ORIGINAL_ARTICLE
An optimization model for aggregate production planning and control: a genetic algorithm approach
In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance, supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While the raw material purchasing, ordering, and inventory costs have been grouped into an upstream category, finished goods inventory, and delivery costs in the downstream category. The most notable differences with the other existing models of aggregate production planning are in the consideration of the cost factors and formulation process in the mathematical model. A real-life industrial case problem is formulated and solved by using a genetic algorithm to demonstrate the applicability and feasibility of the proposed model. The results indicate that the proposed model is capable of solving any type of aggregate production planning efficiently and effectively.
https://www.riejournal.com/article_94178_1bd1b260d5efd921efbf29238133674e.pdf
2019-09-01
203
224
10.22105/riej.2019.192936.1090
Aggregate Production Planning
cost optimization
Genetic Algorithm
Production System
S. M.
Ahmed
tazim_ipe@just.edu.bd
1
Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jahsore, Bangladesh.
LEAD_AUTHOR
T. K.
Biswas
tarun@just.edu.bd
2
Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jahsore, Bangladesh.
AUTHOR
C. K.
Nundy
chandankumernundy@gmail.com
3
Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jahsore, Bangladesh.
AUTHOR
[1] Gansterer, M. (2015). Aggregate planning and forecasting in make-to-order production systems. International journal of production economics, 170, 521-528.
1
[2] Kumar, G. M., & Haq, A. N. (2005). Hybrid genetic—ant colony algorithms for solving aggregate production plan. Journal of advanced manufacturing systems, 4(01), 103-111.
2
[3] Al-e, S. M. J. M., Aryanezhad, M. B., & Sadjadi, S. J. (2012). An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. The international journal of advanced manufacturing technology, 58(5-8), 765-782.
3
[4] Dakka, F, Aswin, M., & Siswojo, B. (2017). Multi-Plant multi-product aggregate production planning using genetic algorithm. International journal of engineering research and management, 4, 2349- 2058.
4
[5] Swinney, R. (2011). Selling to strategic consumers when product value is uncertain: The value of matching supply and demand. Management science, 57(10), 1737-1751.
5
[6] Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of manufacturing systems, 32(1), 1-19.
6
[7] Entezaminia, A., Heydari, M., & Rahmani, D. (2016). A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers. Journal of manufacturing systems, 40, 63-75.
7
[8] Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of cleaner production, 133, 1074-1085.
8
[9] Makui, A., Heydari, M., Aazami, A., & Dehghani, E. (2016). Accelerating benders decomposition approach for robust aggregate production planning of products with a very limited expiration date. Computers & industrial engineering, 100, 34-51.
9
[10] Hsieh, S., & Wu, M. S. (2000). Demand and cost forecast error sensitivity analyses in aggregate production planning by possibilistic linear programming models. Journal of intelligent manufacturing, 11(4), 355-364.
10
[11] Wang, R. C., & Fang, H. H. (2001). Aggregate production planning with multiple objectives in a fuzzy environment. European journal of operational research, 133(3), 521-536.
11
[12] Wang, R. C., & Liang, T. F. (2005). Aggregate production planning with multiple fuzzy goals. The international journal of advanced manufacturing technology, 25(5-6), 589-597.
12
[13] Gulsun, B., Tuzkaya, G., Tuzkaya, U. R., & Onut, S. (2009). An aggregate production planning strategy selection methodology based on linear physical programming. International journal of industrial engineering, 16(2), 135-146.
13
[14] Nowak, M. (2013). An interactive procedure for aggregate production planning. Croatian operational research review, 4(1), 247-257.
14
[15] Chakrabortty, R. K., Hasin, M. A. A., Sarker, R. A., & Essam, D. L. (2015). A possibilistic environment based particle swarm optimization for aggregate production planning. Computers & industrial engineering, 88, 366-377.
15
[16] Shyu, S. J., Lin, B. M., & Yin, P. Y. (2004). Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time. Computers & industrial engineering, 47(2-3), 181-193.
16
[17] Montgomery, J., Fayad, C., & Petrovic, S. (2006). Solution representation for job shop scheduling problems in ant colony optimisation. International workshop on ant colony optimization and swarm intelligence (pp. 484-491). Berlin, Heidelberg: Springer.
17
[18] Pal, A., Chan, F. T. S., Mahanty, B., & Tiwari, M. K. (2011). Aggregate procurement, production, and shipment planning decision problem for a three-echelon supply chain using swarm-based heuristics. International journal of production research, 49(10), 2873-2905.
18
[19] Bremermann, H. J., Oehme, R., & Taylor, J. G. (1958). Proof of dispersion relations in quantized field theories. Physical review, 109(6), 2178.
