ORIGINAL_ARTICLE
Implementation of Six Sigma DMAIC methodology for increasing the competitiveness of SMEs in Ethiopia
Six Sigma has gained wide acceptance as an improvement methodology to enhance the organization competitiveness in market. For SMEs building a competitive advantage is a difficult task. Changes in the economic environment affect the way such entities perceive factors which could help them not only survive on the market but shape their competitiveness. In the period of significant economic turbulence, the factors that play a major role in shaping the competitive market position are company image (product brand) and lower product price. This study uses Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) approach as a framework to identify, quantify, and eliminate sources of variation in Meseret Gabi Machinery Metal Works Enterprise in Dessie (Ethiopia). This helps to improve the competitiveness of the enterprise in the market place by addressing the complaints and requirements of the customer continuously.
https://www.riejournal.com/article_122755_4e262ebbe6e305dad9d06286fd9bcc9b.pdf
2021-03-01
1
8
10.22105/riej.2021.266497.1183
Keywords: Six Sigma
DMAIC Methodology
Competitiveness
Small and Medium Enterprises (SMEs)
A.
Ali
abdellayimam1@gmail.com
1
Mechanical Engineering Department, School of Mechanical and Chemical Engineering, Institute of Technology, Woldia University, Woldia, Ethiopia.
LEAD_AUTHOR
[1] Sipa, M., Gorzeń-Mitka, I., & Skibiński, A. (2015). Determinants of competitiveness of small enterprises: Polish perspective. Procedia economics and finance, 27, 445-453. https://doi.org/10.1016/S2212-5671(15)01019-9
1
[2] Vandenberg, P. (2009). Micro, small and medium-sized enterprises and the global economic crisis: impacts and policy responses. ILO.
2
[3] Gurmeet, S., & Belwal, R. (2008). Entrepreneurship and SMEs in Ethiopia: evaluating the role, prospects and problems faced by women in this emergent sector. Gender in management: an international journal, 23(2), 120-136.
3
[4] Okpara, J. O. (2011). Factors constraining the growth and survival of SMEs in Nigeria: implications for poverty alleviation. Management research review, 34(2), 156-171. https://doi.org/10.1108/01409171111102786
4
[5] Ayele, A. W., & Derseh, A. B. (2020). Challenges that hinder the sustainability of small and medium scale enterprises in East Gojjam Zone, Northern Ethiopia. International journal of development research, 10(07), 37926-37934. https://doi.org/10.37118/ijdr.19201.07.2020
5
[6] Evans, J. R., & Lindsay, W. M. (2014). An introduction to six sigma and process improvement. Cengage learning. Stamfort: Cengage Learning.
6
[7] Schroeder, R. G., Linderman, K., Liedtke, C., & Choo, A. S. (2008). Six Sigma: definition and underlying theory. Journal of operations management, 26(4), 536-554.
7
[8] Moosa, K., & Sajid, A. (2010). Critical analysis of Six Sigma implementation. Total quality management, 21(7), 745-759.
8
[9] Lei, G., Wang, T., Zhu, J., Guo, Y., & Wang, S. (2015). System-level design optimization method for electrical drive systems—Robust approach. IEEE transactions on industrial electronics, 62(8), 4702-4713.
9
[10] Smętkowska, M., & Mrugalska, B. (2018). Using Six Sigma DMAIC to improve the quality of the production process: a case study. Procedia-social and behavioral sciences, 238, 590-596.
10
[11] Ramanan, L., & Ramanakumar, K. P. V. (2014). Necessity of six sigma–as a measurement metric in measuring quality of higher education. Intnl journal of business management invention, 3(1), 28-30.
11
[12] Ramanan, L., Kumar, M., & Ramanakumar, K. P. V. (2014). Six sigma–DMAIC frame work for enhancing quality in engineering educational institutions. International journal of business management invention, 3(1), 36-40.
12
[13] Ali, A. Y. (2020). Six Sigma-DMAIC and food waste hierarchy-based framework for reducing food waste in University canteens in Ethiopia. International journal of research in industrial engineering, 9(1), 77-83.
13
[14] John, B., & Areshankar, A. (2018). Reduction of rework in bearing end plate using six sigma methodology: a case study. Journal of applied research on industrial engineering, 5(1), 10-26.
14
[15] Hernadewita, H., Ismail, M., Nurdin, M., & Kusumah, L. (2019). Improvement of magazine production quality using six sigma method: case study of a PT. XYZ. Journal of applied research on industrial engineering, 6(1), 71-79.
15
[16] Callan, B., & Guinet, J. (2000). Enhancing the competitiveness of SMEs through innovation. Proceedings of the conference for ministers responsible for SMEs and industry ministers. Conference conducted at the meeting of Economic Co-operation and Development, Bologna, Italy.
16
[17] Deeb, S., Bril-El Haouzi, H., Aubry, A., & Dassisti, M. (2018). A generic framework to support the implementation of six sigma approach in SMEs. IFAC-PapersOnLine, 51(11), 921-926.
17
[18] Fouweather, T., Coleman, S., & Thomas, A. (2006). Six sigma training programmes to help SMEs improve. Intelligent production machines and systems, 39-44. https://doi.org/10.1016/B978-008045157-2/50014-6
18
[19] Garrido-Vega, P., Sacristán-Díaz, M., & Magaña-Ramírez, L. M. (2016). Six sigma in SMES with low production volumes. A successful experience in aeronautics. Universia business review, (51), 52-71. https://www.redalyc.org/pdf/433/43347130003.pdf
19
[20] Abbes, N., Sejri, N., Chaabouni, Y., & Cheikhrouhou, M. (2018, December). Application of Six Sigma in clothing SMEs: A case study. IOP conference series: materials science and engineering (Vol. 460, No. 1, p. 012009). Istanbul, Turkey: IOP Publishing.
