A Multi-objective Genetic Algorithm Approach for Multi-component Products Recovery and Remanufacturing Planning

The remanufacturing process has gained recognition primarily for its effectiveness in addressing environmental concerns related to End-Of-Life (EOL) and End-Of-Use (EOU) products. Consequently, a growing number of companies specialise in remanufacturing various product types. This practice not only prolongs product lifespan but also reduces manufacturing costs. This paper examines the challenges encompassing all stages of the remanufacturing process: product recovery, transportation, and remanufacturing operations for customers with similar product types over a finite horizon. The problem involves planning the recovery of used products for remanufacturing and grade enhancement. The main decisions include selecting customers for product recovery and replacement, optimising transportation for used product retrieval, and making decisions for the post-remanufacturing grade. The objective is to minimise both economic and environmental costs. To address this, we propose an NSGA-II (Non-dominated Sorting Genetic Algorithm) based multi-objective solution approach to tackle this problem.