19
[20] Ramezanian, R., Rahmani, D., & Barzinpour, F. (2012). An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert systems with applications, 39(1), 1256-1263.
20
[21] Chakrabortty, R. K., & Hasin, M. A. A. (2013). Solving an aggregate production planning problem by fuzzy based genetic algorithm approach. International journal of fuzzy logic systems, 3(1), 1-15.
21
[22] Hossain, M. M., Nahar, K., Reza, S., & Shaifullah, K. M. (2016). Multi-period, multi-product, aggregate production planning under demand uncertainty by considering wastage cost and incentives. World review of business research, 6(2), 170-185.
22
[23] Savsani, P., Banthia, G., Gupta, J., & Ronak, V. (2016). Optimal aggregate production planning by using genetic algorithm. Proceedings of the international conference on industrial engineering and operations management, (IEOM) (pp. 863-874).
23
[24] Mahmud, S., Hossain, M. S., & Hossain, M. M. (2018). Application of multi-objective genetic algorithm to aggregate production planning in a possibilistic environment. International journal of industrial and systems engineering, 30(1), 40-59.
24
[25] Jamalnia, A., Yang, J. B., Feili, A., Xu, D. L., & Jamali, G. (2019). Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions. The international journal of advanced manufacturing technology, 102(1-4), 159-181.
25
[26] Al Aziz, R., Paul, H. K., Karim, T. M., Ahmed, I., & Azeem, A. (2018). Modeling and optimization of multi-layer aggregate production planning. Journal of operations and supply chain management, 11(2), 1-15.
26
[27] Mehdizadeh, E., Niaki, S. T. A., & Hemati, M. (2018). A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution. Computers & operations research, 91, 21-36.
27
[28] Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts, design for optimization of process controllers. Computer and information science, 4(2), 39.
28
[29] Chakrabortty, R. K., & Hasin, M. A. A. (2013). Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International journal of industrial engineering computations, 4, 1-12.
29
[30] Mohammadi-Andargoli, H., Tavakkoli-Moghaddam, R., Shahsavari Pour, N., & Abolhasani-Ashkezari, M. H. (2012). Duplicate genetic algorithm for scheduling a bi-objective flexible job shop problem. International journal of research in industrial engineering, 1(2), 10-26.
30
[31] Moradi, N., & Shadrokh, S. (2019). A simulated annealing optimization algorithm for equal and un-equal area construction site layout problem. International journal of research in industrial engineering, 8(2), 89-104.
31
[32] Ali, S. M., & Nakade, K. (2015). A mathematical optimization approach to supply chain disruptions management considering disruptions to suppliers and distribution centers. Operations and supply chain management, 8(2), 57-66.
32
ORIGINAL_ARTICLE
Optimizing Supermarket Supply Chain using fuzzy AHP: A Study on Save 'n' Safe in Bangladesh
In today's business landscape, the concept of the supply chain has gained significant prominence. An efficient supply chain is essential for guiding a business in an organized manner. This importance extends to supermarkets, where effective supply chain management necessitates improved communication with suppliers, customers, and internal management. Each facet of the supply chain plays a pivotal role in its own right. This study aims to investigate the crucial factors within the supermarket supply chain, drawing insights from existing literature and input from supply chain experts. The objective is to construct a framework that prioritizes these factors, considering each facet of the supply chain and arranging them from most to least critical. This research focuses on the analysis of a specific supermarket in Bangladesh, namely Save 'n' Safe, utilizing the FAHP methodology, a Multi-Criteria Decision-Making (MCDM) tool, to identify the most influential factors. The findings highlight that inventory management, internal information sharing, and accurate demand forecasting are the key determinants for Save 'n' Safe, a supermarket. Furthermore, the paper offers recommendations to enhance the current supply chain situation. The implications of this study extend not only to other supermarkets but also to various retail and grocery stores.
https://www.riejournal.com/article_94177_86006bb8ebd45fb7ce718837b80df4a3.pdf
2019-09-01
225
242
10.22105/riej.2018.149536.1060
Supply chain
fuzzy AHP
supermarket
Decision-making
Azad
Abbasi
1
Department of Industrial Engineering, Tehran University, Tehran, Iran.
LEAD_AUTHOR
Ellickson, P. B. (2011). The evolution of the supermarket industry: From A&P to Wal&Martm. Simon Graduate School of Business Working Paper No. FR 11&17.
1
Farid, M. S., Alam, M. J., Rahman, M. M., Barua, S., & Sarker, B. (2018). Direct and associated factors influencing the growth in supermarket activity in Bangladesh. Asian research journal of arts & social sciences, 1-12.
2
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of business logistics, 22(2), 1-25.
