Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

Abstract

This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed to solve dynamic scheduling under perturbations. The uncertainty sources considered in this study are the expiration date of product components and production delays. The proposed Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACO) take into consideration two objective functions: minimizing the makespan and reducing the number of perishable products. The algorithms are tested on a flow-shop agri-food system.

Application of Genetic Algorithms to Optimize Distribution in Food Transport Companies: A Systematic Literature Review

“…(Fitriana et al 2019);(Akpinar 2021);(Mohammed et al 2017);(Zhou et al 2018);(Zhang et al 2013);(Ganji et al 2020);(Li et al 2023);(Miranda- Ackerman et al 2019);(Tangour et al 2021) …”

Special Issue: “The Planning and Scheduling of Manufacturing Systems”

“…Tangour et al [4] studied a scheduling problem in the flow-shop agri-food production of perishable products under constrained resources. To solve the dynamic scheduling problem, a genetic algorithm and ant colony optimization algorithm under the perturbations of the expiration date of product components and production delays were proposed.…”