Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment. (22nd July 2019)

• Record Type: Journal Article
• Authors: Goli, Alireza; Zare, Hasan Khademi; Tavakkoli‐Moghaddam, Reza; Sadegheih, Ahmad
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 4
• Page End: 34
• Publication Date: 2019-07-22
• Subjects: financial flow of supply chain -- multiobjective metaheuristic algorithms -- possibilistic programming; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12228
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: This paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.