3
GUO, Y. X., & YANG, Y. B. (2008). Research on comprehensive assessment model of service quality in rural supermarkets based on FAHP. Journal of shijiazhuang railway institute (social science), 4.
4
Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial marketing management, 29(1), 65-83.
5
Mehmeti, G., Musabelliu, B., & Xhoxhi, O. (2016). The review of factors that influence the supply chain performance. Academic journal of interdisciplinary studies, 5(2), 181.
6
Nandi, R., Gowdru, N. V., & Bokelmann, W. (2017). Factors influencing smallholder farmers in supplying organic fruits and vegetables to supermarket supply chains in Karnataka, India: a transaction cost approach. International journal of rural management, 13(1), 85-107.
7
Abunar, S. M., Ali, M., Fazelrabbi, M., & Ismail, H. (2016). A study of state of food retail supply chain in Saudi Arabia: a conceptual framework. Engineering management research, 5(2), 1.
8
Abunar, Z., & Zerban, A. M. (2016). Enhancing accounting information systems to facilitate supply chain management between supermarkets/suppliers: The case of Saudi Arabia. Journal of accounting and marketing, 5(2).
9
Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). Improving supply chain performance through management capabilities. Production planning & control, 28(6–8), 473–477.
10
Gupta, V., & Abidi, N. (2017). Exploring factors affecting supply chain of IT products: a retailer's perspective. Procedia computer science, 122, 969-976.
11
Karayalcin, I. I. (1982). The analytic hierarchy process: Planning, priority setting, resource allocation. Thomas L. SAATY McGraw-Hill, New York, 1980, xiii+ 287 pages,£ 15.65.
12
Van Laarhoven, P. J., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy sets and systems, 11(1-3), 229-241.
13
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
14
Meng, T. (2014). A FAHP-based comprehensive evaluation on rural supermarket service quality: a case study of Jiangsu province. Computer modelling & new technologies, 11(12), 1.
15
Gopalan, R., Sreekumar., & Satpathy, B. (2015). Evaluation of retail service quality–a fuzzy AHP approach. Benchmarking: an international journal, 22(6), 1058-1080.
16
Naude, M. J., & Badenhorst-Weiss, J. A. (2011). The effect of problems on supply chain wide efficiency. Journal of transport and supply chain management, 5(1), 278-298.
17
Cancro, J., & Mc Ginnis, M. (2004). Evaluating the financial condition of suppliers. 89th annual international supply management conference.
18
Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International journal of production economics, 87(3), 333-347.
19
Ding, F. Y., Raghavan, B., & Pollard, S. (2007). Supplier capacity analysis for a manufacturing firm with a case study. Production planning and control, 18(6), 514-525.
20
Bhandari, R. (2014). Impact of technology on logistics and supply chain management. IOSR journal of business and management, 2, 17.
21
Mentzer, J. T., Myers, M. B., & Stank, T. P. (2006). Handbook of global supply chain management. Sage Publications.
22
Koçoğlu, İ., İmamoğlu, S. Z., İnce, H., & Keskin, H. (2011). The effect of supply chain integration on information sharing: Enhancing the supply chain performance. Procedia-social and behavioral sciences, 24, 1630-1649.
23
Kanji, G. K., & Wong, A. (1999). Business excellence model for supply chain management. Total quality management, 10(8), 1147-1168.
24
Awasthi, A., & Grzybowska, K. (2014). Barriers of the supply chain integration process. Logistics operations, supply chain management and sustainability(pp. 15-30). Springer, Cham.
25
Rouibah, K., Hamdy, H. I., & Al-Enezi, M. Z. (2009). Effect of management support, training, and user involvement on system usage and satisfaction in Kuwait. Industrial management & data systems, 109(3), 338-356.
26
Meehan, J., & Muir, L. (2008). SCM in Merseyside SMEs: benefits and barriers. The TQM journal, 20(3), 223-232.
27
Manzouri, M., Rahman, M. N. A., Arshad, H., & Ismail, A. R. (2010). Barriers of supply chain management implementation in manufacturing companies: a comparison between Iranian and Malaysian companies. Journal of the chinese institute of industrial engineers, 27(6), 456-472.
28
Mahmood, W. H. W., Muhamad, M. R., & Tahar, N. M. (2009). Supply chain management: after business process re-engineering. International journal of industrial and systems engineering, 3(5), 411-416.
29
Al-Shboul, M. D. A. R., Barber, K. D., Garza-Reyes, J. A., Kumar, V., & Abdi, M. R. (2017). The effect of supply chain management practices on supply chain and manufacturing firms' performance. Journal of manufacturing technology management, 28(5), 577-609.
30
Ou, C. S., Liu, F. C., Hung, Y. C., & Yen, D. C. (2010). A structural model of supply chain management on firm performance. International journal of operations & production management, 30(5), 526-545.
