10), 1943-1949.
217
[218] Tang, J., & Song, J. (2010). Discrete particle swarm optimization combined with no-wait algorithm in stages for scheduling mill roller annealing process. International journal of computer integrated manufacturing, 23 (11), 979-991.
218
[219] Tao, W., & Rongqiu, C. (2006). Heuristic algorithm of scheduling model for cold coils annealing. Proceedings of the world congress on intelligent control and automation (WCICA) (pp. 7263-7266).
219
[220] Tavakkoli-Moghaddam R., & Safaei, N. (2007). A new mathematical model for flexible flow lines with blocking processor and sequence-dependent setup time. Multiprocessor scheduling, theory and applications: I-Tech education and publishing. Vienna, Austria 2007, 266–283.
220
[221] Tavakkoli-Moghaddam, R., Rahimi-Vahed, A., & Mirzaei, A.H. (2007). A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: weighted mean completion time and weighted mean tardiness. Information sciences, 177(22), 5072-5090.
221
[222] Tavakkoli-Moghaddam, R., Azarkish, M., & Sadeghnejad-Barkousaraie, A. (2011). A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Sys. with Appl, 38(9), 10812-10821.
222
[223] Tavakkoli-Moghaddam, R., & Amin-Tahmasbi, H. (2012). A multi-objective immune system for a new bi-objective permutation flowshop problem with sequence-dependent setup times. Iranian journal of operations research, 3(2), 66-82.
223
[224] Taillard, E. (1993) Benchmarks for basic scheduling problems. European journal of operational research, 64(2), 278-285.
224
[225] Tian, H., Li, K., & Liu, W. (2016). A Pareto-based adaptive variable neighborhood search for biobjective hybrid flow shop scheduling problem with sequence-dependent setup time. Math. Pro. in Eng.
225
[226] Tyaghi, N., Seidgar, H., Abedi, M., & Chandramouli, A. B. (2014). Learning and forgetting effects of flexible flow shop scheduling. International journal of innovation and applied studies, 7(3), 857-867.
226
[227] Urlings, T., Ruiz, R., & Serifoglu, F. S. (2010). Genetic algorithms with different representation schemes for complex hybrid flexible flow line problems. International journal of metaheuristics, 1(1), 30-54.
227
[228] Vanchipura, R., Sridharan, R., & Babu, A. S. (2014). Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time. Journal of manufacturing systems, 33(1), 65-75.
228
[229] Wang, H. S., Wang, L. C., Chen, T. L., Chen, Y. Y., & Cheng, C. Y. (2013a). Multi-stage parallel machines and lot-streaming scheduling problems–a case study for solar cell industry. In Prabhu, V., Taisch, M., & Kiritsis D (Eds). Advances in production management systems (pp. 151-158). Springer, Berlin, Heidelberg.
229
[230] Wang, I. L., Wang, Y. C., & Chen, C. W. (2013b). Scheduling unrelated parallel machines in semiconductor manufacturing by problem reduction and local search heuristics. Flex. Ser. & Man. J, 25(3), 343-366.
230
[231] Xiao, J., & Zheng, L. (2010). A MILP-based batch scheduling for two-stage hybrid flowshop with sequence-dependent setups in semiconductor assembly and test manufacturing. IEEE conference on automation science and engineering (pp. 21-24). Toronto, ON, Canada.
231
[232] Xu, J. Y., Dong, N. Q., & Gu, S. S. (2013). Multi-objective permutation flowshop scheduling with sequence-dependent setup times. Computer integrated manufacturing systems, 19(12).
232
[233] Xu, Z. H., Li, J. M., & Gu, X. S. (2016). Multi-objective flow shop scheduling problem based on GMOGSO. Control and decision, 31(10), 1772-1778.
233
[234] Xu, J., Wu, C. C., Yin, Y., & Lin, W. C. (2017). An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times. Appl. Soft Comp, 52, 39-47.
234
[235] Yaurima, V., Burtseva, L., & Tchernykh, A. (2009). Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers. Comp. & Ind. Eng, 56(4), 1452-1463.
235
[236] Yazdani, M., & Naderi, B. (2017). Modeling and scheduling no-idle hybrid flow shop problems. J. of Opt. in Industrial Eng, 10(21), 59-66.
236
[237] Yokoyama, M. (2001). Hybrid flow-shop scheduling with assembly operations. International journal of production economics, 73(2), 103-116.
237
[238] Zandieh, M., Fatemi Ghomi, S. M. T., & Husseini, S. M. (2006). An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Appl. Math. and Comp, 180(1), 111-127.
238
[239] Zandieh, M., & Rashidi, E. (2009). An effective hybrid genetic algorithm for hybrid flow shops with sequence dependent setup times and processor blocking. J. of Industrial Eng, 4(8), 51-58.
239
[240] Zandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int. J. of Prod. Res, 47(24), 6999-7027.
240
[241] Zandieh, M., Dorri, B., & Khamseh, A. (2009). Robust metaheuristics for group scheduling with sequence-dependent setup times in hybrid flexible flow shops. The Int. J.of Adv. Manuf. Tech, 43(7), 767-778.
241
[242] Zandieh, M., Mozaffari, E., & Gholami, M. (2010). A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems. Journal of intelligent manufacturing, 21(6), 731-743.
242
[243] Zandieh, M., & Karimi, N. (2011). An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times. Journal of intelligent manufacturing, 22(6), 979-989.
243
[244] Zandieh, M., & Hashemi, A. (2015). Group scheduling in hybrid flexible flowshop with sequence-dependent setup times and random breakdowns via integrating genetic algorithm and simulation. International journal of industrial and systems engineering, 21(3), 377-394.
