A Novel Modified ARAS Approach for Evaluating Sustainable Green Supply Chains: A Case Study in the Food Industry
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
1 - Department of Mathematics, Islamic Azad University, Semnan Branch, Semnan, Iran.
کلید واژه: Multi-Criteria Decision-Making, Modified ARAS Method, Sustainable Supply Chain, Green Supply Chain Evaluation, Food Industry,
چکیده مقاله :
[This paper introduces a novel framework for evaluating the performance of sustainable green supply chains using the Modified Additive Ratio Assessment (ARAS-M) method, an enhanced version of the traditional ARAS approach. A comprehensive literature review identified key factors impacting green supply chain sustainability, including environmental impact, energy efficiency, waste management, and resource optimization. The proposed ARAS-M method incorporates a linear programming model to optimize the weighting process, making it adaptable for future development under fuzzy logic, which allows for improved modeling of uncertainty in decision-making. The method was applied to assess six food industry companies' supply chains, with expert input to rank these companies based on identified attributes. Validation was achieved by comparing the rankings to real-world performance metrics such as environmental certifications and resource consumption reports. Results indicated that companies excelling in environmental policies, efficient energy use, and waste reduction ranked highest. This study demonstrates the effectiveness of the ARAS-M method in evaluating green supply chain performance and highlights the importance of sustainability practices in enhancing supply chain resilience and competitiveness.
چکیده انگلیسی:
منابع و مأخذ:
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[24] Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30-35. https://doi.org/10.1016/j.procir.2015.08.086
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[26] Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3), 123-141. https://doi.org/10.1016/S1644-9665(12)60141-1
[27] Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27(1), 49-65. https://doi.org/10.15388/Informatica.2016.76
[28] Ozbek, A., & Erol, E. (2017). Ranking of factoring companies in accordance with ARAS and COPRAS methods. International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(2), 105-116. https://doi.org/10.6007/IJARAFMS/v7-i2/2876
[29] Prasad, R. (2019). Selection of internal safety auditors in an Indian construction organization based on the SWARA and ARAS methods. Journal of Occupational Health and Epidemiology, 8(3), 134-140. https://doi.org/10.29252/johe.8.3.134
[30] Lee, J., Ozaki, I., Kishino, S., & Suzuki, K. (2021). Evaluation method of ARAS combining simulator experiment and computer simulation in terms of cost-benefit analysis. International Journal of Intelligent Transportation Systems Research, 19, 44-55. https://doi.org/10.1007/s13177-019-00215-z
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[1] Mousavi Janbehsarayi, S. F., Niksokhan, M. H., Hassani, M. R., & Ardestani, M. (2023). Multi-objective decision-making based on theories of cooperative game and social choice to incentivize implementation of low-impact development practices. Journal of Environmental Management, 330, 117243. https://doi.org/10.1016/j.jenvman.2023.117243
[2] Hwang, C. L., Lai, Y. J., & Liu, T. Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889-899. https://doi.org/10.1016/0305-0548(93)90109-V
[3] Soltanifar, M. (2021). An investigation of the most common multi-objective optimization methods with propositions for improvement. Decision Analytics Journal, 1, 100005. https://doi.org/10.1016/j.dajour.2021.100005
[4] Moallemi, E. A., Elsawah, S., Turan, H. H., & Ryan, M. J. (2018). Multi-objective decision making in multi-period acquisition planning under deep uncertainty. Winter Simulation Conference (WSC), 2018, 1334-1345. https://doi.org/10.1109/WSC.2018.8632316
[5] Groenia, S., van den Berg, M., Volker, L., Valcke, S., & Barros, E. (2024). Multi-objective decision-making for sustainable construction: Designing an interactive method for multi-actor project settings. Paper presented at 40th Annual ARCOM Conference 2024: Looking back to move forward, London, United Kingdom.