31
Li, S., Rao, S. S., Ragu-Nathan, T. S., & Ragu-Nathan, B. (2005). Development and validation of a measurement instrument for studying supply chain management practices. Journal of operations management, 23(6), 618-641.
32
ORIGINAL_ARTICLE
An optimization model for cold chain food distribution
Food product has a characteristic of continuous quality deterioration until the food is consumed. Cold chain distribution can improve the sustainability of the product quality but requires a more significant investment for storage facilities and vehicles as well as higher operation cost to control the temperature. This research focuses on a distribution problem faced by an ice cream distributor. In this paper, we developed a mixed-integer non-linear programming model to minimize the total cost, which consists of fixed cost, transportation cost of the vehicles, energy cost to keep the cold storage temperature, and inventory cost. The model considers the vehicle characteristics and hard time-windows for the distributor and all the stores. The implementation of this model demonstrates that the proposed route is able to reduce the total cost.
https://www.riejournal.com/article_96952_99741663629e2652ff59f7cf563e0cf3.pdf
2019-09-01
243
253
10.22105/riej.2019.202178.1097
Frozen food
cold chain
perishable
Distribution
Integer programming
Time-Windows
A. K.
Garside
annisa_garside@yahoo.com
1
Department of Industrial Engineering, Faculty of Technic, University of Muhammadiyah Malang, Indonesia.
LEAD_AUTHOR
[1] Tunali, S., & Oztuzcu, G. (2018). A two-phase approach for supply chain network design: a real-world case study from automotive industry. International journal of research in industrial engineering, 7(1), 1-18.
1
[2] Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. Or spectrum, 32(4), 863-904.
2
[3] Jackson, V., Blair, I. S., McDowell, D. A., Kennedy, J., & Bolton, D. J. (2007). The incidence of significant foodborne pathogens in domestic refrigerators. Food control, 18(4), 346-351.
3
[4] Montanari, R. (2008). Cold chain tracking: a managerial perspective. Trends in food science & technology, 19(8), 425-431.
4
[5] Aiello, G., La Scalia, G., & Micale, R. (2012). Simulation analysis of cold chain performance based on time–temperature data. Production planning & control, 23(6), 468-476.
5
[6] Laguerre, O., Hoang, H. M., & Flick, D. (2013). Experimental investigation and modelling in the food cold chain: Thermal and quality evolution. Trends in food science & technology, 29(2), 87-97.
6
[7] Defraeye, T., Cronjé, P., Berry, T., Opara, U. L., East, A., Hertog, M., ... & Nicolai, B. (2015). Towards integrated performance evaluation of future packaging for fresh produce in the cold chain. Trends in food science & technology, 44(2), 201-225.
7
[8] Bogataj, M., Bogataj, L., & Vodopivec, R. (2005). Stability of perishable goods in cold logistic chains. International journal of production economics, 93, 345-356.
8
[9] Badia-Melis, R., Mc Carthy, U., Ruiz-Garcia, L., Garcia-Hierro, J., & Villalba, J. R. (2018). New trends in cold chain monitoring applications-A review. Food control, 86, 170-182.
9
[10] Ndraha, N., Hsiao, H. I., Vlajic, J., Yang, M. F., & Lin, H. T. V. (2018). Time-temperature abuse in the food cold chain: Review of issues, challenges, and recommendations. Food control, 89, 12-21.
10
[11] Zhao, H., Liu, S., Tian, C., Yan, G., & Wang, D. (2018). An overview of current status of cold chain in China. International journal of refrigeration, 88, 483-495.
11
[12] Chen, H. K., Hsueh, C. F., & Chang, M. S. (2009). Production scheduling and vehicle routing with time windows for perishable food products. Computers & operations research, 36(7), 2311-2319.
12
[13] Zhang, G., Habenicht, W., & Spieß, W. E. L. (2003). Improving the structure of deep frozen and chilled food chain with tabu search procedure. Journal of food engineering, 60(1), 67-79.
13
[14] Hsu, C. I., Hung, S. F., & Li, H. C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of food engineering, 80(2), 465-475.
14
[15] Osvald, A., & Stirn, L. Z. (2008). A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. Journal of food engineering, 85(2), 285-295.
15
[16] Zhang, Y., & Chen, X. D. (2014). An optimization model for the vehicle routing problem in multi-product frozen food delivery. Journal of applied research and technology, 12(2), 239-250.
16
[17] Amorim, P., & Almada-Lobo, B. (2014). The impact of food perishability issues in the vehicle routing problem. Computers & industrial engineering, 67, 223-233.
