Maximizing the flow of used goods by designing a reverse logistics network using meta-heuristic methods

Document Type : Original Article

Authors

1 Department of Computer Sciences, University of Qom, Qom, IRAN.

2 Department of Mathematics, University of Qom, Qom, IRAN.

10.22091/jemsc.2026.14496.1324

Abstract

In supply chains, the goal is usually to meet demand at the lowest cost. But there are cases where either the transportation costs are insignificant or, such as in critical situations, the supply of more goods has a much higher priority than the costs. In such cases, instead of minimizing the cost, we should maximize the transfer flow values. In this case, the supply chain network minimization problem (minimum cost flow) becomes a type of flow maximization problem (maximum flow). In this paper, we have addressed a type of flow maximization problem in supply chains. First, we have defined and modeled it, then, considering its complex structure, we have obtained a suitable approximate solution for it by using a meta-heuristic method.

Keywords

Main Subjects

References

Alinezhad, M., Mahdavi, I., Hematian, M., & Tirkolaee, E. B. (2021). A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environment, Development and Sustainability, 1(24), 1-28. https://doi/org/10.1007/s10668-021-01809-y
Aslam, J., Saleem, A., Khan, N. T., & Kim, Y. B. (2021). Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry. J. Innov. Knowl, 6, 124–134. https://doi:org/10.1016/j.jik.2021.01.002  
Baghalian, A., Rezapour, Sh., & Zanjirani Farahani, R. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 1(227), 199– 215. https://doi.org/10.1016/j.ejor.2012.12.017
Baghizadeh, K., Cheikhrouhou, N., Govindan, K., & Ziyarati, M. (2022). Sustainable agriculture supply chain network design considering water-energy-food nexus using the queuing system: A hybrid robust possibilistic programming. Natural Resource Modeling, 35(1), e12337. https://doi.org/10.1111/nrm.12337
Banasik, A., Kanellopoulos, A., Claassen, G., M. Bloemhof Ruwaard, J. M., & Vander Vorst, J. G. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, PB(183), 409-420. https://doi.org/10.1016/j.ijpe.2016.08.012 
Behzadi, G., Sullivan, M. J., Olsen, T. L., Scrimgeour, F., & Zhang, A. (2017). Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. International Journal of Production Economics, C(191), pp. 207-220. https://doi.org/10.1016/j.ijpe.2017.06.018
Bustos, L., Gonzalez, E., & Vejar, A. (2017). A multi-objective optimization model for the design of an effective decarbonized supply chain in mining. International Journal of Production Economics, 6(193), 449–464. https://doi.org/10.1016/j.ijpe.2017.08.012
Chang, Y., Iakovou, E., & Shi, W. (2019). Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art, challenges and opportunities. Int. J. Prod. Res., 7(58), 1-18. https://doi:org/10.1080/00207543.2019.1651946
Colicchia, C., Creazza, A., Dallari, F., & Melacini, M. (2016). Eco-efficient supply chain networks: development of a design framework and application to a real case study. Production planning & control, 3(27), 157-168. https://doi.org/10.1080/09537287.2015.1090030
Demir, S., Gunduz, M. A., Kayikci, Y., & Paksoy, T. (2023). Readiness and maturity of smart and sustainable supply chains: A model proposal. Engineering Management Journal, 2(35), 181-206. https://doi.org/10.1080/10429247.2022.2132974
Farahani, R. Z., Rezapour, S., T. Drezner, & S. Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118. https://doi.org/10.1016/j.omega.2013.08.006
Fatemi, M. S., Ghodratnama, A., & Tavakkoli-Moghaddam, R. (2022). A multi-functional tri-objective mathematical model for the pharmaceutical supply chain considering congestion of drugs in factories. Research in Transportation Economics, C(92), 101094. https://doi.org/10.1016/j.retrec.2021.101094
Gonela, V., Zhang, J., Osmani, A., & Onyeaghala, R. (2015). Stochastic optimization of sustainable hybrid generation bioethanol supply chains, Transportation Research Part E: Logistics and Transportation Review, 77, 1-28. https://doi.org/10.1016/j.tre.2015.02.008
Goodarzian, F., Hosseini-Nasab, H., Munuzuri, J., & Fakhrzad, M. B. (2020). A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics. Applied Soft Computing, 92, 106331. 
Hajipour, V., & Salimian, S. (2021). Design and optimization of healthcare location-inventory problem in the relief supply chains. Journal of Industrial Management Studies, 19(63), 193-229.  https://doi.org/10.22054/jims.2021.36813.2185
Hombach, L. E., Busing, C., & Walther, G. (2018). Robust and sustainable supply chains under market uncertainties and different risk attitudes: A case study of the German biodiesel market. European Journal of Operational Research, 269(1), 302-312. https://doi.org/10.1016/j.ejor.2017.07.015
Iftikhar, A.,  Ali, I.,  Arslan, A., & Tarba, S. (2022). Digital innovation, data analytics, and supply chain resiliency: A bibliometric-based systematic literature review. Annals of operations research, 5(2), 1-24.  https://doi.org/10.1007/s10479-022-04765-6  
Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. Int. J. Prod. Econ., 231, 107831. https://doi:org/10.1016/j.ijpe.2020.107831 
Madadi, A., Kurz, M., Mason, S., & Taaffe, K. (2014). Supply chain design under quality disruptions and tainted materials delivery. Transportation Research Part E, Logistics and Transportation Review, C(67), 105–123.