20
[21] Amitrano, F. G., Estorilio, C. C. A., de Oliveira Franzosi Bessa, L., & Hatakeyama, K. (2016). Six Sigma application in small enterprise. Concurrent engineering, 24(1), 69-82. https://doi.org/10.1177/1063293X15594212
21
[22] Shrivastava, P. K. (2017). Implementing the lean Six Sigma as a strategy in a small medium enterprises (SMEs). International journal of bussiness administration and management, 7(1), 131-140.
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[23] Antony, J. (2008). Can Six Sigma be effectively implemented in SMEs? International journal of productivity and performance management, 57(5), 420-423. https://doi.org/10.1108/17410400810881863
23
[24] Dai, S., & Zhang, M. (2015, March). Study and analysis on Six Sigma management in SMEs. 2015 international conference on education technology, management and humanities science (ETMHS 2015). Atlantis Press. https://doi.org/10.2991/etmhs-15.2015.251
24
[25] Fonseca, L. M. (2017). In search of six sigma in portuguese SMEs. International journal of industrial engineering and management, 8(2017), 31-38.
25
[26] Meshram, A. (2015). Importance of Six Sigma in small and medium enterprises to improve the productivity. International journal of engineering and management research (IJEMR), 5(1), 139-144.
26
Vijayaram, T. R., Sulaiman, S., Hamouda, A. M. S., & Ahmad, M. H. M. (2006). Foundry quality control aspects and prospects to reduce scrap rework and rejection in metal casting manufacturing industries. Journal of materials processing technology, 178(1-3), 39-43.
27
ORIGINAL_ARTICLE
Frontal and non-frontal face detection using deep neural networks (DNN)
Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.
https://www.riejournal.com/article_122236_f083509815545e716ec35e8475a2c894.pdf
2021-03-01
9
21
10.22105/riej.2021.264744.1177
Face recognition
Deep Neural Networks (DNN)
OpenCV
NumPy
PyCharm
Python
Machine Learning
N.
Prasad
prasadnitin05@gmail.com
1
Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.
LEAD_AUTHOR
B.
Rajpal
bhawesh2020@gmail.com
2
Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.
AUTHOR
K. K.
R. Mangalore
kaushal.rao.m@gmail.com
3
Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.
AUTHOR
R.
Shastri
ravishastri9031@gmail.com
4
Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.
AUTHOR
N.
Pradeep
nikithapradeep11@gmail.com
5
Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.
AUTHOR
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1
[2] Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global. DOI: 10.4018/978-1-7998-1313-2.ch006
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[3] Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems, 24, 134-149.
3
[4] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International journal of engineering and advanced technology (IJEAT), 8(3), 347-353.
4
[5] Kumar, R., Edaltpanah, S. A., Jha, S., Broumi, S., & Dey, A. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23, 5-15.
5
[6] Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.
6
[7] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex and intelligent systems, 5(2), 255-263. https://doi.org/10.1007/s40747-019-0108-1
7
[8] Pratihar, J., Kumar, R., Edalatpanah, S. A., & Dey, A. (2021). Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex and intelligent systems, 7(1), 29-40. https://doi.org/10.1007/s40747-020-00153-4
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[9] Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology, 8(3), 385-390.
9
[10] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple t-norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.
10
[11] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.
11
[12] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Soft subring theory under interval-valued neutrosophic environment (Vol. 36). Neutrosophic sets and systems, 36, 193-219.
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[13] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Introduction to interval-valued neutrosophic subring (Vol. 36). Neutrosophic sets and systems, 36, 220-245.
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[14] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic hypersoft subgroup. Neutrosophic sets and systems, 33, 208-233.
14
[15] Kumar, R., Edalatpanah, S. A., & Mohapatra, H. (2020). Note on “optimal path selection approach for fuzzy reliable shortest path problem”. Journal of intelligent and fuzzy systems, 39(5), 7653- 7656.
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[16] Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA), 9(2), 132-161. doi: 10.4018/IJFSA.2020040106
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[17] Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering, 4(1), 1-7.
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[18] Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering, 8(6), 724-731.
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[19] Kumar, R., Edalatpanah, S. A., Gayen, S., & Broum, S. (2021). Answer note “a novel method for solving the fully neutrosophic linear programming problems: suggested modifications”. Neutrosophic sets and systems, 39(1), 12.
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[20] Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research, 8(4), 975-982.
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[21] Mohapatra, H., Rath, S., Panda, S., & Kumar, R. (2020). Handling of man-in-the-middle attack in wsn through intrusion detection system. International journal of emerging trends in engineering research, 8(5), 1503-1510.
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89
ORIGINAL_ARTICLE
A conceptual framework of green smart IoT-based supply chain management
The green smart supply chain is a phenomenon that has emerged as a result of the development of sustainable and smart business and information technology trends. Sustainable and green supply chains are an innovative phenomenon that uses information technology to improve the quality of activities in operating areas. In order to ensure that activities are adapted to social and environmental needs. In this regard, the Internet of Things is one of the most important components of technology infrastructure for smart. For this purpose, in this research, a framework for implementing a green IoT-based supply chain is presented. This framework is based on the four-stage architecture of the Internet of Things and has been created by emphasizing the literature and the interaction and review of the opinions of active experts in this field. This framework illustrates the direct relationship between data generation and how it interacts with the sectors affected by environmental sustainability and outlines a clear pathway for sustainable and green decision-making in the supply chain. This framework has been endorsed by experts in the supply chain field and can pave the way for effective implementation of the green supply chain with an emphasis on technology in manufacturing organizations.
https://www.riejournal.com/article_127067_02ff1be5caa1701ba2adb589bd17b69a.pdf
2021-03-01
22
34
10.22105/riej.2021.274859.1189
Green Internet of things (G-IoT)
Green supply chain
Smart business
Smart Supply Chain
H.
Nozari
ham.nozari.eng@iauctb.ac.ir
1
Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
M.
Fallah
mohammad.fallah43@yahoo.com
2
Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
A.
Szmelter-Jarosz
a.szmelter@ug.edu.pl
3
Department of Logistics, Faculty of Economics, University of Gdańsk, Poland.