244
ORIGINAL_ARTICLE
Facility location selection for plastic manufacturing industry in Bangladesh by using AHP method
In present’s, the location selection problems play an important role for the top-level manager or entrepreneur for opening a new manufacturing company or relocate or expand their operation. As an engineered material, the plastic is used for manufacturing a wide variety of domestic products. For this reason, the plastic manufacturing industries are growing in Bangladesh through the last two eras. This paper might be helpful to select a new location or expansion of the existing one in Bangladesh for the plastic manufacturing company. In this study, we have taken five commercial districts as location and ten criteria for deep consideration from all promising sites of Bangladesh.For this purpose, data has collected through surveying and questionnaires. Then, the Analytic Hierarchy Process (AHP) has used to make a preference measure to select the best location for plastic manufacturing industries. From the comparison value of the composite weight, it can be found that Mongla is the best alternative location for the decision problems.
https://www.riejournal.com/article_69748_dbbabbf7f262a9260c2b364ed28e77ea.pdf
2018-11-01
307
319
10.22105/riej.2018.135742.1049
Facility location
AHP
Multi-criteria decision making
M. S.
Rahman
sumon.just16@gmail.com
1
Department of Industrial and Production Engineering, Faculty of Engineering and Technology, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
LEAD_AUTHOR
M. I.
Ali
imraan.just@gmail.com
2
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
U.
Hossain
uzzalhossainipe@gmail.com
3
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
T. K.
Mondal
tm386080@gmail.com
4
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
[1] Chu, T. C. (2002). Facility location selection using fuzzy TOPSIS under group decisions. International journal of uncertainty, fuzziness and knowledge-based systems, 10(06), 687-701.
1
[2] Koç, E., & Burhan, H. A. (2014). An analytic hierarchy process (AHP) approach to a real world supplier selection problem: a case study of carglass turkey. Global Business & Management Research, 6(1).
2
[3] MacCarthy, B. L., & Atthirawong, W. (2003). Factors affecting location decisions in international operations–a Delphi study. International journal of operations & production management, 23(7), 794-818.
3
[4] Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179.
4
[5] Mandal, S., & Mondal, S. S. (2016). Analytic hierarchy process (AHP) approach for selection of open cast coal mine project. International journal of industrial engineering, 7(2).
5
[6] Baumol, W. J., & Wolfe, P. (1958). A warehouse-location problem. Operations research, 6(2), 252-263.
6
[7] Rosenthal, R. E., White, J. A., & Young, D. (1978). Stochastic dynamic location analysis. Management science, 24(6), 645-653.
7
[8] Ebert, R. J., & Adam, E. E. (1992). Production and operations management: concepts, models, and behavior. Prentice Hall.
8
[9] Rao, R. V. (2007). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. Springer Science & Business Media.
9
[10] Kaboli, A., Aryanezhad, M. B., Shahanaghi, K., & Niroomand, I. (2007, October). A new method for plant location selection problem: A fuzzy-AHP approach. IEEE international conference on systems, man and cybernetics (ISIC) (pp. 582-586). IEEE.
10
[11] Chatterjee, D., & Mukherjee, B. (2013). Potential hospital location selection using AHP: A study in rural India. International journal of computer applications, 71(17).
11
[12] Fortenberry, J. C., & Mitra, A. (1986). A multiple criteria approach to the location-allocation problem. Computers & industrial engineering, 10(1), 77-87.
12
[13] Kahne, S. (1975). A procedure for optimizing development decisions. Automatica, 11(3), 261-269.
13
[14] Kirkwood, C. W. (1982). A case history of nuclear power plant site selection. Journal of the operational research society, 33(4), 353-363.
14
[15] Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. The international journal of advanced manufacturing technology, 28(7-8), 839-844.
15
[16] Ko, J. (1980). Solving a distribution facility location problem using an analytic hierarchy process approach. Situations (Zahedi, 1996).
16
[17] Hundal, G. P. S., & Kant, S. (2017). Product design development by integrating QFD approach with heuristics-AHP, ANN and fuzzy logics-a case study in miniature circuit breaker. International journal of productivity and quality management, 20(1), 1-28.
17
[18] Saaty, T. L., & Decision, H. T. M. A. (1990). The analytic hierarchy process. European journal of operational research, 48, 9-26.
18
ORIGINAL_ARTICLE
Investment projects ranking with DEA method considering feasibility study results
According to the increasing need of industry to financiers and investors in order to incorporate and encouraging them to invest in various fields of industry, the necessity of a method to help investors for making a decision has emphasized. We present a method which tries to omit to apply personally partial views in making a decision in order to make the results more reliable. This paper focuses on ranking investing opportunities. The strategy which has used is the Data Envelopment Analysis that the 4 main sub-models including Charnes, Cooper & Rhodes (CCR) model, Input Oriented Banker, Charnes & Cooper (BCC) model, Output Oriented BCC model, and Additive model have been utilized. The contribution of this research is using 4 DEA models for ranking projects in terms of feasibility whereas in the similar researches, as what found in the literature, the mentioned models have not been taken into account simultaneously. The developed model is applied in an Iranian investment company which has 15 investment opportunities that we have evaluated and ranked them based on 5 financial indices with DEA mechanism. Our approach can be performed by any investment company or financier to rank their investment projects considering feasibility study results of each investment opportunity.
https://www.riejournal.com/article_79916_78e934e860d17d6ad64cd41540b69617.pdf
2018-11-01
320
335
10.22105/riej.2018.147016.1058
Data Envelopment Analysis
Investment Opportunity
Feasibility study
Ranking
M. V.
Sebt
vhd_sebt@yahoo.com
1
Department of Industrial Engineering, Faculty Technical-Engineering, Kharazmi University, Tehran, Iran.
LEAD_AUTHOR
M.
Juybari
mnajafijuy@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran.
AUTHOR
V. R.
Soleymanfar
st_v_soleymanfar@azad.ac.ir
3
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
AUTHOR
[1] Aggelopoulos, E., & Georgopoulos, A. (2017). Bank branch efficiency under environmental change: A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches. European journal of operational research, 261(3), 1170–1188.