Mangla, S. K., Luthra, S., Mishra, N., Singh, A., Rana, N. P., Dora M., & Dwivedi, Y. (2018). Barriers to effective circular supply chain management in a developing country context. Prod. Plann. Contr, 6(29), 551–569. https://doi:org/10.1080/09537287.2018.1449265  
Meneghetti, A., & Monti, L. (2015). Greening the food supply chain: An optimisation model for sustainable design of refrigerated automated warehouses. International Journal of Production Research, 21(53), 6567-6587. https://doi.org/10.1080/00207543.2014.985449
Modgil, S., Singh, R. K., &  Hannibal, C. (2022). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246-1268. https://doi.org/10.1108/IJLM-02-2021-0094
Nayeri, S., Torabi, S. A., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 311, 127691. https://doi.org/10.1016/j.jclepro.2021.127691 
Purwaningsih, E., Muslikh, M., Suhaeri, S., & Basrowi, B. (2024). Utilizing blockchain technology in enhancing supply chain efficiency and export performance, and its implications on the financial performance of SMEs. Uncertain Supply Chain Manag, 1(12), 449–460.  https://doi.org/10.5267/j.uscm.2023.9.007 
Rashid, A., Rasheed, R., Ngah, A. H., Pradeepa Jayaratne, M. D. R., Rahi, S., & Tunio, M. N. (2024). Role of information processing and digital supply chain in supply chain resilience through supply chain risk management. Journal of Global Operations and Strategic Sourcing, 17(9), 429-447. https://doi.org/10.1108/JGOSS-12-2023-0106
Ren, J., Manzardo, A.,  Toniolo, S., & Scipioni, A. (2013). Sustainability of hydrogen supply chain, Part II: Prioritizing and classifying the sustainability of hydrogen supply chains based on the combination of extension theory and AHP. International Journal of Hydrogen Energy, 38(32), 13845-13855. https://doi.org/10.1016/j.ijhydene.2013.08.078
Roshan, M.,  Tavakkoli-Moghaddam, R., & Rahimi, Y. (2019). A two-stage approach to agile pharmaceutical supply chain management with product substitutability in crises. Computers & Chemical Engineering, 127, 200-217. https://doi.org/10.1016/j.compchemeng.2019.05.014
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. Int. J. Prod. Res, 7(57), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261 
Sadeghi, K., & Qaisari Hasan Abadi, M. (2024). Sustainable supply chain resilience for logistics problems: Empirical validation using robust and computational intelligence methods. Journal of Cleaner Production, 437, 0267. https://doi.org/10.1016/j.jclepro.2023.140267 
Saif, A., & Elhedhli, S. (2016). Cold supply chain design with environmental considerations: A simulation-optimization approach. European Journal of Operational Research, 1(251), 274-287. https://doi.org/10.1016/j.ejor.2015.10.056
Sajjad, A., Eweje, G., & Tappin, D. (2015). Sustainable supply chain management: Motivators and barriers. Bus. Strat., 7(24), 643–655. https://doi:org/10.1002/bse.1898.
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2020). Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic. International Journal of Logistics Research and Applications, 1(25), 1-21. https://doi.org/10.1080/13675567.2020.1810213
Singh, G., Singh, S., Daultani, Y., & Chouhan, M. (2023). Measuring the influence of digital twins on the sustainability of manufacturing supply chain: A mediating role of supply chain resilience and performance. Computers & Industrial Engineering, 186, 109711. https://doi.org/10,1016/j.cie.2023.109711
Singh, S. M., & Shore, A. (2022). Building artificial intelligence enabled resilient supply chain: A multi-method approach. Journal of Enterprise Information Management, 37(2), 414-436.  https://doi.org/10.1108/JEIM-09-2022-0326
Taleizadeh, A. A., Ghavamifar, A., & Khosrojerdi, A. (2020). Resilient network design of two supply chains under price competition: Game theoretic and decomposition algorithm approach. Operational Research, 1(22), 825-857. https://doi.org/10.1007/s12351-020-00565-7
Tat, R., Heydari, J., & Rabbani, M. (2020). A mathematical model for pharmaceutical supply chain coordination: Reselling medicines in an alternative market. Journal of Cleaner Production, 268, 121897. https://doi.org/10.1016/j.jclepro.2020.121897
Tavakkoli Moghaddam, S., Javadi, M., & Hadji Molana, S. M. (2019). A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization. Journal of Industrial Engineering International, 4(15), 709-721. https://doi.org/10.1007/s40092-018-0287-1
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. https://doi.org/10.1016/j.fss.2007.08.010
Ugarte, G. M., Golden, J. S., & Dooley, K. J. (2016). Lean versus green: The impact of lean logistics on greenhouse gas emissions in consumer goods supply chains. Journal of Purchasing and Supply Management, 22(2), 98-109. https://doi.org/10.1016/j.pursup.2015.09.002
Vali-Siar, M. M., & Roghanian, E. (2021). Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption. Sustainable Production and Consumption, 1(30), 278-300. https://doi.org/10.1016/j.spc.2021.12.003
Yakavenka, V., Mallidis, I., Vlachos, D., Iakovou, E., & Eleni, Z. (2020). Development of a multi-objective model for the design of sustainable supply chains: The case of perishable food products. Annals of Operations Research, 1(294), 593-621. https://doi.org/10.1007/s10479-019-03434-5
Yuan, Y., Tan, H., & Liu, L. (2024). The effects of digital transformation on supply chain resilience: A moderated and mediated model. Journal of Enterprise Information Management, 37(2), 488-510. https://doi.org/10.1007/s10479-019-03434-5
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