17
[18] Wang, X., Wang, M., Ruan, J., & Zhan, H. (2016). The multi-objective optimization for perishable food distribution route considering temporal-spatial distance. Procedia computer science, 96, 1211-1220.
18
[19] Song, B. D., & Ko, Y. D. (2016). A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. Journal of food engineering, 169, 61-71.
19
[20] Hsiao, Y. H., Chen, M. C., & Chin, C. L. (2017). Distribution planning for perishable foods in cold chains with quality concerns: Formulation and solution procedure. Trends in food science & technology, 61, 80-93.
20
[21] Trihardani, L., & Dewi, O. A. C. (2017). Pengembangan Algoritma Hybrid Metaheuristik Untuk Penentuan Rute Pengiriman Produk Perishable. Jurnal teknik industri, 18(2), 191-206.
21
[22] MOEA-IDB. (2001). A handout of training class for industry technology talent in 2001. Taiwan.
22
[23] Khoidir, A. (2018). Vehicle routing problem with multiple time windows untuk mengoptimalkan rute distribusi ice cream (PT. Lukindari Permata Malang) (Doctoral dissertation, University of Muhammadiyah Malang).
23
ORIGINAL_ARTICLE
Calculation of fuzzy matrices determinant
Matrix and its determinant are two basic tools, which are important in financial, accounting, and economic affairs. Therefore, in this paper, a simple and effective method is proposed to obtain the determinant of fuzzy matrices. First, using arithmetic operations based on Transmission Average (TA),the second order fuzzy determinant is calculated.Then,Sarrus rule is defined to calculate third order fuzzy determinant. Finally, by defining minor of fuzzy matrix and ijth adjugate of the fuzzy matrix, nth order fuzzy determinant is calculated. The effectiveness and applicability of the proposed method are verified by solving some numerical examples.
https://www.riejournal.com/article_96953_dd87d77c5812790a284a6ec9c8e22529.pdf
2019-09-01
254
261
10.22105/riej.2019.202666.1098
Arithmetic operations
fuzzy approximation
Fuzzy determinant
F.
Babakordi
babakordif@yahoo.com
1
Department of Mathematics, Faculty of Science, Gonbad Kavous University, Gonbad Kavous, Iran.
LEAD_AUTHOR
N. A.
Taghi-Nezhad
ntaghinezhad@gmail.com
2
Department of Mathematics, Faculty of Science, Gonbad Kavous University, Gonbad Kavous, Iran.
AUTHOR
[1] Khalifa, H. A. (2019). On solving two-person zero-sum fuzzy matrix games via linear programming approach. International journal of research in industrial engineering, 8(1), 17-27.
1
[2] Amin-Tahmasbi, H., & Hami, M. (2019). Stuff scheduling in capillary marketing and analyse of its impact on the company’s financial issues. International journal of research in industrial engineering, 8(1), 53-65.
2
[3] Tavakoli, M. M., Molavi, B., & Shirouyehzad, H. (2017). Organizational performance evaluation considering human capital management approach by fuzzy-dea: a case study. International journal of research in industrial engineering, 6(1), 1-16.
3
[4] Ezzati, R., Maleknejad, K., & Fathizadeh, E. (2017). CAS Wavelet Function Method for Solving Abel Equations with Error Analysis. International journal of research in industrial engineering, 6(4), 350-364.
4
[5] Taghi-Nezhad, N. A., & Taleshian, F. (2018). A Solution Approach for Solving Fully Fuzzy Quadratic Programming Problems. Journal of applied research on industrial engineering, 5(1), 50-61.
5
[6] Adabitabar Firozja, M., Babakordi, F., & Shahhosseini, M. (2011). Gauss elimination algorithm for interval matrix. International journal of industrial mathematics, 3(1), 9-15.
6
[7] Babakordi, F., & Allahviranloo, T. (2016). An efficient method for solving LR fuzzy dual matrix systems. Journal of intelligent & fuzzy systems, 30(1), 575-581.
7
[8] Nasseri, S. H., Taghi-Nezhad, N., & Ebrahimnejad, A. (2017). A note on ranking fuzzy numbers with an area method using circumcenter of centroids. Fuzzy information and engineering, 9(2), 259-268.
8
[9] Goodarzi, F. K., Taghinezhad, N. A., & Nasseri, S. H. (2014). A new fuzzy approach to solve a novel model of open shop scheduling problem. Univ Politeh Buchar Sci Bull Ser A Appl Math Phys, 76(3), 199-210.
9
[10] Taleshian, F., Fathali, J., & Allah Taghi-Nezhad, N. (2018). Fuzzy majority algorithms for the 1-median and 2-median problems on a fuzzy tree. Fuzzy information and engineering, 10(2), 225-248.
