Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms

This paper focuses on optimizing the long- and short-term planning of perishable product supply chain network (PPSCN). It addresses integration strategic location, tactical inventory, operational routing decisions. Additionally, it takes into consideration specific characteristics products, including their shelf life, inventory management, transportation damages. The main objective is to minimize overall cost. To achieve this, a nonlinear mixed integer programming model developed for multi-echelon, multi-product, multi-period location-inventory-routing problem (LIRP) in PPSCN. Two hybrid metaheuristic algorithms, namely genetic algorithm (GA) multiple population (MPGA), are hybridized with variable neighborhood search (VNS) proposed solve this NP-hard problem. Moreover, novel coding method devised represent complex structure LIRP input parameters tuned using Taguchi experimental design method, considering sensitivity meta-heuristic algorithms these parameters. Through experiments various scales, MPGA VNS indicates superior performance, as evidenced by results. Sensitivity analysis conducted examine influence key optimal objective, providing valuable management implications. results clearly validate efficacy solution reliable tool