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Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

Author

Listed:
  • Kalpit Patne
  • Nagesh Shukla
  • Senevi Kiridena
  • Manoj Kumar Tiwari

Abstract

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

Suggested Citation

  • Kalpit Patne & Nagesh Shukla & Senevi Kiridena & Manoj Kumar Tiwari, 2018. "Solving closed-loop supply chain problems using game theoretic particle swarm optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5836-5853, September.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:17:p:5836-5853
    DOI: 10.1080/00207543.2018.1478149
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    Citations

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    Cited by:

    1. Jian Zhou & Wenying Xia & Ke Wang & Hui Li & Qianyu Zhang, 2020. "Fuzzy Bi-Objective Closed-Loop Supply Chain Network Design Problem with Multiple Recovery Options," Sustainability, MDPI, vol. 12(17), pages 1-26, August.
    2. Zoubida Benmamoun & Khaoula Khlie & Mohammad Dehghani & Youness Gherabi, 2024. "WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems," Mathematics, MDPI, vol. 12(7), pages 1-61, April.
    3. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    4. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    5. Yang, Yuxiang & Goodarzi, Shadi & Bozorgi, Ali & Fahimnia, Behnam, 2021. "Carbon cap-and-trade schemes in closed-loop supply chains: Why firms do not comply?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    6. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    7. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.

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