The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development
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Abstract
The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
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[1]
A. Hendrawan, “The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development”, INFOTEL, vol. 16, no. 4, pp. 793–818, Dec. 2024.
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Informatics
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[3] H.-M. Lyu and Z.-Y. Yin, “An improved MCDM combined with GIS for risk assessment of multi-hazards in Hong Kong,” Sustain Cities Soc, vol. 91, p. 104427, Apr. 2023, doi: 10.1016/j.scs.2023.104427.
[4] G. Villacreses, J. Martínez-Gómez, D. Jijón, and M. Cordovez, “Geolocation of photovoltaic farms using Geographic Information Systems (GIS) with Multiple-criteria decision-making (MCDM) methods: Case of the Ecuadorian energy regulation,” Energy Reports, vol. 8, pp. 3526–3548, 2022, doi: https://doi.org/10.1016/j.egyr.2022.02.152.
[5] A. Siva Bhaskar and A. Khan, “Comparative analysis of hybrid MCDM methods in material selection for dental applications,” Expert Syst Appl, vol. 209, p. 118268, Dec. 2022, doi: 10.1016/j.eswa.2022.118268.
[6] A. Sotoudeh-Anvari, “The applications of MCDM methods in COVID-19 pandemic: A state of the art review,” Appl Soft Comput, vol. 126, p. 109238, 2022, doi: https://doi.org/10.1016/j.asoc.2022.109238.
[7] A. C. Ram Kumar, N. Mohammed Raffic, K. Ganesh Babu, and S. Selvakumar, “Static structural analysis of spur gear using ANSYS 15.0 and material selection by COPRAS, MOORA techniques,” Mater Today Proc, vol. 47, pp. 25–36, 2021, doi: 10.1016/j.matpr.2021.03.485.
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[13] A. Budak, İ. Kaya, A. Karaşan, and M. Erdoğan, “Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics,” Appl Soft Comput, vol. 92, p. 106322, 2020, doi: https://doi.org/10.1016/j.asoc.2020.106322.
[14] S. M. Mousavi, B. Ataie-Ashtiani, and S. M. Hosseini, “Comparison of statistical and MCDM approaches for flood susceptibility mapping in northern Iran,” J Hydrol (Amst), vol. 612, p. 128072, Sep. 2022, doi: 10.1016/j.jhydrol.2022.128072.
[15] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J Informetr, vol. 11, no. 4, pp. 959–975, 2017, doi: https://doi.org/10.1016/j.joi.2017.08.007.
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[19] M. Ozcalici and M. Bumin, “An integrated multi-criteria decision making model with Self-Organizing Maps for the assessment of the performance of publicly traded banks in Borsa Istanbul,” Appl Soft Comput, vol. 90, p. 106166, May 2020, doi: 10.1016/j.asoc.2020.106166.
[20] B. Cicciù, F. Schramm, and V. B. Schramm, “Multi-criteria decision making/aid methods for assessing agricultural sustainability: A literature review,” Environ Sci Policy, vol. 138, pp. 85–96, 2022, doi: https://doi.org/10.1016/j.envsci.2022.09.020.
[21] B. Masoomi, I. G. Sahebi, M. Fathi, F. Yıldırım, and S. Ghorbani, “Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPAS-COPRAS approach),” Energy Strategy Reviews, vol. 40, p. 100815, Mar. 2022, doi: 10.1016/j.esr.2022.100815.
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[24] H. B. Akdeniz, S. Yalpir, and S. Inam, “Assessment of suitable shrimp farming site selection using geographical information system based Analytical Hierarchy Process in Turkey,” Ocean Coast Manag, vol. 235, p. 106468, Mar. 2023, doi: 10.1016/j.ocecoaman.2022.106468.
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