AUTHOR
[1] Katakis, I. (2015). Mining urban data (part A). Journal of information systems, 54, 113-114. https://doi.org/10.1016/j.is.2015.08.002
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2
[3] Andrienko, G., Gunopulos, D., Ioannidis, Y., Kalogeraki, V., Katakis, I., Morik, K., & Verscheure, O. (2017). Mining urban data (Part C). Journal of information systems, 64, 219-220. https://doi.org/10.1016/j.is.2016.09.003
3
[4] Jara, A. J., Bocchi, Y., & Genoud, D. (2014, September). Social internet of things: the potential of the Internet of Things for defining human behaviours. 2014 international conference on intelligent networking and collaborative systems (pp. 581-585). Salerno, Italy: IEEE. DOI: 10.1109/INCoS.2014.113
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[5] Bibri, S. E., & Krogstie, J. (2017). On the social shaping dimensions of smart sustainable cities: A study in science, technology, and society. Sustainable cities and society, 29, 219-246. https://doi.org/10.1016/j.scs.2016.11.004
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[6] Albreem, M. A., El-Saleh, A. A., Isa, M., Salah, W., Jusoh, M., Azizan, M. M., & Ali, A. (2017, November). Green internet of things (IoT): An overview. 2017 IEEE 4th international conference on smart instrumentation, measurement and application (ICSIMA) (pp. 1-6). Putrajaya, Malaysia, IEEE. DOI: 10.1109/ICSIMA.2017.8312021
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[7] Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97-114.
7
[8] Fox, G. C., Kamburugamuve, S., & Hartman, R. D. (2012, May). Architecture and measured characteristics of a cloud based internet of things. 2012 international conference on collaboration technologies and systems (CTS) (pp. 6-12). IEEE. DOI: 10.1109/CTS.2012.6261020
8
[9] Xu, W., Zhang, Z., Wang, H., Yi, Y., & Zhang, Y. (2020). Optimization of monitoring network system for eco safety on internet of things platform and environmental food supply chain. Computer communications, 151, 320-330. https://doi.org/10.1016/j.comcom.2019.12.033
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[10] Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: vision, applications and research challenges. Ad hoc networks, 10(7), 1497-1516. https://doi.org/10.1016/j.adhoc.2012.02.016
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[11] Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply chain management: an international journal, 24(1), 39-61. https://doi.org/10.1108/SCM-03-2018-0142
11
[12] Addo-Tenkorang, R., Gwangwava, N., Ogunmuyiwa, E. N., & Ude, A. U. (2019). Advanced animal track-&-trace supply-chain conceptual framework: an internet of things approach. Procedia manufacturing, 30, 56-63. https://doi.org/10.1016/j.promfg.2019.02.009
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[15] Pang, Z., Chen, Q., Tian, J., Zheng, L., & Dubrova, E. (2013, January). Ecosystem analysis in the design of open platform-based in-home healthcare terminals towards the internet-of-things. 2013 15th international conference on advanced communications technology (ICACT) (pp. 529-534). PyeongChang, Korea (South): IEEE.
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[16] Adhikary, T., Jana, A. D., Chakrabarty, A., & Jana, S. K. (2019, January). The internet of things (iot) augmentation in healthcare: An application analytics. International conference on intelligent computing and communication technologies (pp. 576-583). Singapore: Springer. https://doi.org/10.1007/978-981-13-8461-5_66
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[17] Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: a survey. Computer networks, 54(15), 2688-2710. https://doi.org/10.1016/j.comnet.2010.05.003
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[18] Yang, Y., Zheng, X., Guo, W., Liu, X., & Chang, V. (2019). Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system. Information sciences, 479, 567-592. https://doi.org/10.1016/j.ins.2018.02.005
18
[19] Pang, Z., Chen, Q., Han, W., & Zheng, L. (2015). Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion. Information systems frontiers, 17(2), 289-319. https://doi.org/10.1007/s10796-012-9374-9
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[20] Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International journal of production research, 57(15-16), 4719-4742. https://doi.org/10.1080/00207543.2017.1402140
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[21] Qiuping, W., Shunbing, Z., & Chunquan, D. (2011). Study on key technologies of Internet of Things perceiving mine. Procedia engineering, 26, 2326-2333. https://doi.org/10.1016/j.proeng.2011.11.2442
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[22] Wu, Y., Chen, M., Wang, K., & Fu, G. (2019). A dynamic information platform for underground coal mine safety based on internet of things. Safety science, 113, 9-18. https://doi.org/10.1016/j.ssci.2018.11.003
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[23] Karakostas, B. (2013). A DNS architecture for the internet of things: A case study in transport logistics. Procedia computer science, 19, 594-601. https://doi.org/10.1016/j.procs.2013.06.079
23
[24] Alam, S., Siddiqui, S. T., Ahmad, A., Ahmad, R., & Shuaib, M. (2020). Internet of Things (IoT) enabling technologies, requirements, and security challenges. In Advances in data and information sciences (pp. 119-126). Singapore: Springer. https://doi.org/10.1007/978-981-15-0694-9_12
24
[25] Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent edge computing for IoT-based energy management in smart cities. IEEE network, 33(2), 111-117. DOI: 10.1109/MNET.2019.1800254
25
[26] Qian, L. P., Wu, Y., Ji, B., Huang, L., & Tsang, D. H. (2019). HybridIoT: Integration of hierarchical multiple access and computation offloading for IoT-based smart cities. IEEE network, 33(2), 6-13. DOI: 10.1109/MNET.2019.1800149
26
[27] Jara, A. J., Bocchi, Y., & Genoud, D. (2014, September). Social internet of things: the potential of the internet of things for defining human behaviours. 2014 international conference on intelligent networking and collaborative systems (pp. 581-585). IEEE. DOI: 10.1109/INCoS.2014.113
27
[28] Said, O., Al-Makhadmeh, Z., & Tolba, A. (2020). EMS: an energy management scheme for green IoT environments. IEEE access, 8, 44983-44998. DOI: 10.1109/ACCESS.2020.2976641
28
[29] Shaikh, F. K., Zeadally, S., & Exposito, E. (2017). Enabling technologies for green internet of things. IEEE systems journal, 11(2), 983-994. DOI: 10.1109/JSYST.2015.2415194
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[30] Xu, X., Gu, L., Wang, J., Xing, G., & Cheung, S. C. (2011). Read more with less: an adaptive approach to energy-efficient RFID systems. IEEE journal on selected areas in communications, 29(8), 1684-1697. DOI: 10.1109/JSAC.2011.110917
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[32] Solanki, A., & Nayyar, A. (2019). Green internet of things (G-IoT): ICT technologies, principles, applications, projects, and challenges. In Handbook of research on big data and the IoT (pp. 379-405). IGI Global. DOI: 10.4018/978-1-5225-7432-3.ch021
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[33] Yan, Z., Yu, X., & Ding, W. (2017). Context-aware verifiable cloud computing. IEEE access, 5, 2211-2227.