1
[2] Amiri, M., Zandieh, M., Vahdani, B., Soltani, R., & Roshanaei, V. (2010). An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market. Expert systems with applications, 37(1), 509–516.
2
[3] Arazmuradov, A. (2016). Assessing sovereign debt default by efficiency. Journal of economic asymmetries, 13, 100–113.
3
[4] Azadeh, A., & Kokabi, R. (2016). Z-number DEA: A new possibilistic DEA in the context of Z-numbers. Advanced engineering informatics, 30(3), 604–617.
4
[5] Banker, R., Chen, J. Y. S., & Klumpes, P. (2016). A trade-level DEA model to evaluate relative performance of investment fund managers. European journal of operational research, 255(3), 903–910.
5
[6] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
6
[7] Carrillo, M., & Jorge, J. M. (2016). A multiobjective DEA approach to ranking alternatives. Expert systems with applications, 50, 130–139.
7
[8] Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of econometrics, 30(1), 91–107.
8
[9] Charnes, A., Cooper, W. W., & Rhodes, E. (1979). Measuring the efficiency of decision-making units. European journal of operational research, 3(4), 339.
9
[10] Chen, Y. C., Chiu, Y. H., Huang, C. W., & Tu, C. H. (2013). The analysis of bank business performance and market risk—applying fuzzy DEA. Economic modelling, 32, 225-232.
10
[11] Choi, H. S., & Min, D. (2017). Efficiency of well-diversified portfolios: Evidence from data envelopment analysis. Omega, 73, 104-113.
11
[12] Cui, Q., Wei, Y. M., & Li, Y. (2016). Exploring the impacts of the EU ETS emission limits on airline performance via the dynamic environmental DEA approach. Applied energy, 183, 984–994.
12
[13] Edirisinghe, N. C. P., & Zhang, X. (2007). Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of banking & finance, 31(11), 3311–3335.
13
[14] Eilat, H., Golany, B., & Shtub, A. (2006). Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology. European journal of operational research, 172(3), 1018–1039.
14
[15] Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the royal statistical society. Series A (General), 120(3), 253-290.
15
[16] Galagedera, D. U., Watson, J., Premachandra, I. M., & Chen, Y. (2016). Modeling leakage in two-stage DEA models: An application to US mutual fund families. Omega, 61, 62-77.
16
[17] Gruber, M. J. (2011). Another puzzle: The growth in actively managed mutual funds. Investments and portfolio performance (pp. 117-144).
17
[18] Hadad, Y., Keren, B., & Laslo, Z. (2016). Multi-criteria methods for ranking project activities. Yugoslav journal of operations research, 26(2), 201–219.
18
[19] Hall, M. J. B., Kenjegalieva, K. A., & Simper, R. (2012). Environmental factors affecting Hong Kong banking: A post-Asian financial crisis efficiency analysis. Global finance journal, 23(3), 184–201.
19
[20] Hu, Z., Yang, J., Wang, S., & Yang, Q. (2016). A hybrid modified DEA efficient evaluation method in electric power enterprises. 3rd international conference on informative and cybernetics for computational social systems (ICCSS)(pp. 283–287). Department of Enterprise Management, Guangdong Power Grid Corporation, Guangzhou, China: Institute of Electrical and Electronics Engineers Inc.
20
[21] Jalalvand, F., Teimoury, E., Makui, A., Aryanezhad, M. B., & Jolai, F. (2011). A method to compare supply chains of an industry. Supply chain management, 16(2), 82–97.
21
[22] Kádárová, J., Durkáčová, M., Teplická, K., & Kádár, G. (2015). The proposal of an innovative integrated BSC – DEA model. Procedia economics and finance, 23, 1503–1508.
22
[23] Karasakal, E., & Aker, P. (2017). A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem. Omega, 73, 79-92.
23
[24] Köksalan, M., & Bilgin Özpeynirci, S. (2009). An interactive sorting method for additive utility functions. Computers & operations research, 36(9), 2565–2572.
24
[25] Lim, S., Oh, K. W., & Zhu, J. (2014). Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European journal of operational research, 236(1), 361–368.
25
[26] Liu, X., Chu, J., Yin, P., & Sun, J. (2017). DEA cross-efficiency evaluation considering undesirable output and ranking priority: A case study of eco-efficiency analysis of coal-fired power plants. Journal of cleaner production, 142, Part, 877–885.
26
[27] Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
27
[28] Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied soft computing, 38, 1–9.
28
[29] McMullen, P. R., & Strong, R. A. (1998). Selection of mutual funds using data envelopment analysis. The journal of business and economic studies, 4(1), 1.
29
[30] Mostafaeipour, A., Qolipour, M., & Mohammadi, K. (2016). Evaluation of installing photovoltaic plants using a hybrid approach for Khuzestan province, Iran. Renewable and sustainable energy reviews, 60, 60–74.
30
[31] Murthi, B. P. S., Choi, Y. K., & Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European journal of operational research, 98(2), 408-418.
31
[32] Ohsato, S., & Takahashi, M. (2015). Management efficiency in japanese regional banks: A Network DEA. Procedia-social and behavioral sciences, 172, 511–518.
32
[33] Ramazankhani, M. E., Mostafaeipour, A., Hosseininasab, H., & Fakhrzad, M. B. (2016). Feasibility of geothermal power assisted hydrogen production in Iran. International journal of hydrogen energy, 41(41), 18351-18369.
33
[34] Rȩbiasz, B., Gawel, B., & Skalna, I. (2017). Hybrid framework for investment project portfolio selection. Studies in computational intelligence.
34
[35] Rhodes, E. (1978). DEA and related approaches for measuring the efficiency of decision making units with an application to program follow through in US education (Doctoral dissertation, Ph. D. Thesis, Carnegie Mellon University).
