Optimizing Inventory Management through Multiobjective Reverse Logistics with Environmental Impact

TL;DR

This paper introduces new multiobjective mathematical models and solution algorithms for inventory management in reverse logistics, considering costs, demand variations, and environmental impacts, validated through numerical experiments.

Contribution

It develops a novel multiobjective model for reverse logistics inventory management that incorporates environmental waste disposal costs and compares scalarization solution techniques.

Findings

Effective Pareto front approximation methods identified

Models successfully validated with numerical experiments

Environmental impact considered in inventory optimization

Abstract

We present novel mathematical models for inventory management within a reverse logistics system. Technological advancements, sustainability initiatives, and evolving customer behaviours have significantly increased the demand for repaired products. Our models account for varying demand levels for newly produced and repaired items. To optimize overall costs with constrained scenarios, we formulated mixed integer programming problems. Solution procedures for the proposed problems are introduced, and the accuracy of these solutions has been validated through numerical experiments. Additionally, we address the cost of waste disposal as an environmental concern. This paper develops a multiobjective mathematical model and provides an algorithm for the Pareto solution. Various scalarization techniques are utilized to identify the Pareto front, and a comparison of these techniques is presented.