33
[34] Nozari, H., Najafi, E., Fallah, M., & Hosseinzadeh Lotfi, F. (2019). Quantitative analysis of key performance indicators of green supply chain in FMCG industries using non-linear fuzzy method. Mathematics, 7(11), 1020. https://doi.org/10.3390/math7111020
34
[35] Topgul, M. H., Kilic, H. S., & Tuzkaya, G. (2019, July). Supply chain greenness assessment based on intuitionistic fuzzy approaches. International conference on intelligent and fuzzy systems (pp. 472-480). Cham: Springer. https://doi.org/10.1007/978-3-030-23756-1_59
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[36] Nielsen, I. E., Majumder, S., & Saha, S. (2019). Exploring the intervention of intermediary in a green supply chain. Journal of cleaner production, 233, 1525-1544. https://doi.org/10.1016/j.jclepro.2019.06.071
36
[37] Nozari, H., & Szmelter, A. (Eds.). (2018). Global supply chains in the pharmaceutical industry. IGI Global. DOI: 10.4018/978-1-5225-5921-4
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[38] Cousins, P. D., Lawson, B., Petersen, K. J., & Fugate, B. (2019). Investigating green supply chain management practices and performance. International journal of operations and production management, 39(5), 767-786. https://doi.org/10.1108/IJOPM-11-2018-0676
38
[39] Petljak, K., Zulauf, K., Štulec, I., Seuring, S., & Wagner, R. (2018). Green supply chain management in food retailing: survey-based evidence in Croatia. Supply chain management: an international journal, 23(1), 1-15. https://doi.org/10.1108/SCM-04-2017-0133
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[40] Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future generation computer systems, 86, 614-628. https://doi.org/10.1016/j.future.2018.04.051
40
[41] Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future generation computer systems, 86, 614-628. https://doi.org/10.1016/j.future.2018.04.051
41
[42] Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers and industrial engineering, 127, 925-953. https://doi.org/10.1016/j.cie.2018.11.030
42
[43] Nord, J. H., Koohang, A., & Paliszkiewicz, J. (2019). The internet of things: review and theoretical framework. Expert systems with applications, 133, 97-108. https://doi.org/10.1016/j.eswa.2019.05.014
43
[44] Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012, December). Future internet: the internet of things architecture, possible applications and key challenges. 2012 10th international conference on frontiers of information technology (pp. 257-260). IEEE. DOI: 10.1109/FIT.2012.53
44
ORIGINAL_ARTICLE
Customers' satisfaction with the level of service in Murtala Muhammad International Airport (MMIA), Lagos, Nigeria
The aim of this study is to examine customers’ satisfaction with the level of service in Murtala Muhammed International Airport (MMIA), Lagos, Nigeria; so as to identify the service attributes requiring managerial attention in the airport. The study evaluated twenty eight service attributes in order to assess passengers’ expectations and satisfaction. Survey was conducted and four hundred copies of questionnaire were administered to the study population. The data was analyzed using descriptive statistics, GAP analysis and t-test statistics. The results revealed that the twenty eight service attributes showed a significant difference (significant level ≤ 0.05) between male and female respondents’ perceptions. Although, the expectations of these service attributes were high yet their satisfaction level was low. This suggests that customers’ satisfaction with MMIA, Lagos decreased. Thus, the service attributes that are of high expectation to passengers using the airport but performing poorly with low satisfaction level include “Speed of bags delivery service”, “Flight are screens”, “Comfort of waiting” and “Phone/Internet/IT facilities.” The aforementioned service attributes are those that require managerial attention in the airport. Hence, major improvement is needed here so as to enhance customers’ satisfaction.
https://www.riejournal.com/article_122754_47d7702eb0144b90c112fa8a8a98f0d5.pdf
2021-03-01
35
45
10.22105/riej.2021.263588.1174
Customer’ s satisfaction
Level of Service
Gap Analysis
Murtala Muhammed international airport (MMIA)
Lagos
U.
Chike
chikegodwin1@gmail.com
1
Department of Logistics and Transport Technology, Federal University of Technology, Akure, Nigeria.
LEAD_AUTHOR
M.
Stephens
msstephens@futa.edu.ng
2
Department of Logistics and Transport Technology, Federal University of Technology, Akure, Nigeria.
AUTHOR
[1] Graham, A., Papatheodorou, A., & Forsyth, P. (Eds.). (2008). Aviation and tourism: implications for leisure travel. Ashgate publishing, Ltd.
1
[2] Karemera, D., Koo, W., Smalls, G., & Whiteside, L. (2015). Trade creation and diversion effects and exchange rate volatility in the global meat trade. Journal of economic integration, 30(2) 240-268. https://www.jstor.org/stable/43386619
2
[3] Doganis, R. (2005). The airport business. Routledge.
3
[4] Fodness, D., & Murray, B. (2007). Passengers' expectations of airport service quality. Journal of services marketing, 21(7), 492-506. https://doi.org/10.1108/08876040710824852
4
[5] Lumsdon, L. M., & Page, S. J. (Eds.). (2007). Tourism and transport. Routledge.