35
[36] Sadeghi, A., & Mohammadzadeh Moghaddam, A. (2016). Uncertainty-based prioritization of road safety projects: An application of data envelopment analysis. Transport policy, 52, 28–36.
36
[37] Siok, M. F., & Tian, J. (2011). Benchmarking embedded software development project performance. 13th IEEE international symposium on high assurance systems engineering (HASE) (pp. 277–284). Lockheed Martin Aeronautics Company, Fort Worth, TX, United States.
37
[38] Stoica, O., Mehdian, S., & Sargu, A. (2015). The impact of internet banking on the performance of romanian banks: DEA and PCA approach. Procedia economics and finance, 20, 610–622.
38
[39] Tsolas, I. E., & Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert systems with applications, 42(7), 3491–3500.
39
[40] Walczak, D., & Rutkowska, A. (2017). Project rankings for participatory budget based on the fuzzy TOPSIS method. European journal of operational research, 260(2), 706–714.
40
[41] Wei, L., & Zhijiang, W. (2016). Cost - benefit analysis of green building based on input - output theory. Revista de la facultad de ingenieria, 31(10), 213–221.
41
[42] Wu, D. (2017). Robust decision support system for asset assessment and management. IEEE systems journal, 11(3), 1486-1491.
42
[43] Wu, J., Chu, J., Sun, J., & Zhu, Q. (2016). DEA cross-efficiency evaluation based on Pareto improvement. European journal of operational research, 248(2), 571–579.
43
[44] Wu, J., Chu, J., Sun, J., Zhu, Q., & Liang, L. (2016). Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Computers & industrial engineering, 93, 143–151.
44
[45] Yang, J. B., Wang, H. H., Wang, W. C., & Ma, S. M. (2016). Using data envelopment analysis to support best-value contractor selection. Journal of civil engineering and management, 22(2), 199-209.
45
[46] Yin, H., Yang, J., & Mehran, J. (2013). An empirical study of bank efficiency in China after WTO accession. Global finance journal, 24(2), 153–170.
46
[47] Zhang, Y. (2015). Research on decision-making method of bid evaluation for engineering projects based on fuzzy DEA and grey relation. Open cybernetics and systemics journal, 9(1), 711–718.
47
[48] Zhang, Y., Yang, C., Yang, A., Xiong, C., Zhou, X., & Zhang, Z. (2015). Feature selection for classification with class-separability strategy and data envelopment analysis. Neurocomputing, 166, 172–184.
48
[49] Zhu, Q., Wu, J., Li, X., & Xiong, B. (2017). China’s regional natural resource allocation and utilization: A DEA-based approach in a big data environment. Journal of cleaner production, 142, part, 809–818.
49
ORIGINAL_ARTICLE
Assessment of fuzzy failure mode and effect analysis (FMEA) for reach stacker crane (RST): A case study
FMEA (Failure Mode and Effect Analysis) refers to a proactive quality tool that enables the identification and prevention of the potential failure modes of a product or process. However, in executing traditional FMEA, the difficulties such as vague information, relative importance ratings, decisions on same ratings, and opinion difference among experts arise which reduce the validity of the results. This paper presents a fuzzy logic based FMEA depending on fuzzy IF-THEN rules over traditional FMEA to make it precise and give proper maintenance decision. Here, the Risk Priority Number (RPN) is calculated and compared to the Fuzzy Risk Priority Number (FRPN) to give maintenance decision. Furthermore, the FMEA of Reach Stacker Crane (RST) is presented to demonstrate the proposed Fuzzy FMEA.
https://www.riejournal.com/article_69749_6d2b88f522a5f7404ff4aac3d498f9b8.pdf
2018-11-01
336
348
10.22105/riej.2018.140970.1050
Failure mode and effect analysis (FMEA)
Risk priority number
Fuzzy theory
Fuzzy FMEA
IF-THEN rules
Md.
Fazle Rabbi
rfazle08@gmail.com
1
Industrial Engineering and Management, Khulna University of Engineering and Technology, Khulna, Bangladesh.
LEAD_AUTHOR
[1] Yeh, R. H., & Hsieh, M. H. (2007). Fuzzy assessment of FMEA for a sewage plant. Journal of the Chinese institute of industrial engineers, 24(6), 505-512.
1
[2] Xu, K., Tang, L. C., Xie, M., Ho, S. L., & Zhu, M. L. (2002). Fuzzy assessment of FMEA for engine systems. Reliability engineering & system safety, 75(1), 17-29.
2
[3] Guimarães, A. C. F., & Lapa, C. M. F. (2004). Fuzzy FMEA applied to PWR chemical and volume control system. Progress in Nuclear Energy, 44(3), 191-213.
3
[4] Xu, K., Tang, L. C., Xie, M., Ho, S. L., & Zhu, M. L. (2002). Fuzzy assessment of FMEA for engine systems. Reliability engineering & system safety, 75(1), 17-29.
4
[5] Bell, D., Cox, L., Jackson, S., & Schaefer, P. (1992, January). Using causal reasoning for automated failure modes and effects analysis (FMEA). Proceedings of annual reliability and maintainability symposium (pp. 343-353). Las Vegas, NV, USA, USA: IEEE.
5
[6] Wang, J., Ruxton, T., & Labrie, C. R. (1995). Design for safety of engineering systems with multiple failure state variables. Reliability engineering & system safety, 50(3), 271-284.
6
[7] Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability engineering & system safety, 50(2), 203-213.
7
[8] Quin, S., & Widera, G. E. O. (1996). Uncertainty analysis in quantitative risk assessment. Journal of pressure vessel technology, 118(1), 121-124.
8
[9] El-Shal, S. M., & Morris, A. S. (2000). A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems. Journal of intelligent & fuzzy systems, 9(3, 4), 207-223.
