A bi-objective inventory optimization in forward and reverse logistic supply chains with shortages

Department of Mathematics, Indian Institute of Technology Kharagpur, Kharagpur 721302, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 28 February 2025
Accepted: 21 January 2026

Abstract

Nowadays, remanufacturing is a sustainable and cost-effective process that restores used products or components to their original performance standards, often making them as good as new. This article focuses on the process of remanufacturing used products, emphasizing their restoration to the original functionality and performance standards and finding a cost-effective solution. It considers various aspects of the remanufacturing process, including collection, inspection, repair, and reassembly, while highlighting the environmental benefits associated with this sustainable practice. We have presented a detailed analysis of all cost components and carbon emissions associated with each process in the system, including costs incurred at the primary manufacturer, primary retailer, collection center, and other relevant stages. The main aim of this article is to optimize total system cost and carbon emissions associated with each process. To get the model optimum, we have solved the bi-objective problem by non-dominated sorting genetic algorithm (NSGA-II), which ensures an optimal balance between the two objectives. The major novelties of this work include imperfect screening, quadratic demand, and unequal shipment. For model validation, a numerical example has been analyzed on the basis of a case study, which results in a set of Pareto optimal solutions for the problem. A sensitivity analysis has been presented to evaluate the impact of varying parameters on the outcomes. The findings of this study reveal that it is possible to achieve up to a 65.21% reduction in costs through the proposed approach.