5
[6] Lian, J. I., & Denstadli, J. M. (2010). Booming leisure air travel to Norway–the role of airline competition. Scandinavian journal of hospitality and tourism, 10(1), 1-15. https://doi.org/10.1080/15022250.2010.484215
6
[7] UNWTO (2015). Tourism Highlights. Retrieved May 20, 2020, from http://mkt.unwto.org/publication/unwtotourism-highlights-2015-edition
7
[8] Lewis, B. R. (1993). Service quality measurement. Marketing intelligence and planning, 11(4), 4-12. https://doi.org/10.1108/02634509310044199
8
[9] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of marketing, 49(4), 41-50.
9
[10] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Alternative scales for measuring service quality: a comparative assessment based on psychometric and diagnostic criteria. Journal of retailing, 70(3), 201-230. https://doi.org/10.1016/0022-4359(94)90033-7
10
[11] Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2018). Services marketing: Integrating customer focus across the firm. McGraw-Hill Education.
11
[12] Bebko, C. P. (2000). Service intangibility and its impact on consumer expectations of service quality. Journal of services marketing, 14(1), 9-26. https://doi.org/10.1108/08876040010309185
12
[13] Douglas, L., & Connor, R. (2003). Attitudes to service quality–the expectation gap. Nutrition and food science, 33(4), 165-172. https://doi.org/10.1108/00346650310488516
13
[14] Walker, J., & Baker, J. (2000). An exploratory study of a multi‐expectation framework for services. Journal of services marketing, 14(5), 411-431. https://doi.org/10.1108/08876040010340946
14
[15] Maister, D. H. (2002). Marketing professional services: forward-thinking strategies for boosting your business, your image, and your profits. Consulting to management, 13(3), 57.
15
[16] Yang, C. C. (2003). Establishment and applications of the integrated model of service quality measurement. Managing service quality: an international journal, 13(4), 310-324. https://doi.org/10.1108/09604520310484725
16
[17] Ladhari, R. (2009). A review of twenty years of servqual research. International journal of quality and service sciences, 1(2), 172-198. https://doi.org/10.1108/17566690910971445
17
[18] Asubonteng, P., McCleary, K. J., & Swan, J. E. (1996). Servqual revisited: a critical review of service quality. Journal of services marketing, 10(6), 62-81. https://doi.org/10.1108/08876049610148602
18
[19] Widarsyah, R. (2013). The impact of airport service quality dimension on overall airport experience and impression (Master's Thesis, Department of Hotel Administration, University of Nevada, Las Vegas). Retrieved from https://digitalscholarship.unlv.edu/thesesdissertations/1906/
19
[20] Adeniran, A., & Fadare, S. O. (2018). Relationship between passengers’ satisfaction and service quality in murtala muhammed international airport, Lagos, Nigeria. International journal of research in industrial engineering, 7(3), 349-369.
20
[21] Udoka, C. G. (2020). The impact of passengers’ traffic on exchange rate and economic growth in nigerian aviation industry. International journal of research in industrial engineering. 9(4), 364-378. DOI: 10.22105/riej.2020.257832.1149
21
[22] Spasojevic, B., Lohmann, G., & Scott, N. (2018). Air transport and tourism–a systematic literature review (2000–2014). Current issues in tourism, 21(9), 975-997. https://doi.org/10.1080/13683500.2017.1334762
22
[23] Jagoda, K., & Balasuriya, V. (2012). Passengers'perceptions of airport service quality: an exploratory study. Citeseerx. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.643.6125
23
[24] Bæringsdóttir, H. B. (2010). Airport service quality, satisfaction and loyalty membership-the case of keflavik and landvetter airports. rapport nr.: Master degree project 2009: 66. http://hdl.handle.net/2077/22450
24
[25] Sakti, R. D. (2010). Service science perspective on customer satisfaction for improving airport services: case study: adisutjipto airport and goteborg airport (Unspecified Thesis). Retrieved from https://repository.ugm.ac.id/id/eprint/87927
25
[26] Lubbe, B., Douglas, A., & Zambellis, J. (2011). An application of the airport service quality model in South Africa. Journal of air transport management, 17(4), 224-227. https://doi.org/10.1016/j.jairtraman.2010.08.001
26
[27] Mudassar, K., Talib, S., Cheema, S., & Raza, M. S. (2013). The impact of service quality on customer satisfaction and the moderating role of word-of-mouth. African journal of business management, 7(18), 1751-1756.
27
[28] Bogicevic, V., Yang, W., Bilgihan, A. & Bujisic, M. (2013). Airport service quality drivers of passenger satisfaction. Tourism review, 68(4), 3-18. https://doi.org/10.1108/TR-09-2013-0047
28
[29] Ching, M. K. (2014, June). Passengers' perception on airport service and quality satisfaction. Proceedings of international academic conferences (No. 0201722). Vienna: International institute of social and economic sciences.
29
[30] Yang, J. S., Park, J. W., & Choi, Y. J. (2015). Passengers expectations of airport service quality: a case study of jeju international airport. International journal of business and social research, 5(7), 30-37.
30
[31] Hoang, T., Dang, M., Nguyen, T., & Kim, H. (2016). Factors affecting the service quality standards at international airports when vietnam integrates tpp: a case study at tan- son nhat airport, Ho Chi Minh City,Vietnam. British journal of marketing studies,4(1), 43-52.
31
[32] Shamaoun, M. O. M. (2017). Airport service quality and passengers satisfaction (Doctoral dissertation, Sudan University of Science and Technology).
32
ORIGINAL_ARTICLE
Applying flexible job shop scheduling in patients management to optimize processing time in hospitals.
The continuous growth of the population causes an increased demand for our healthcare services. Insufficient hospitals face challenges to serve the patient within a preferable duration. Long lines in front of counters increase the processing time of a patient. From the entry to the completion, plenty of time waste just for unscheduled hospital management system. Job shop scheduling is an optimization process in which jobs are assigned with maintain a particular sequence. In this paper, we proposed flexible job shop scheduling to solve this type of problem by considering patients as job and test counter as machine for the optimization of the processing time and increase the efficiency of a hospital or a clinic. Genetic Algorithm was used to analyze the processing time for multiple counter of a hospital for a stable and effective scheduling. The results showed that an optimized makespan was generated and patients could fulfill their needs much quickly after applying flexible job shop scheduling.
https://www.riejournal.com/article_126002_c6ddcec75b5398fcfa6f2e06519f76de.pdf
2021-03-01
46
55
10.22105/riej.2021.260145.1158
scheduling
make-span
generic algorithm
N.