9
[10] He, D., & Adamyan, A. (2001, December). An impact analysis methodology for design of products and processes for reliability and quality. Proceedings of the ASME design engineering technical conference (pp. 209-217). Pittsburgh, PA, United States.
10
[11] Capunzo, M. A. R. I. O., Cavallo, P. I. E. R. P. A. O. L. O., Boccia, G. I. O. V. A. N. N. I., Brunetti, L. U. I. G. I., & Pizzuti, S. A. N. T. E. (2004). A FMEA clinical laboratory case study: how to make problems and improvements measurable. Clinical leadership and management review, 18(1), 37-41.
11
[12] Lee, B. H. (2001). Using Bayes belief networks in industrial FMEA modeling and analysis. Proceedings of annual reliability and maintainability symposium. International symposium on product quality and integrity (pp. 7-15). Philadelphia, PA, USA, USA: IEEE.
12
[13] Dittmann, L., Rademacher, T., & Zelewski, S. (2004, August). Performing FMEA using ontologies. 18th international workshop on qualitative reasoning (pp. 209-216). Northwestern University, Evanston, Illinois, USA.
13
[14] Kandel, A. (1986). Fuzzy mathematical techniques with applications. Addison-Wesley.
14
[15] Quin, S., & Widera, G. E. O. (1996). Uncertainty analysis in quantitative risk assessment. Journal of pressure vessel technology, 118(1), 121-124.
15
ORIGINAL_ARTICLE
Relationship between passengers’ satisfaction and service quality in murtala muhammed international airport, Lagos, Nigeria
The study examines the relationship between passengers’ satisfaction and service quality in MMIA. The sample size for the study is a total of three hundred and eighty-four (384), meanwhile, 58.3 percent of response rate was valid for data analysis. 49.1 percent valid questionnaire responses were obtained from the international terminal while 50.9 percent valid questionnaire responses were obtained from the domestic terminal. From the survey, the majority of the respondent was male representing 62.5 percent. From correlation analysis, about 71.1 percent of all service dimensions give a positive and very strong correlation, while about 18.4 percent of all service dimensions give a positive and strong correlation, also about 7.9 percent of all service dimensions give a positive and weak correlation, and about 2.6 percent of all service dimensions give a positive and very weak correlation. Efficiency of available public transport options is the only service with a very weak correlation. The study also revealed that there is a relationship between passengers’ satisfaction and service quality at P.value less than 0.05. This signifies that service quality leads to passengers’ satisfaction. It is therefore suggested that airport services should be quality so as to have a corresponding effect on high passengers’ satisfaction.
https://www.riejournal.com/article_79914_b30c6972fc0813dda715022aef2b003a.pdf
2018-11-01
349
369
10.22105/riej.2018.134686.1045
Relationship
Passengers’ satisfaction
Service Quality
Gap Analysis
A.
Adeniran
4tynil@gmail.com
1
Department of Transport Management Technology, Federal University of Technology, Akure. P. M. B. 704, Akure, Nigeria.
LEAD_AUTHOR
S.
Fadare
sfadare23@gmail.com
2
Department of Urban and Regional Planning, Obafemi Awolowo University, Ilesa Ife, Nigeria.
AUTHOR
[1] Adeniran, A. O. (2017). Assessment of Passengers’ Satisfaction of Service Quality in Murtala Muhammed International Airport, Ikeja, Lagos, Nigeria (Master Thesis at the Department of Transport Management Technology, Federal University of Technology, Akure, Nigeria).
1
[2] Aidoo, E. N., Agyemang, W., Monkah, J. E., & Afukaar, F. K. (2013). Passenger’s satisfaction with public bus transport services in Ghana: A case study of Kumasi–Accra route. Theoretical and empirical researches in urban management, 8(2), 33-44.
2
[3] Airports Council International (ACI). (2000). Quality of service at airports. ACI.
3
[4] Airports Council International (ACI) (2002). Activities and achievements.ACI.
4
[5] Ali, A. N. (2010). An assessment of the quality of intraurban bus services in the city of Enugu, Enugu State, Nigeria. Theoretical and empirical researches in urban management, 5(6 (15), 74-91.
5
[6] Al-Refaie, A., Bata, N., Eteiwi, D., & Jalham, I. (2014). Examining factors that affect passenger's overall satisfaction and loyalty: evidence from jordan airport. Jordan journal of mechanical & industrial engineering, 8(2).
6
[7] Chumakova, A. (2014). Passengers’ satisfaction on facility services in terminal 2 of Tampere airport (Bachelor’s thesis at Tampere University of Applied Sciences). Retrieved from https://www.theseus.fi/handle/10024/73689
7
[8] Anderson, E. W., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of service research, 3(2), 107-120.
8
[9]Graham, A. (2003). Managing Airports: An international perspective. Butterworth-Heinemann. An imprint of Elsevier.
9
[10] Appelbaum, S. H., & Fewster, B. M. (2003). Global aviation human resource management: contemporary employee and labour relations practices. Management research news, 26(10/11), 56-69.
10
[11] Beerli, A., Martin, J. D., & Quintana, A. (2004). A model of customer loyalty in the retail banking market. European journal of marketing, 38(1/2), 253-275.
11
[12] Oghojafor, B. A., & Adekoya, A. G. (2014). Determinants of customers’ satisfaction in the Nigerian aviation industry using analytic hierarchy process (AHP) Model. Acta Universitatis Danubius. Œconomica, 10(4).
12
[13] Brady, M. K., Cronin Jr, J. J., & Brand, R. R. (2002). Performance-only measurement of service quality: a replication and extension. Journal of business research, 55(1), 17-31.
13
[14] Budiono, O. A. (2009). Customer’ satisfaction in public bus transport: A study of travelers’ perceptions in Indonesia (Master Thesis, Service Science Program. Karlstad University).
14
[15]Cronin Jr, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218.
15
[16] Cronin Jr, J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and extension. The journal of marketing, 55-68.
