Flexible fuzzy-robust optimization method in closed-loop supply chain network problem modeling for the engine oil industry

Seyed Mohammad Shams Moosavi; Mehdi Seifbarghy; Seyed Mohammad Haji Molana

doi:10.31181/dmame622023569

Authors

Seyed Mohammad Shams Moosavi Department of industrial engineering, Islamic Azad University, Science and research branch, Tehran, Iran https://orcid.org/0000-0002-2795-5398
Mehdi Seifbarghy Department of industrial engineering, Faculty of engineering-al-Zahra university-Tehran, Iran
Seyed Mohammad Haji Molana Department of industrial engineering, Islamic Azad University, Science and research branch, Tehran, Iran

DOI:

https://doi.org/10.31181/dmame622023569

Keywords:

Closed-loop supply chain network, reliability, flexible fuzzy robust optimization, discount, metaheuristic algorithms

Abstract

This study models a closed loop supply chain network for the Iranian engine oil market. The primary goal of the created model is to summarize tactical choices like choosing the best degree of discount and allocating the best flow of products across facilities as well as strategic decisions like selecting a supplier and finding new facilities. The three aim functions of reducing overall expenses, optimizing employment rate, and limiting unrealized demand are considered. The novel flexible fuzzy robust optimization approach also controls the uncertainty parameters and the meta-heuristics algorithm for solving the model. This investigation showed that the network's overall transportation and operational expenses have risen as the rate of uncertainty and dependability has grown. MOGWO was chosen as an effective algorithm and employed in solving numerical examples of more significant size after the final examination of comparison indices between solution techniques (case study). According to the findings of a case study, the four oil businesses, Behran, Sepahan, Iranol, and Pars, were chosen as the best production hubs since they can generate 514 million liters of engine oil annually. As a consequence, building the network cost a total of 434321010 million Rials, required the employment of more than 37 thousand individuals, and left 90 million liters of fuel short.

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References

Abad, A. R. K. K.,& Pasandideh, S. H. R. (2022). Green closed-loop supply chain network design with stochastic demand: A novel accelerated benders decomposition method. Scientia Iranica, 29, 2578–2592. https://doi.org/10.24200/sci.2020.53412.3249

Ahmadi, S., & Amin, S. H. (2019). An integrated chance-constrained stochastic model for a mobile phone closed-loop supply chain network with supplier selection. Journal of Cleaner Production, 226, 988–1003. https://doi.org/10.1016/j.jclepro.2019.04.132

Alshamsi, A., & Diabat, A. (2018). Large-scale reverse supply chain network design: An accelerated Benders decomposition algorithm. Computers and Industrial Engineering, 124, 545–559. https://doi.org/10.1016/j.cie.2018.05.057

Aliahmadi, A., Ghahremani-Nahr, J., & Nozari, H. (2023). Pricing decisions in the closed-loop supply chain network, taking into account the queuing system in production centers. Expert Systems with Applications, 212, 118741. https://doi.org/10.1016/j.eswa.2022.118741

Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications, 73, 82–91. https://doi.org/10.1016/j.eswa.2016.12.024

Boronoos, M., Mousazadeh, M., & Torabi, S. A. (2021). A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green SC network design. Environment, Development and Sustainability, 23, 3368–3395.

Chen, Z. S., Zhang, X., Govindan, K., Wang, X. J., & Chin, K. S. (2021). Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach. Expert Systems with Applications, 166, 114051

Cricelli, L., Greco, M., & Grimaldi, M. (2021). An investigation on the effect of inter-organizational collaboration on reverse logistics. International Journal of Production Economics, 240,108216 https://doi.org/10.1016/j.ijpe.2021.108216

Darestani, S. A., & Hemmati, M. (2019). Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system. Computers and Industrial Engineering, 136, 277–292. https://doi.org/10.1016/j.cie.2019.07.018

Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Multi-objective stochastic closed-loop supply chain network design with social considerations. Applied Soft Computing Journal, 71, 505–525. https://doi.org/10.1016/j.asoc.2018.07.025

Fathollahi-Fard, A. M., Ahmadi, A., & Al-e-Hashem, S. M. J. M. (2020). Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management, 275, 111277 https://doi.org/10.1016/j.jenvman.2020.111277

Garg, K., Kannan, D., Diabat, A., & Jha, P. C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297–314. https://doi.org/10.1016/j.jclepro.2015.02.075

Ghahremani Nahr, J. (2020). Improvement the efficiency and efficiency of the closed loop SC: Whale optimization algorithm and novel priority-based encoding approach. Journal of Decisions and Operations Research, 4, 299–315.

Ghasempoor Anaraki, M., Vladislav, D. S., Karbasian, M., Osintsev, N., & Nozick, V. (2021). Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach. Journal of Fuzzy Extension and Applications, 2, 69–88. http://www.journalfea.com/article_126927_b766d27cac24e52cb4b16d6f1b8e69f4.pdf

Ghahremani Nahr, J., Kian, R., & Rezazadeh, H. (2018). A Modified Priority-Based Encoding for Design of a Closed-Loop Supply Chain Network Using a Discrete League Championship Algorithm. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/8163927

Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert Systems with Applications, 116, 454–471. https://doi.org/10.1016/j.eswa.2018.09.027

Ghahremani Nahr, J., Pasandideh, S. H. R., & Niaki, S. T. A. (2020). A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount. Journal of Industrial and Production Engineering, 37, 1–22. https://doi.org/10.1080/21681015.2017.1421591

Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural Computing and Applications, 32, 3967–3985. https://doi.org/10.1007/s00521-018-3847-9

Govindan, K., Darbari, J. D., Agarwal, V., & Jha, P. C. (2017). Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network. Journal of Cleaner Production, 165, 1598–1619. https://doi.org/10.1016/j.jclepro.2017.06.180

Guo, Y., Shi, Q., & Guo, C. (2022). A Fuzzy Robust Programming Model for Sustainable Closed-Loop Supply Chain Network Design with Efficiency-Oriented Multi-Objective Optimization. Processes, 10. https://doi.org/10.3390/pr10101963

Haddadsisakht,A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 118–131. https://doi.org/10.1016/j.ijpe.2017.09.009

Huang, L., Murong, L., & Wang, W. (2020). Green closed-loop supply chain network design considering cost control and CO 2 emission . Modern Supply Chain Research and Applications, 2, 42–59. https://doi.org/10.1108/mscra-02-2019-0005

Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers and Industrial Engineering, 116, 178–191. https://doi.org/10.1016/j.cie.2017.12.025

Kalantari, S., Kazemipoor, H., Movahedi Sobhani, F., & Mohammad Hadji Molana, S. (2022). Designing sustainable closed-loop supply chain network with considering spot-to-point inflation and carbon emission policies: A case study. Computers and Industrial Engineering, 174, 46–77. https://doi.org/10.1016/j.cie.2022.108748

Khalili Nasr, A., Tavana, M., Alavi, B., & Mina, H. (2021). A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. Journal of Cleaner Production, 287, 124994. https://doi.org/10.1016/j.jclepro.2020.124994

Kousar, S., Batool, M., Kausar, N., Pamucar, D., Ozbilge, E., & Tantay, B. (2022). Multi-objective Intuitionistic Fuzzy Linear Programming model for optimization of industrial closed-loop supply chain network. Advances in Production Engineering And Management, 17, 381–393. https://doi.org/10.14743/apem2022.3.443

Liu, Y., Ma, L., & Liu, Y. (2021). A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity. Applied Mathematical Modelling, 92, 99–135. https://doi.org/10.1016/j.apm.2020.10.042

Mardan, E., Govindan, K., Mina, H., & Gholami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. Journal of Cleaner Production, 235, 1499–1514. https://doi.org/10.1016/j.jclepro.2019.06.187

Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of Cleaner Production, 242, 118452. https://doi.org/10.1016/j.jclepro.2019.118452

Pahlevan, S. M., Hosseini, S. M. S., & Goli, A. (2021). Sustainable supply chain network design using products'' life cycle in the aluminum industry. Environmental Science and Pollution Research, 1–25. https://doi.org/10.1007/s11356-020-12150-8

Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36, 3433–3446. https://doi.org/10.1016/j.apm.2011.10.007

Pop, P. C., Pintea, C. M., Pop Sitar, C., & Hajdu-Məcelaru, M. (2015). An efficient Reverse Distribution System for solving sustainable supply chain network design problem. Journal of Applied Logic, 13, 105–113. https://doi.org/10.1016/j.jal.2014.11.004

Sadeghi, A., Mina, H., & Bahrami, N. (2020). A mixed integer linear programming model for designing a green closed-loop supply chain network considering location-routing problem. International Journal of Logistics Systems and Management, 36, 177–198. https://doi.org/10.1504/IJLSM.2020.107389

Seyedrezaei, M., Najafi, S. E., Aghajani, A., & Valami, H. B. (2012). Designing a Genetic Algorithm to Optimize Fulfilled Orders in Order Picking Planning Problem with Probabilistic Demand. International Journal of Research in Industrial Engineering Journal Homepage, 1, 40–57.

Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. International Journal of Advanced Manufacturing Technology, 68, 917–931. https://doi.org/10.1007/s00170-013-4953-6

Soon, A., Heidari, A., Khalilzadeh, M., Antucheviciene, J., Zavadskas, E. K., & Zahedi, F. (2022). Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels. Systems, 10, 94. https://doi.org/10.3390/systems10040094

Yadegari, E., Alem-Tabriz, A., & Zandieh, M. (2019). A memetic algorithm with a novel neighborhood search and modified solution representation for closed-loop supply chain network design. Computers and Industrial Engineering, 128, 418–436. https://doi.org/10.1016/j.cie.2018.12.054

Yolmeh, A., & Saif, U. (2021). Closed-loop supply chain network design integrated with assembly and disassembly line balancing under uncertainty: an enhanced decomposition approach. International Journal of Production Research, 59, 2690–2707. https://doi.org/10.1080/00207543.2020.1736723

Zahedi, A., Salehi-Amiri, A., Hajiaghaei-Keshteli, M., & Diabat, A. (2021). Designing a closed-loop supply chain network considering multi-task sales agencies and multi-mode transportation. Soft Computing, 25, 6203–6235. https://doi.org/10.1007/s00500-021-05607-6

Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty. Computers and Industrial Engineering, 74, 139–148. https://doi.org/10.1016/j.cie.2014.05.008

Zhang, X., Zhao, G., Qi, Y., & Li, B. (2019). A robust fuzzy optimization model for closed-loop supply chain networks considering sustainability. Sustainability (Switzerland), 11(20). https://doi.org/10.3390/su11205726

Ziari, M., & Sajadieh, M. S. (2022). A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry). RAIRO - Operations Research, 56, 431–444. https://doi.org/10.1051/ro/2022002

Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry. Journal of Cleaner Production, 133, 314–337. https://doi.org/10.1016/j.jclepro.2016.05.091