Sarfaraj
sarfaraj.nagib@gmail.com
1
Department of Industrial and Production Engineering, Mechanical Faculty, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
LEAD_AUTHOR
Md. L.
Lingkon
limonurrahman16@gmail.com
2
Department of Industrial and Production Engineering, Mechanical Faculty, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
AUTHOR
N.
Zahan
nusrathzahantasnim@gmail.com
3
Department of Industrial and Production Engineering, Mechanical Faculty, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
AUTHOR
[1] Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of operations research, 1(2), 117-129. https://doi.org/10.1287/moor.1.2.117
1
[2] Wang, L., Zhou, G., Xu, Y., & Liu, M. (2012). An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. The international journal of advanced manufacturing technology, 60(9), 1111-1123. https://doi.org/10.1007/s00170-011-3665-z
2
[3] Saraswati, T. G., & Saputri, M. E. (2019, May). Queuing management and evaluation of standard operating procedures for hospital mental health polyclinics. 1st international conference on economics, business, entrepreneurship, and finance (ICEBEF 2018) (pp. 779-782). Atlantis Press. https://doi.org/10.2991/icebef-18.2019.163
3
[4] Teekeng, W., & Thammano, A. (2011). A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems. Procedia computer science, 6, 69-75. https://doi.org/10.1016/j.procs.2011.08.015
4
[5] Gao, J., Sun, L., & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and operations research, 35(9), 2892-2907. https://doi.org/10.1016/j.cor.2007.01.001
5
[6] Li, J. Q., Pan, Q. K., & Gao, K. Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. The international journal of advanced manufacturing technology, 55(9), 1159-1169. https://doi.org/10.1007/s00170-010-3140-2
6
[7] Sajadi, S. M., Alizadeh, A., Zandieh, M., & Tavan, F. (2019). Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach. International journal of mathematics in operational research, 14(2), 268-289. https://doi.org/10.1504/IJMOR.2019.097759
7
[8] Kumar, G., & Bisoniya, T. S. (2005). Flexible job shop scheduling operation using genetic algorithm. Journal of innovations in engineering and technology, 5, 1-5.
8
[9] Xie, J., Gao, L., Peng, K., Li, X., & Li, H. (2019). Review on flexible job shop scheduling. IET collaborative intelligent manufacturing, 1(3), 67-77.DOI: 10.1049/iet-cim.2018.0009
9
[10] Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & operations research, 35(10), 3202-3212.
10
[11] Pinedo, M. (2012). Scheduling (Vol. 29). New York: Springer.
11
[12] Marynissen, J., & Demeulemeester, E. (2016). Literature review on integrated hospital scheduling problems. KU Leuven, faculty of economics and business, KBI_1627. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2873413
12
[13] Mahanta, L. B. (2019). Estimation of the waiting time of patients in a hospital with simple Markovian model using order statistics. Hacettepe journal of mathematics and statistics, 48(1), 274-289.
13
[14] Bekal, M., Manikandan, A., Tewari, A., & GB, P. (2019). Parallel patient treatment algorithm and it’s application in hospital queuing recommendation. International journal of advance research, ideas and innovations in technology, 5(3), 578-582.
14
[15] Chawasemerwa, T., Taifa, I. W., & Hartmann, D. (2018). Development of a doctor scheduling system: a constraint satisfaction and penalty minimisation scheduling model. International journal of research in industrial engineering, 7(4), 396-422.
15
Barzegar, B., Motameni, H., & Bozorgi, H. (2012). Solving flexible job-shop scheduling problem using gravitational search algorithm and colored Petri net. Journal of applied mathematics, 2012. https://doi.org/10.1155/2012/651310
16
ORIGINAL_ARTICLE
Presentation and implementation multi-objective mathematical models to balance the assembly line
The use of assembly lines is one of the important approaches in mass production of industrial products. Imbalance of assembly lines increases cycle time and idle times, resulting in reduced production rates, line efficiency, and increased system costs, which ultimately lead to low productivity. A hybrid model assembly line is a type of production line on which various models of products are assembled. These assembly lines are increasingly accepted in the industry in order to overcome the diversity of customer demand. The hybrid model assembly line is able to respond quickly to sudden changes in demand for different models of a product without maintaining a large inventory.The purpose of this paper is to present a multi-objective integer linear mathematical programming model for balancing assembly lines, which is solved using the general criteria method. The three objective functions considered in this model are: (1) Minimizing cycle time (2) Minimize the idle time of each station and (3) increase the efficiency of the assembly line. In order to investigate the model, Iran-Shargh Neishabour Company has been considered as a case study. After implementing the proposed model of the paper, the results show the optimal performance of the proposed model and the studied parameters in line balancing have been significantly improved.
https://www.riejournal.com/article_127068_a8cc169125d1e17fc9595b4e1a09afe8.pdf
2021-03-01
56
66
10.22105/riej.2021.269298.1184
assembly line balancing
Multi-product assembly line
Multi-Objective Optimization
Linear Programming
Cycle Time
E.
Shadkam
ie.el.shadkam@gmail.com
1
Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad, Iran.
LEAD_AUTHOR
F.
Ghavidel
negar.ghavidel5@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad, Iran.