10
[11] Taghi-nezhad, N., Naseri, H., Khalili Goodarzi, F., &Taleshian Jelodar, F. (2015). Reactive scheduling presentation for an open shop problem focused on jobs’ due dates. Journal of production and operations management, 6(2), 95-112.
11
[12] Nasseri, S. H., Khalili, F., Taghi-Nezhad, N., & Mortezania, S. (2014). A novel approach for solving fully fuzzy linear programming problems using membership function concepts. Ann. Fuzzy Math. Inform, 7(3), 355-368.
12
[13] Fazel Rabbi, M. (2018). Assessment of fuzzy failure mode and effect analysis (FMEA) for reach stacker crane (RST): A case study. International journal of research in industrial engineering, 7(3), 336-348.
13
[14] Kim, J. B., Baartmans, A., & Sahadin, N. S. (1989). Determinant theory for fuzzy matrices. Fuzzy sets and systems, 29(3), 349-356.
14
[15] Ragab, M. Z., & Emam, E. G. (1995). The determinant and adjoint of a square fuzzy matrix. Information sciences, 84(3-4), 209-220.
15
[16] Abbasi, F., Allahviranloo, T., & Abbasbandy, S. (2015). A new attitude coupled with fuzzy thinking to fuzzy rings and fields. Journal of intelligent & fuzzy systems, 29(2), 851-861.
16
[17] Allahviranloo, T., Perfilieva, I., & Abbasi, F. (2018). A new attitude coupled with fuzzy thinking for solving fuzzy equations. Soft computing, 22(9), 3077–3095.
17
[18] Dhar, M. (2013). A Note on Determinant and Adjoint of Fuzzy Square Matrix. IJ intelligent systems and applications, 5, 58-67.
18
[19] Allahviranloo, T., & Babakordi, F. (2017). Algebraic solution of fuzzy linear system as: A ̃X ̃+B ̃X ̃ =Y ̃. Soft computing, 21(24), 7463-7472.
19
[20] Taghi-Nezhad, N. A. (2019). The p-median problem in fuzzy environment: proving fuzzy vertex optimality theorem and its application. Soft computing, 23(22), 11399-11407.
20
[21] Klir, G. J., Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. Upper Saddlie River: Prentice-Hall PTR.
21
[22] Khalili, F., Naseri, H., & Taghi-Nezhad, N. A. (2019). A new interactive approach for solving fully fuzzy mixed integer linear programming problems. Yugoslav journal of operations research. http://yujor.fon.bg.ac.rs/index.php/yujor/article/view/692/649
22
[23] Nasseri, S. H., Taghi-Nezhad, N. A., & Ebrahimnejad, A. (2017). A novel method for ranking fuzzy quantities using center of incircle and its application to a petroleum distribution center evaluation problem. International journal of industrial and systems engineering, 27(4), 457-484.
23
ORIGINAL_ARTICLE
Some fixed point results involving a general LW-type cyclic mapping in complete b-metric-like spaces
In recent years, the research of the fixed point theorem is a hot topic all the time. In this paper, we propose the notion of new mapping, that is, a general LW-type cyclic mapping, in a complete b-metric-like spaces. Then, we obtain the existence and uniqueness theorem of its fixed point. Moreover, we give an example to illustrate the main results of this paper.
https://www.riejournal.com/article_94180_9b07fd744930f2a14d13fc3e89a78e4d.pdf
2019-09-01
262
273
10.22105/riej.2019.195844.1093
B-metric-like space
Fixed Point Theorem
LW-type cyclic mapping
S. Q.
Weng
wengsq96@163.com
1
Ideological and Political Foundation, SiChuan Sanhe College of Professionals, Hejiang, Luzhou, 646200, Sichuan, China.
LEAD_AUTHOR
[1] Banach, S. (1922). On operations in abstract sets and their application to integral equations. Fund. math, 3 (1), 133-181. (In French)
1
[2] Rakotch, E. (1962). A note on contractive mappings. Proceedings of the American mathematical society, 13(3), 459-465.
2
[3] Matthews, S. G. (1994). Partial metric topology. Eighth summer conference at queens college annals of the new york academy of sciences, 728, 183-197.
3
[4] Bukatin, M., Kopperman, R., Matthews, S., & Pajoohesh, H. (2006). Partial metric spaces. The American mathematical monthly, 116(8), 708-718.
4
[5] Amini-Harandi, A. (2012). Metric-like spaces, partial metric spaces and fixed points. Fixed point theory and applications, 2012(1), 204.
5
[6] Czerwik, S. (1993). Contraction mappings in $ b $-metric spaces. Acta mathematica et informatica universitatis ostraviensis, 1(1), 5-11.
