Optimizing perishable food products across states: a multi objective evolutionary algorithm for surplus to deficit transportation

Abstract

Ensuring the seamless flow of goods from farmer to stakeholder is paramount especially considering the facts such as demand fluctuations, quantity availability, vehicle capacity, inventory limits and truck preferences. In this paper, we proposed a mixed integer linear programming (MILP) model for distributing perishable food goods from surplus states to surplus and deficit state wholesalers in the Indian subcontinent, with the goal of minimizing total cost (fixed, variable, and operational) and transportation time. To ensure product delivery on time, we looked at various scenarios including a case with real-life constraints. Solving this computationally complex task with conflicting objectives in a reasonable time is difficult. To handle the aforementioned multi-objective problem, we proposed an evolutionary algorithm based modified non-dominated sorting genetic algorithm II (MNSGA-II). Furthermore, the suggested algorithm's performance is tested against the most extensively used multi-objective algorithm, Non-dominated Sorting Genetic method-II (NSGA-II). According to the findings, the proposed MNSGA-II outperforms practically all instances.