AUTHOR
[1] Fisel, J., Exner, Y., Stricker, N., & Lanza, G. (2019). Changeability and flexibility of assembly line balancing as a multi-objective optimization problem. Journal of manufacturing systems, 53, 150-158. https://doi.org/10.1016/j.jmsy.2019.09.012
1
[2] Zhong, Y., Deng, Z., & Xu, K. (2019). An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems. Computers and industrial engineering, 138, 106121. https://doi.org/10.1016/j.cie.2019.106121
2
[3] Sun, B. Q., Wang, L., & Peng, Z. P. (2020). Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing. Computers and Industrial engineering, 146, 106604. https://doi.org/10.1016/j.cie.2020.106604
3
[4] Liu, R., Liu, M., Chu, F., Zheng, F., & Chu, C. (2021). Eco-friendly multi-skilled worker assignment and assembly line balancing problem. Computers and industrial engineering, 151, 106944. https://doi.org/10.1016/j.cie.2020.106944
4
[5] Çil, Z. A., Li, Z., Mete, S., & Özceylan, E. (2020). Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human–robot collaboration. Applied soft computing, 93, 106394. https://doi.org/10.1016/j.asoc.2020.106394
5
[6] Çil, Z. A., & Kizilay, D. (2020). Constraint programming model for multi-manned assembly line balancing problem. Computers and operations research, 124, 105069. https://doi.org/10.1016/j.cor.2020.105069
6
[7] Eghtesadifard, M., Khalifeh, M., & Khorram, M. (2020). A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017. Computers and industrial engineering, 139, 106182. https://doi.org/10.1016/j.cie.2019.106182
7
[8] Jackson, J. R. (1956). A computing procedure for a line balancing problem. Management science, 2(3), 261-271. https://doi.org/10.1287/mnsc.2.3.261
8
[9] Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers and industrial engineering, 87, 74-80. https://doi.org/10.1016/j.cie.2015.04.017
9
[10] Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Computers and industrial engineering, 62(1), 39-55. https://doi.org/10.1016/j.cie.2011.08.015
10
[11] Liu, B., & Liu, B. (2009). Theory and practice of uncertain programming (Vol. 239). Berlin: Springer.
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[12] Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied soft computing, 40, 221-235. https://doi.org/10.1016/j.asoc.2015.11.025
12
[13] Ponnambalam, S. G., Aravindan, P., & Naidu, G. M. (2000). A multi-objective genetic algorithm for solving assembly line balancing problem. The international journal of advanced manufacturing technology, 16(5), 341-352. https://doi.org/10.1007/s001700050166
13
[14] Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932. https://doi.org/10.1287/mnsc.32.8.909
14
[15] Xiaofeng, H., Erfei, W., Jinsong, B., & Ye, J. (2010). A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. European journal of operational research, 206(3), 703-707. https://doi.org/10.1016/j.ejor.2010.02.034
15
[16] Özbakır, L., & Tapkan, P. (2011). Bee colony intelligence in zone constrained two-sided assembly line balancing problem. Expert systems with applications, 38(9), 11947-11957. https://doi.org/10.1016/j.eswa.2011.03.089
16
[17] Chen, R. S., Lu, K. Y., & Yu, S. C. (2002). A hybrid genetic algorithm approach on multi-objective of assembly planning problem. Engineering applications of artificial intelligence, 15(5), 447-457. https://doi.org/10.1016/S0952-1976(02)00073-8
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[18] Mansouri, S. A. (2005). A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. European journal of operational research, 167(3), 696-716. https://doi.org/10.1016/j.ejor.2004.07.016
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[19] Nourmohammadi, A., & Zandieh, M. (2011). Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS. International journal of production research, 49(10), 2833-2855. https://doi.org/10.1080/00207540903473367
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[20] Delice, Y., Aydoğan, E. K., Söylemez, İ., & Özcan, U. (2018). An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times. Sādhanā, 43(12), 1-15. https://doi.org/10.1007/s12046-018-0969-9
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[21] Ogan, D., & Azizoglu, M. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. Journal of manufacturing systems, 36, 46-54. https://doi.org/10.1016/j.jmsy.2015.02.007
21
[22] Graves, S. C., & Whitney, D. E. (1979, December). A mathematical programming procedure for equipment selection and system evaluation in programmable assembly. 1979 18th IEEE conference on decision and control including the symposium on adaptive processes (pp. 531-536). IEEE. DOI: 10.1109/CDC.1979.270236
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[24] Ahmed, T., Sakib, N., Hridoy, R. M., & Shams, A. T. (2020). Application of line balancing heuristics for achieving an effective layout: a case study. International journal of research in industrial engineering, 9(2), 114-129.
24
[25] Zhang, Z., Tang, Q., & Chica, M. (2020). Multi-manned assembly line balancing with time and space constraints: A MILP model and memetic ant colony system. Computers and industrial engineering, 150, 106862. https://doi.org/10.1016/j.cie.2020.106862
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[26] Zhang, B., Xu, L., & Zhang, J. (2021). Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach. Applied soft computing, 98, 106739. https://doi.org/10.1016/j.asoc.2020.106739
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[27] Li, Z., Kucukkoc, I., & Tang, Q. (2021). Enhanced branch-bound-remember and iterative beam search algorithms for type II assembly line balancing problem. Computers and operations research, 131, 105235. https://doi.org/10.1016/j.cor.2021.105235
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[28] Meng, K., Tang, Q., Zhang, Z., & Yu, C. (2021). Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm. Engineering applications of artificial intelligence, 100, 104183. https://doi.org/10.1016/j.engappai.2021.104183
28
[29] Shafi Salimi, P., & Edalatpanah, S. A. (2020). Supplier selection using fuzzy AHP method and D-Numbers. Journal of fuzzy extension and applications, 1(1), 1-14.
29
[30] Khalili, N., Shahnazari Shahrezaei, P., & Abri, A. G. (2020). A multi-objective optimization approach for a nurse scheduling problem considering the fatigue factor (case study: Labbafinejad Hospital). Journal of applied research on industrial engineering, 7(4), 396-423.
30
[31] Mohammad, P., & Kazemipoor, H. (2020). An integrated multi-objective mathematical model to select suppliers in green supply chains. International journal of research in industrial engineering, 9(3), 216-234.
31
[32] El-Shorbagy, M. A., Mousa, A. A. A., ALoraby, H., & Abo-Kila, T. (2020). Evolutionary algorithm for multi-objective multi-index transportation problem under fuzziness. Journal of applied research on industrial engineering, 7(1), 36-56.