6
[7] Alghamdi, M. A., Hussain, N., & Salimi, P. (2013). Fixed point and coupled fixed point theorems on b-metric-like spaces. Journal of inequalities and applications, 2013(1), 402.
7
[8] Banach, S. (1922). On operations in abstract sets and their application to integral equations. Fund. math, 3 (1), 133-181. (In French)
8
[9] Lei Ming,. Wu Ding-ping,. (2017). Fixed Point Theorems Concerning New Type Cyclic Maps in Complete B-metric-like Spaces. Journal of Chengdu University of information technology. 1(32), 82-85.
9
[10] Aydi, H., Felhi, A., & Sahmim, S. (2016). On common fixed points for ((alpha,psi))-contractions and generalized cyclic contractions in b-metric-like spaces and consequences. Journal of nonlinear sciences and applications (JNSA), 9(5), 2492-2510.
10
[11] Nashine, H. K., & Kadelburg, Z. (2017). Existence of solutions of cantilever beam problem via (α-β-FG)-contractions in b-metric-like spaces. Filomat, 31(11), 3057-3074.
11
ORIGINAL_ARTICLE
A signed distance method for solving multi-objective transportation problems in fuzzy environment
This paper aims to study the multi-objective transportation problem with fuzzy parameters. These fuzzy parameters represented as (α, β) interval-valued fuzzy numbers instead of the normal fuzzy numbers. Using the signed distance ranking, the problem converted into the corresponding crisp multi-objective transportation problem. Then, the solution method introduced by [8] for solving the problem is applied. This method provides the ideal and the set of all fuzzy (α, β) efficient solutions. The advantage of this method is more flexible than the standard multi-objective transportation problem, where it allows the decision maker to choose the (α, β) levels of fuzzy numbers he is willing. A numerical example to illustrate the utility, effectiveness, and applicability of the method is given.
https://www.riejournal.com/article_94179_7d26c90796da3ea354f2dfdef0cc1dec.pdf
2019-09-01
274
282
10.22105/riej.2019.193041.1091
Multi-objective transportation problem
(α
β) fuzzy numbers
signed distance function
Multi-objective decision making problem
Optimal transportation
Optimal flowing method
H.
Abd El-Wahed Khalifa
hamiden_2008@yahoo.com
1
Department of Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.
LEAD_AUTHOR
[1] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141.
1
[2] Bit, A. K., Biswal, M. P., & Alam, S. S. (1992). Fuzzy programming approach to multicriteria decision making transportation problem. Fuzzy sets and systems, 50(2), 135-141.
2
[3] Chanas, S., Kołodziejczyk, W., & Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy sets and systems, 13(3), 211-221.
3
[4] Chiang, J. (2001). Fuzzy linear programming based on statistical confidence interval and interval-valued fuzzy set. European journal of operational research, 129(1), 65-86.
4
[5] Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy sets and systems, 3(1), 37-48.
5
[6] Ezzati, R., Kharram, E., & Enayati, R. (2015). A new algorithm to solve fully fuzzy linear programming problems using the MOLP. Applied mathematical modelling, 3(12), 3183- 3193.
6
[7] Hamadameen, O. A. (2018). A novel technique for solving multi-objective linear programming problems. Aro- the scientific journal of Koya university, 1, 1-8.
7
[8] Jayalakshmi, M., & Sujatha, V.(2018). A new algorithm to solve multi-objective assignment problem. International journal of pure and applied mathematics, (119), 719- 724.
8
[9] Kaur, L., Rakshit, M., & Singh, S. (2018). A new approach to solve multi-objective transportation problem. Applications & applied mathematics, 13(1).
9
[10] Kiruthiga, M., & Loganathan, C. (2015). Fuzzy multi-objective linear programming problem using membership function. International journal of science, engineering, and technology, applied sciences, 5(8), 1171-1178.
10
[11] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
11
[12] Leberling, H. (1981). On finding compromise solutions in multicriteria problems using the fuzzy min-operator. Fuzzy sets and systems, 6(2), 105-118.
12
[13] Mahmoudi, F., & Nassrei, S. H. (1981). A new approach to solve fully fuzzy linear programming problem. Journal of applied research on industrial engineering, 6(2), 139- 149.
13
[14] Maity, G., & Roy, S. K. (2016). Solving multi-objective transportation problem with interval goal using utility function approach. International journal of operational research, 27(4), 513-529.
14
[15] Najafi, H. S., Edalatpanah, S. A., & Dutta, H. (2016). A nonlinear model for fully fuzzy linear programming with fully unrestricted variables and parameters. Alexandria engineering journal, 55(3), 2589-2595.
15
[16] Nomani, M. A., Ali, I., & Ahmed, A. (2017). A new approach for solving multi-objective transportation problems. International journal of management science and engineering management, 12(3), 165-173.