16
[17] Kuo, C. W., Jouc, R. C., & Chen, T. Y. (2012). Applying loss aversion to assess the effect of air passenger’asymmetric responses to service quality on passengers’ behavioral intentions: An empirical study in cross-strait direct flights. EWGT2012Compendium of Papers.
17
[18] Fodness, D., & Murray, B. (2007). Passengers' expectations of airport service quality. Journal of services marketing, 21(7), 492-506.
18
[19] David Mc A, B. (2013). Service quality and customer satisfaction in the airline industry: A comparison between legacy airlines and low-cost airlines. American journal of tourism research, 2(1), 67-77.
19
[20] Eboli, L. &Mazzulla, G. (2012). Performance indicators for an objective measure of public transport service quality. Journal of european transport, Vol. 51.
20
[21] Horsu, E. N., & Yeboah, S. T. (2015). Influence of service quality on customer satisfaction: A study of minicab taxi services in Cape Coast, Ghana. International journal of economics, commerce and management, 3(5), 1451-1464.
21
[22] Fadare, S. O., & Adeniran, A. O. (2018). Comparative analysis of public operated airport terminal and concessioned airport terminal in Lagos, Nigeria. Discovery, 54(272), 304-318.
22
[23] Grönroos, C. (1984). A service quality model and its marketing implications. European journal of marketing, 18(4), 36-44.
23
[24] Arif, M., Gupta, A., & Williams, A. (2013). Customer service in the aviation industry–An exploratory analysis of UAE airports. Journal of air transport management, 32, 1-7.
24
[25] Ha, H. Y. (2006). An integrative model of consumer satisfaction in the context of e‐services. International journal of consumer studies, 30(2), 137-149.
25
[26] Adeniran, J. A., & Adekunle, B. O. (2016). Is service quality a correlate of customer satisfaction? Evidence from Nigerian airports. International journal of marketing studies, 8(6), 128.
26
[27] Kassim, N., & Asiah Abdullah, N. (2010). The effect of perceived service quality dimensions on customer satisfaction, trust, and loyalty in e-commerce settings: A cross cultural analysis. Asia pacific journal of marketing and logistics, 22(3), 351-371.
27
[28] Kilbourne, W. E., Duffy, J. A., Duffy, M., & Giarchi, G. (2004). The applicability of SERVQUAL in cross-national measurements of health-care quality. Journal of services marketing, 18(7), 524-533.
28
[29] Kotler, P. (1994). Marketing management, analysis, planning, implementation, and control, Philip Kotler. London: Prentice-Hall International.
29
[30] Likert, R. (1931). A Technique for the measurement of attitudes. Archives of Psychology. New York: Columbia University Press.
30
[31] Lockwood, C. and Wright, L. (1999). Principles of service marketing and management. Prentice-Hall.
31
[32] Mattozo, T. C., da Silva, G. S., Neto, A. P. F., & Costa, J. A. F. (2012, August). Logistic regression applied to airport customer satisfaction using hierarchical quality model. International conference on intelligent data engineering and automated learning (pp. 558-567). Springer, Berlin, Heidelberg.
32
[33] McIver, J., & Carmines, E. G. (1981). Unidimensional scaling. Thousand Oaks, CA: Sage.
33
[34] Ming-kei, C. & Yui Yip, L. (2016). Travelers’ Perception on Airport Satisfaction. Journal of business & economic policy, 3(2), 55-60.
34
[35] Mugenda, O. M. & Mugenda, A. G. (2003). Research methods: Quantitative and qualitative approaches. Acts Press, Nairobi.
35
[36] Natalisa, D., & Subroto, B. (2003). Effects of management commitment on service quality to increase passengers’ satisfaction of domestic airlines in Indonesia. Singapore management review, 25(1), 85-104.
36
[37] Nunnally, J. C., & Bernstein, I. H. (1967). Psychometric theory (Vol. 226). New York: McGraw-Hill.
37
[38] Ogunnaike, O. O., & Olaleke, O. (2010). Assessing the relationship between service quality and customer satisfaction; evidence from Nigerian banking industry. Global journal of management and business research, 10(3), 2-5.
38
[39] Olsen, L. L., & Johnson, M. D. (2003). Service equity, satisfaction, and loyalty: from transaction-specific to cumulative evaluations. Journal of service research, 5(3), 184-195.
39
[40] Pallant, J. (2005). SPSS survival manual: A step by step guide to data analysis using SPSS for windows. Berkshire: Open University Press.
40
[41] Zeithaml, V. A., Parasuraman, A., Berry, L. L., & Berry, L. L. (1990). Delivering quality service: Balancing customer perceptions and expectations. Simon and Schuster.
41
[42] Randheer, K., & Al-Motawa, A. A. (2011). Measuring commuters’ perception on service quality using SERVQUAL in public transportation. International journal of marketing studies, 3(1), 21.
42
[43] Krishnamurthy, R., SivaKumar, M. A. K., & Sellamuthu, P. (2010). Influence of service quality on customer satisfaction: Application of SERVQUAL model. International journal of business and management, 5(4), 117.
43
[44] Oh, S. O., & Jin-Woo, P. (2014). A Study on importance and satisfaction of airport selection attributes: focus on gimpo international airport and incheon international airport. International journal of business and social science, 5(10).
44
[45] Tiernan, S., Rhoades, D. L., & Jr, W. B. (2008). Airline Service Quality. Managing service quality, 18(3):212-224.
45
[46] Ojo, T. K. (2014). Users’ perceptions of service quality in murtala muhammed international airport (Mmia), Lagos, Nigeria. Journal of marketing and consumer research, 3, 48-53.
46
[47] Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods. Cengage Learning.