[6] Yalılı, M., Menlik, T., & Boran, F. E. (2024). A novel multi-objective decision-making model to determine optimum resource and capacity configuration for hybrid electricity generation systems: A comparative case study in Türkiye. Applied Energy, 376, 124338. https://doi.org/10.1016/j.apenergy.2024.124338
[7] Zhou, X., Tan, W., Sun, Y., Huang, T., & Yang, C. (2024). Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS. Expert Systems with Applications, 240, 122539. https://doi.org/10.1016/j.eswa.2023.122539
[8] Soltanifar, M. (2021). The voting linear assignment method for determining priority and weights in solving MADM problems. Journal of Applied Research on Industrial Engineering, 8(Special Issue), 1-17. https://doi.org/10.22105/jarie.2021.268606.1240
[9] Tavana, M., Soltanifar, M., & Santos-Arteaga, F. J. (2023). Analytical hierarchy process: Revolution and evolution. Annals of Operations Research, 326, 879–907. https://doi.org/10.1007/s10479-021-04432-2
[10] Soltanifar, M., & Tavana, M. (2024). A novel pairwise comparison method with linear programming for multi-attribute decision-making. EURO Journal on Decision Processes, 12, 100051. https://doi.org/10.1016/j.ejdp.2024.100051
[11] Soltanifar, M. (2024). A new interval for ranking alternatives in multi attribute decision making problems. Journal of Applied Research on Industrial Engineering, 11(1), 37-56. https://doi.org/10.22105/jarie.2022.339957.1467
[12] Soltanifar, M., Tavana, M., Santos-Arteaga, F. J., & Sharafi, H. (2023). A hybrid multi-attribute decision-making and data envelopment analysis model with heterogeneous attributes: The case of sustainable development goals. Environmental Science & Policy, 147, 89-102. https://doi.org/10.1016/j.envsci.2023.06.004
[13] Soltanifar, M., Zargar, S. M., & Aman, M. (2023). Improved WASPAS method for determining criteria priority and weights in solving MADM problems: A case study to determine leadership style in Covid-19 pandemic. Journal of Decisions and Operations Research, 8(3), 749-770. https://doi.org/10.22105/dmor.2023.345520.1616
[14] Soltanifar, M., & Zargar, S. M. (2021). Assessing and ranking cloud computing security risks based on a hybrid approach based on pairwise comparisons. Iranian Journal of Information Processing and Management, 37(1), 27-58. https://doi.org/10.52547/jipm.37.1.27
[15] El-Araby, A. (2023). The utilization of MARCOS method for different engineering applications: A comparative study. International Journal of Research in Industrial Engineering, 12(2), 155-164. https://doi.org/10.22105/riej.2023.395104.1379
[16] Kabgani, M. H. (2023). Measuring effective indicators for waste disposal in order to assess the sustainable environment: Application of fuzzy approach. International Journal of Research in Industrial Engineering, 12(3), 287-305. https://doi.org/10.22105/riej.2023.368774.1345
[17] Sheel, C. C., Rahman, S. M. A., & Bhowmick, T. (2024). A decision-making method for supplier selection in industrial manufacturing industry: A mathematical framework of integrating analytical hierarchical process and reliability risk evaluation in the field of industrial engineering sectors. International Journal of Research in Industrial Engineering. https://doi.org/10.22105/riej.2024.474342.1469
[18] Niyazi, M., & Tavakkoli-Moghaddam, R. (2014). Solving a facility location problem by three multi-criteria decision making methods. International Journal of Research in Industrial Engineering, 3(4), 41-56. https://www.riejournal.com/article_48006.html
[19] Nikjo, B., Rezaeian, J., & Javadian, N. (2015). Decision making in best player selection: An integrated approach with AHP and extended TOPSIS methods based on WeFA framework in MAGDM problems. International Journal of Research in Industrial Engineering, 4(1(4)), 1-14. https://doi.org/10.22105/riej.2017.49166
[20] Soltanifar, M. (2023). Improved Kemeny median indicator ranks accordance method. Asia-Pacific Journal of Operational Research, 40(03), 2250024. https://doi.org/10.1142/S0217595922500245
[21] Soltanifar, M., Krylovas, A., & Kosareva, N. (2023). Voting-KEMIRA median indicator ranks accordance method for determining criteria priority and weights in solving multi-attribute decision-making problems. Soft Computing, 27, 6613–6628. https://doi.org/10.1007/s00500-022-07807-0
[22] Soltanifar, M., & Santos-Arteaga, F. J. (2024). Hybrid DEA-BWM-KEMIRA approach for multiple attribute decision-making: A weighted analysis perspective. Soft Computing. https://doi.org/10.1007/s00500-024-09933-3