16
[17] Oheigeartaigh, M. (1982). A fuzzy transportation algorithm. Fuzzy sets and systems, 8(3), 235- 243.
17
[18] Pandian, P., & Anuradha, D. (2011). A new method for solving bi-objective transportation problems. Australian journal of basic and applied sciences, 5(10), 67-74.
18
[19] Rommelfanger, H., Hanuscheck, R., & Wolf, J. (1989). Linear programming with fuzzy objectives. Fuzzy sets and systems, 29(1), 31-48.
19
[20] Sakawa, M., & Yano, H. (1985). Interactive decision making for multi-objective linear fractional programmingproblems with fuzzy parameters. Cybernetics and system, 16(4), 377-394.
20
[21] Tanaka, H., & Asai, K. (1984). Fuzzy linear programming problems with fuzzy numbers. Fuzzy sets and systems, 13(1), 1-10.
21
[22] Thamaraiselvi, A., & Santhi, R. (2015). Optimal solution of fuzzy transportation problem using hexagonal fuzzy numbers. International Journal: Scientific and engineering research, 6, 2229-5518.
22
[23] Yu, V. F., Hu, K. J., & Chang, A. Y. (2015). An interactive approach for the multi-objective transportation problem with interval parameters. International journal of production research, 53(4), 1051-1064.
23
[24] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
24
[25] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55.
25
ORIGINAL_ARTICLE
Evaluation of the efficiency by DEA a case study of hospital
Data Envelopment Analysis (DEA) is a nonparametric technique used to determine the relative efficiency of Decision Making Units (DMUs). Results from DEA analysis yield important information regarding the optimal operating capabilities of each unit. This paper studies DEA to hospital sectors and identifies their rankings during a period of six years. Data for the study comes from a hospital in the North of Iran. This article compares the performance of different parts of the hospital over the years. It can also aid improve hospital performance.
https://www.riejournal.com/article_100768_3310e9849f537d1c5ba5dc13afbf6816.pdf
2019-09-01
283
293
10.22105/riej.2020.211600.1106
Efficiency
super-efficiency method
Hospital sector
Kh.
Ghaziyani
ghaziyani89@gmail.com
1
Department of Mathematics, Ayandegan Insttitute of Higher Education, Tonekabon, Iran.
LEAD_AUTHOR
B.
Ejlaly
2
Department of Engineering, Amirkabir University, Tehran, Iran.
AUTHOR
S. F.
Bagheri
gh.bagheri@gmail.com
3
Department of Mathematics, Azad University, Branch Lahijan, Guilan, Iran.
AUTHOR
[1] Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.
1
[2] Barouni, M., Amiresmaieli, M. R., Shahravan, A., & Amini, S. (2017). The efficiency assessment of dental units using data envelopment analysis approach: The case of Iran. Iranian journal of public health, 46(4), 552.
2
[3] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
3
[4] Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. International journal of systems science, 20(7), 1099-1118.
4
[5] Cooper, W. W., Seiford, L. M., & Zhu, J. (Eds.). (2011). Handbook on data envelopment analysis (Vol. 164). Springer Science & Business Media.
5
[6] Cribari-Neto, F., & Zeileis, A. (2009). Beta regression in R. Department of statistics and mathematics x, WU Vienna University of Economics and Business, Vienna.
6
[7] Cheng, Z., Cai, M., Tao, H., He, Z., Lin, X., Lin, H., & Zuo, Y. (2016). Efficiency and productivity measurement of rural township hospitals in China: a bootstrapping data envelopment analysis. BMJ open, 6(11), e011911.
7
[8] Flokou, A., Aletras, V., & Niakas, D. (2017). A window-DEA based efficiency evaluation of the public hospital sector in Greece during the 5-year economic crisis. PloS one, 12(5), e0177946.
8
[9] Ghaem Panah, M., & Alaedin, F. (2002). Establishment of performance-based management in hospital emergency Zyayyan. Tehran: Institute of Health Researchers.
9
[10] Jia, T., & Yuan, H. (2017). The application of DEA (data envelopment analysis) window analysis in the assessment of influence on operational efficiencies after the establishment of branched hospitals. BMC health services research, 17(1), 265.
10
[11] Moradi, G., Piroozi, B., Safari, H., Nasab, N. E., Bolbanabad, A. M., & Yari, A. (2017). Assessment of the efficiency of hospitals before and after the implementation of health sector evolution plan in Iran based on Pabon Lasso model. Iranian journal of public health, 46(3), 389.
11
[12] Vaňková, I., & Vrabková, I. (2014). The factors influencing economic efficiency of the hospital bed care in terms of the regional allowance organizations. Review of economic perspectives, 14(3), 233-248.
12