Metaheuristic algorithms for a sustainable agri-food supply chain considering marketing practices under uncertainty. (1st March 2023) - Gholian-Jouybari, Fatemeh; Hashemi-Amiri, Omid; Mosallanezhad, Behzad; Hajiaghaei-Keshteli, Mostafa
Highlights: Devising a sustainable agri-food supply chain considering marketing concepts. Proposing a robust convex optimization approach to address the uncertainty. Optimizing the proposed network using exact and metaheuristic approaches. Modifying the Keshtel Algorithm by developing its swirling operator. MOKASEO outperforms based on the developed hybrid MCDM method. Abstract: In recent decades, the rapid growth of the global population has caused a significant increase in agricultural and food product demands. Thereby, the production of various items in the agricultural food supply chain network has increased to diminish food security concerns. On the other hand, the excessive production of products has led to various issues, such as greenhouse gas emissions and increased water consumption in farmlands, followed by supply chain-related challenges affecting the intermediaries in the next network levels. In this study, an agricultural food supply chain network under marketing practices is firstly probed by developing a stochastic multi-objective programming model to effectively improve three main pillars of sustainability. A convex robust optimization approach addresses the uncertainty of the farm production capacity and the saffron demands in the supply network. The effectiveness of the proposed mathematical model is certified by a case study on saffron business using the LP-metric method. A metaheuristic-oriented methodology comprising a modified Keshtel Algorithm is adapted to deal with the NP -hardness of the problems. The performance of the proposed solution methods is evaluated by two strategies, a statistical comparison and a supportive tool developed based on multi-criteria decision-making (MCDM) methods. The results validate the capability of the applied algorithms to solve the problem in different dimensions. Moreover, the MCDM method approves that MOKASEO outclassed in small, medium, and large-sized problems compared to other algorithms.