47
ORIGINAL_ARTICLE
Total quality management (TQM): Implementation in primary education system of Bangladesh
The role of primary education is to ensure the broad-based development of pupils. This means ensuring that all pupils are able to develop their cognitive, social, emotional, cultural, and physical skills to the best of their abilities and preparing them for their further school career. It is known that education in Bangladesh is highly subsided. The government has given the highest importance to the education sector to ensure education for all. A large percentage of a country's national budget is set to promote education and make it more accessible. But the education system of Bangladesh faces several problems. Low performances in primary levels and dropout are matters of concern because the low performances can result of poverty, widening disparities in education opportunities and facilities, poor school attendance, less contact time in school, lack of skilled school teachers, and lack of coordination between parents and the teachers. For that reasons, this research is conducted to implement the Total Quality Management (TQM) to improve the condition and the quality of the primary education system. Total Quality Management is a tool for ensuring proper quality in the entire organization. The main objective of the research is to eliminate problems such as the lack of quality of teachers and to improve the relation between teachers, students, and parents. Another aim of this research is to improve the quality of the environment of the class room in order to motivate the students to go to school; so, the dropout can be reduced.
https://www.riejournal.com/article_68805_d9396a907235b1c82c0650d22d02aa95.pdf
2018-11-01
370
380
10.22105/riej.2018.128170.1041
Total Quality Management
education system
Primary School
5S
Quality Assurance
Kaizen
K.
Hasan
kamrul.iem2k13@gmail.com
1
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
AUTHOR
Md. S.
Islam
saifuliem@iem.kuet.ac.bd
2
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
AUTHOR
A. T.
Shams
aurponshams@gmail.com
3
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
AUTHOR
H.
Gupta
hgupta.ipe.kuet@gmail.com
4
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
LEAD_AUTHOR
[1] Juran, J. M. (1993). Quality planning and analysis; from product development through use. New York McGraw-Hill199634.
1
[2] McCulloch, M. (1993). Total quality management: its relevance for higher education. Quality assurance in education, 1(2), 5-11.
2
[3] Sisman, M., & Turan, S. (2002). Total quality management in education (TQM in Education). PegemA, ankara.
3
[4] Sallis, E. (2014). Total quality management in education. Routledge.
4
[5] Töremen, F., Karakuş, M., & Yasan, T. (2009). Total quality management practices in Turkish primary schools. Quality assurance in education, 17(1), 30-44.
5
[6] Senge, P. M. (1991). The fifth discipline, the art and practice of the learning organization. Performance+ Instruction, 30(5), 37-37.
6
[7] Şişman, M., & Turan, S. (2002). The function of the advisory boards of the education zone in education and school management. Education research journal, 2 (6), 136-146.
7
[8] Croker, R. E. (1996). Defining instructional quality by employing the total quality management (TQM) method: A research project. Retrieved from https://files.eric.ed.gov/fulltext/ED403434.pdf
8
[9] Terry, P. M. (1996). Using Total Quality Management Principles to Implement School-Based Management. Retrieved from https://files.eric.ed.gov/fulltext/ED412590.pdf
9
[10] Lezotte, L.W. (1992), Creating the Total Quality Effective School, Effective Schools Products Ltd, Okemos, MI. Retrieved from https://files.eric.ed.gov/fulltext/ED359611.pdf
10
[11] Maguad, B. A. (1999, June). A total quality approach to Adventist education. Retrieved from http://christintheclassroom.org/vol_24/24cc_157-176.pdf
11
[12] Vaill, P. B. (1989). Managing as a performing art: New ideas for a world of chaotic change. Jossey-Bass.
12
[13] Felder, R. M., & Brent, R. (1999). How to improve teaching quality. Quality management journal, 6(2), 9-21.
13
[14] Johnson, D. W., & Todd, D. E. (1998). Harvesting effects on long-term changes in nutrient pools of mixed oak forest. Soil science society of America journal, 62(6), 1725-1735.
14
[15] Better operations. (2017). Retrieved from http://better-operations.com/2015/10/08/lean-primary-school/
15
ORIGINAL_ARTICLE
Integrated model of critical success factors of construction projects: A case of Esfahan
Nowadays, the construction managers try to accomplish the projects on time and successful simultaneously. However, the concept of success is not clear in their mind. The purpose of this paper is to identify the factors that effect on project success in the construction field; so, an integrated model of critical success factor for construction projects has been suggested. The proposed model consists of three categories of variables, i.e. people related factors, project related factors, and environmental factors. This model clarifies the definition of success in the mind of construction professionals and develops the critical success factors for construction projects through prior research. The novelty of this research is the comprehensive view of critical success factors in an integrated model format. The model has been tested on construction project managers in Esfahan. Findings show that in Esfahan the success of construction projects depends on people, project, and environment related factors, respectively. This paper clarifies the ambiguous definition of success in the mind of construction professionals.
https://www.riejournal.com/article_73517_7d179da26081f3ac6d1e063af431a412.pdf
2018-11-01
381
395
10.22105/riej.2018.143702.1053
Critical Success Factors (CSF)
people related factors
project related factors
Environment related factors
Construction projects
N.
Janatyan
n.janatyan@yahoo.com
1
Department of Management, Shahid Beheshti University, Tehran, Iran.
LEAD_AUTHOR
M. R.
Hashemianfar
hasemianfar@yahoo.com
2
Department of Civil Engineering, Institute of Daneshpajoohan, Esfahan, Iran.
AUTHOR
M.
Kasaee
masoudkass@yahoo.com
3
Department of Management, Shahid Beheshti University, Tehran, Iran.
AUTHOR
[1] Abdollahzadeh, G. R., & Faghihmaleki, H. (2017). A method to evaluate the risk-based robustness index in blast-influenced structures. Earthquakes and structures, 12(1), 47-54.
1
[2] Atkinson, R., Crawford, L., & Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International journal of project management, 24(8), 687-698.
2
[3] Avots, I. (1969). Why does project management fail? California management review, 12(1), 77-82.