[23] Soltanifar, M., Tavana, M., Santos-Arteaga, F. J., & Charles, V. (2024). A new fuzzy KEMIRA method with an application to innovation park location analysis and selection. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2024.3471876
[24] Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30-35. https://doi.org/10.1016/j.procir.2015.08.086
[25] Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multi criteria decision-making. Technological and Economic Development of Economy, 16(2), 159-172. https://doi.org/10.3846/tede.2010.10
[26] Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3), 123-141. https://doi.org/10.1016/S1644-9665(12)60141-1
[27] Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27(1), 49-65. https://doi.org/10.15388/Informatica.2016.76
[28] Ozbek, A., & Erol, E. (2017). Ranking of factoring companies in accordance with ARAS and COPRAS methods. International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(2), 105-116. https://doi.org/10.6007/IJARAFMS/v7-i2/2876
[29] Prasad, R. (2019). Selection of internal safety auditors in an Indian construction organization based on the SWARA and ARAS methods. Journal of Occupational Health and Epidemiology, 8(3), 134-140. https://doi.org/10.29252/johe.8.3.134
[30] Lee, J., Ozaki, I., Kishino, S., & Suzuki, K. (2021). Evaluation method of ARAS combining simulator experiment and computer simulation in terms of cost-benefit analysis. International Journal of Intelligent Transportation Systems Research, 19, 44-55. https://doi.org/10.1007/s13177-019-00215-z
[31] Goswami, S.S., & Mitra, S. (2020). Selecting the best mobile model by applying AHP-COPRAS and AHP-ARAS decision making methodology. International Journal of Data and Network Science, 4, 27–42. https://doi.org/10.5267/j.ijdns.2019.8.004
[32] Karabašević, D.M., Maksimović, M.V., Stanujkić, D.M., Jocić, G.B., & Rajčević, D.P. (2018). Selection of software testing method by using ARAS method. Tehnika, 73(5), 724-729. https://doi.org/10.5937/tehnika1805724K
[33] Idaman, A., Amrullah, & Rolanda, V. (2024). Analysis of the Additive Ratio Assessment Method in the selection of the best production head. Jurnal Informasi Dan Teknologi, 6(2), 172-181. https://doi.org/10.60083/jidt.v6i2.546
[34] Fan, J., Han, D., & Wu, M. (2023). Picture fuzzy ARAS and VIKOR methods for multi-attribute decision problem and their application. Complex & Intelligent Systems, 9, 5345–5357. https://doi.org/10.1007/s40747-023-01007-5
[35] Heidary Dahooie, J., Zavadskas, E.K., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval-valued fuzzy ARAS method: A case study of oil and gas well drilling projects. Symmetry, 10(2), 45, 1-32. https://doi.org/10.3390/sym10020045
[36] Soltanifar, M., Zargar, S.M., & Homayounfar, M. (2022). Green supplier selection: A hybrid group voting AHP approach. Journal of Operational Research and Its Applications, 19(2), 113-132. https://doi.org/10.52547/jamlu.19.2.113
[37] Sharafi, H., Soltanifar, M., & Hosseinzadeh Lotfi, F. (2022). Selecting a green supplier utilizing the new fuzzy voting model and the fuzzy combinative distance-based assessment method. EURO Journal on Decision Processes, 10, 100010. https://doi.org/10.1016/j.ejdp.2021.100010
[38] Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making. Berlin: Springer. https://doi.org/10.1007/978-3-642-48318-9
[39] Ghram, M., & Frikha, H.M. (2018). A new procedure of criteria weight determination within the ARAS method. Multiple Criteria Decision Making, 13, 56-73. https://doi.org/10.22367/mcdm.2018.13.03
[40] Cook, W.D., & Kress, M. (1990). A data envelopment model for aggregating preference rankings. Management Science, 36(11), 1302–1310. https://doi.org/10.1287/mnsc.36.11.1302
[41] Noguchi, H., Ogawa, M., & Ishii, H. (2002). The appropriate total ranking method using DEA for multiple categorized purposes. Journal of Computational and Applied Mathematics, 146(1), 155–166. https://doi.org/10.1016/S0377-0427(02)00425-9
[42] Llamazares, B., & Pena, T. (2009). Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates. European Journal of Operational Research, 197, 714–721. https://doi.org/10.1016/j.ejor.2008.06.031
[43] Mastos, T., & Gotzamani, K. (2022). Sustainable supply chain management in the food industry: A conceptual model from a literature review and a case study. Foods, 11(15), 2295. https://doi.org/10.3390/foods11152295
[44] Zhu, Q., Geng, Y., & Sarkis, J. (2020). Sustainable waste management in supply chains: Practices and implications. International Journal of Production Economics, 228, 107652. https://doi.org/10.1016/j.ijpe.2020.107652
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