32
Lascelles, B. G., Taylor, P. R., Miller, M. G. R., Dias, M. P., Oppel, S., Torres, L., ... & Small, C. (2016). Applying global criteria to tracking data to define important areas for marine conservation. Diversity and distributions, 22(4), 422-431. https://doi.org/10.1111/ddi.12411
33
ORIGINAL_ARTICLE
Data envelopment analysis for estimate efficiency and ranking operating rooms: a case study
Data Envelopment Analysis (DEA) is one of the non-parametric methods for evaluating each unit's efficiency. Limited resources in the healthcare system are the main reason for measuring the efficiency of hospitals. Because Operating Rooms (OR) are the most vital part of any hospital, we determine the factors affecting operating rooms' efficiency and evaluate the performance and ranking of operating rooms in 10 of Tehran's largest hospitals. This model's inputs include accuracy in scheduling surgeries, average turnover time, number of successful surgeries and live patients, number of canceled surgeries, number of surgical errors, and number of emergency surgery. Also, outputs consist of the number of operating rooms and equipment, the average number of beds, the number of employees, and the patient satisfaction rate. First, we determine the weight of inputs and outputs by Group Analytic Hierarchy Process (GAHP) with considering experts' ideas in 10 hospitals; then, we utilize three types of DEA model which are input-oriented CCR (CCR-I), output-oriented CCR (CCR-O), input-output oriented CCR (CCR_IO) and AP models to estimate the efficiency of ORs and rank them.
https://www.riejournal.com/article_122559_d1e4bebe5fa453a9942f6bd42784e11c.pdf
2021-03-20
67
86
10.22105/riej.2021.247705.1143
Performance Evaluation
Data Envelopment Analysis
Ranking operating rooms
Sensitivity analysis
Sh.
Ghasemi
sh.ghasemi@ut.ac.ir
1
School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
A.
Aghsami
a.aghsami@ut.ac.ir
2
School of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran.
AUTHOR
M.
Rabbani
mrabani@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
[1] Roland, B., Di Martinelly, C., Riane, F., & Pochet, Y. (2010). Scheduling an operating theatre under human resource constraints. Computers and industrial engineering, 58(2), 212-220.
1
[2] Hans, E. W., & Nieberg, T. (2007). Operating room manager game. INFORMS transactions on education, 8(1), 25-36.
2
[3] Rezaee, M. J., & Karimdadi, A. (2015). Do geographical locations affect in hospitals performance? A multi-group data envelopment analysis. Journal of medical systems, 39(9). https://doi.org/10.1007/s10916-015-0278-3
3
[4] Chowdhury, H., & Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega, 63, 111-122.
4
[5] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
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[6] Firsova, A. A., & Chernyshova, G. Y. (2019). Mathematical models for evaluation of the higher education system functions with DEA approach. Izv. Saratov Univ. Math. Mech.Inform, 19(3), 351-362. DOI: https://doi.org/10.18500/1816-9791-2019-19-3-351-362
6
[7] Taeb, Z., Hosseinzadeh Lotfi, F., & Abbasbandy, S. (2017). Determine the efficiency of time depended units by using data envelopment analysis. International journal of research in industrial engineering, 6(3), 193-201.
7
[8] Sebt, M. V., Juybari, M. N., & Soleymanfar, V. R. (2018). Investment projects ranking with DEA method considering feasibility study results. International journal of research in industrial engineering, 7(3), 320-335.
8
[9] O'neill, L. (1998). Multifactor efficiency in data envelopment analysis with an application to urban hospitals. Health care management science, 1(1), 19-27.
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[10] Huang, L. J., & Hu, T. Z. (2006). Study of agricultural production efficiency in China's Western region based on dea method [J]. Research of agricultural modernization, 6. Retrieved from https://en.cnki.com.cn/Article_en/CJFDTotal-NXDH200606005.htm
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[11] Chang, Y. T., Zhang, N., Danao, D., & Zhang, N. (2013). Environmental efficiency analysis of transportation system in China: A non-radial DEA approach. Energy policy, 58, 277-283.
11
[12] Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of applied economics, 20(1), 169-192.
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[13] 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.
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[14] Butler, T. W., & Li, L. (2005). The utility of returns to scale in DEA programming: an analysis of Michigan rural hospitals. European journal of operational research, 161(2), 469-477.
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[15] Burgess Jr, J. F., & Wilson, P. W. (1996). Hospital ownership and technical inefficiency. Management science, 42(1), 110-123.
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[16] Ozcan, Y. A., Merwin, E., Lee, K., & Morrissey, J. P. (2005). Benchmarking using DEA: the case of mental health organizations. In Operations research and health care (pp. 169-189). Boston, MA: Springer.
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[17] Min, A., & Scott, L. D. (2016). Evaluating nursing hours per patient day as a nurse staffing measure. Journal of nursing management, 24(4), 439-448.
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[18] Ketabi, S. (2011). Efficiency measurement of cardiac care units of Isfahan hospitals in Iran. Journal of medical systems, 35(2), 143-150.
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[19] Hatefi, S. M., & Haeri, A. (2019). Evaluating hospital performance using an integrated balanced scorecard and fuzzy data envelopment analysis. Journal of health management and informatics, 6(2), 66-76.
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[20] Li, Y., Lei, X., & Morton, A. (2019). Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals. Health care management science, 22(2), 215-228.
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[21] Khushalani, J., & Ozcan, Y. A. (2017). Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA). Socio-economic planning sciences, 60, 15-23.
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[22] Zare, H., Tavana, M., Mardani, A., Masoudian, S., & Saraji, M. K. (2019). A hybrid data envelopment analysis and game theory model for performance measurement in healthcare. Health care management science, 22(3), 475-488.
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[23] Omrani, H., Shafaat, K., & Emrouznejad, A. (2018). An integrated fuzzy clustering cooperative game data envelopment analysis model with application in hospital efficiency. Expert systems with applications, 114, 615-628.
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