3
[4] Baker, B. N., Murphy, D. C., & Fisher, D. (1997). Factors affecting project success. Project management handbook, 902-919.
4
[5] Banihashemi, S., Hosseini, M. R., Golizadeh, H., & Sankaran, S. (2017). Critical success factors (CSFs) for integration of sustainability into construction project management practices in developing countries. International journal of project management, 35(6), 1103-1119.
5
[6] Belassi, W., & Tukel, O. I. (1996). A new framework for determining critical success/failure factors in projects. International journal of project management, 14(3), 141-151.
6
[7] Bititci, U. S. (1994). Measuring your way to profit. Management decision, 32(6), 16-24.
7
[8] de Carvalho, M. M., Patah, L. A., & de Souza Bido, D. (2015). Project management and its effects on project success: Cross-country and cross-industry comparisons. International journal of project management, 33(7), 1509-1522.
8
[9] Chua, D. K. H., Kog, Y. C., & Loh, P. K. (1999). Critical success factors for different project objectives. Journal of construction engineering and management, 125(3), 142-150.
9
[10] Cooke-Davies, T. (1990). Return of the project managers. Management today, 119.
10
[11] De Wit, A. (1988). Measurement of project success. International journal of project management, 6(3), 164-170.
11
[12] Garbharran, H., Govender, J., & Msani, T. (2012). Critical success factors influencing project success in the construction industry. Acta structilia, 19(2), 90-108.
12
[13] Gudienė, N., Banaitis, A., Banaitienė, N., & Lopes, J. (2013). Development of a conceptual critical success factors model for construction projects: A case of Lithuania. Procedia engineering, 57, 392-397.
13
[14] Gudienė, N., Banaitis, A., & Banaitienė, N. (2013). Evaluation of critical success factors for construction projects–an empirical study in Lithuania. International journal of strategic property management, 17(1), 21-31.
14
[15] Hughes, M. W. (1986). Why projects fail-the effects of ignoring the obvious. Industrial engineering, 18(4), 14.
15
[16] Ihuah, P. W., Kakulu, I. I., & Eaton, D. (2014). A review of critical project management success factors (CPMSF) for sustainable social housing in Nigeria. International journal of sustainable built environment, 3(1), 62-71.
16
[17] Jugdev, K., Perkins, D., Fortune, J., White, D., & Walker, D. (2013). An exploratory study of project success with tools, software and methods. International journal of managing projects in business, 6(3), 534-551.
17
[18] Kometa, S. T., Olomolaiye, P. O., & Harris, F. C. (1995). An evaluation of clients' needs and responsibilities in the construction process. Engineering, construction and architectural management, 2(1), 57-76.
18
[19] Khosravi, S., & Afshari, H. (2011, July). A success measurement model for construction projects. Proceedings of the international conference on financial management and economics (pp. 186-190). Singapore.
19
[20] Kumaraswamy, M. M., & Chan, W. M. (1999). Factors facilitating faster construction. Journal of construction procurement.
20
[21] Lim, C. S., & Mohamed, M. Z. (1999). Criteria of project success: an exploratory re-examination. International journal of project management, 17(4), 243-248.
21
[22] Mbugua, L. M., Harris, P., Holt, G. D., & Olomolaiye, P. O. (1999, September). A framework for determining critical success factors influencing construction business performance. Proceedings of the 15th annual conference of association of researchers in construction management (pp. 255-64).
22
[23] Molwus, J. J., Erdogan, B., & Ogunlana, S. (2017). Using structural equation modelling (SEM) to understand the relationships among critical success factors (CSFs) for stakeholder management in construction. Engineering, construction and architectural management, 24(3), 426-450.
23
[24] Müller, R., & Jugdev, K. (2012). Critical success factors in projects: Pinto, Slevin, and Prescott–the elucidation of project success. International journal of managing projects in business, 5(4), 757-775.
24
[25] Duy Nguyen, L., Ogunlana, S. O., & Thi Xuan Lan, D. (2004). A study on project success factors in large construction projects in Vietnam. Engineering, construction and architectural management, 11(6), 404-413.
25
[26] Osei-Kyei, R., & Chan, A. P. (2017). Implementing public–private partnership (PPP) policy for public construction projects in Ghana: critical success factors and policy implications. International journal of construction management, 17(2), 113-123.
26
[27] Patanakul, P., & Milosevic, D. (2009). The effectiveness in managing a group of multiple projects: Factors of influence and measurement criteria. International journal of project management, 27(3), 216-233. -134
27
[28] Pinto, J. K., & Prescott, J. E. (1988). Variations in critical success factors over the stages in the project life cycle. Journal of management, 14(1), 5-18.
28
[29] Pinto, J. K., & Prescott, J. E. (1990). Planning and tactical factors in the project implementation process. Journal of management studies, 27(3), 305-327.
29
[30] Pinto, J.K., & Slevin, D. P. (1988). Project success: Definitions and measurement techniques. Project management, 1, 67-72.
30
[31] Pakseresht, A., & Asgari, G. (2012). Determining the critical success factors in construction projects: AHP approach. Interdisciplinary journal of contemporary research in business, 4(8), 383-393.
31
[32] Rubin, I. M., & Seelig, W. (1967). Experience as a factor in the selection and performance of project managers. IEEE transactions on engineering management, (3), 131-135.
32
[33] Sadeh, A., Dvir, D., & Shenhar, A. (2000). The role of contract type in the success of R&D defense projects under increasing uncertainty. Project management journal, 31(3), 14-22.
33
[34] Saqib, M., Farooqui, R. U., & Lodi, S. H. (2008, August). Assessment of critical success factors for construction projects in Pakistan. First international conference on construction in developing countries (ICCIDC-1), Karachi, Pakistan, August.
34
[35] Shah, J. B., & Murphy, J. (1995). Performance appraisals for improved productivity. Journal of management in engineering, 11(2), 26-29.
35