Mixed-integer programming models for a multi-period integrated reverse logistics network with multiple recovery options and different quality levels of returns

Abstract

This paper addresses an integrated reverse logistics (RL) model with a production route and multiple recovery options, including repair, refurbishing, remanufacturing, recycling, and selling spare components. We aim to use the quality grading system method to analyse returns comprehensively. In this regard, quality levels are used to classify returns and quality thresholds are utilised to categorise them into certain quality groups. The proposed model deals with different activities such as locating, purchasing, transporting, processing, inventory holding, recovering, and production in a planning horizon with multiple periods. A two-stage optimisation model is proposed to facilitate the decision-making process. In Stage 1, a mixed integer linear programming (MILP) model is proposed to minimise the costs and determine the number of returned products directed into different recovery routes. In Stage 2, another MILP is proposed to maximise the network’s profit. Furthermore, 350 scenarios are defined to examine the network’s performance and decide the optimal quality thresholds. Finally, the proposed models are applied to a real dataset and solved by the CPLEX solver through Pyhton. According to the results, scenarios that use multiple recovery options earn higher profits and lower costs as most demands are satisfied and the stock level of inventories decreases.