A memetic based algorithm for simultaneous preventive maintenance scheduling and spare-parts inventory management for manufacturing systems

To increase machine availability, it may be necessary to halt them to replace components before failures occur. Therefore, optimizing spare parts inventory management is a key factor in improving maintenance activities. Spare part costs are a significant contributor to total maintenance costs, including holding and ordering costs, and are often dependent on the maintenance schedule. Hence, it is important to optimize preventive maintenance scheduling and spare parts inventory management together. The first attempt to introduce a new Mixed-Integer Linear Programming (MILP) model that integrates both decisions was by Mjirda et al. (2016). Our paper is the first to extend their work in order to come up with an efficient aided decision making framework based on a mat-heuristic approach that delivers a good trade-off between computational time and solution quality. Indeed, this paper introduces a novel Hybrid Memetic Algorithm (HMA) that synergistically combines Memetic Algorithm (MA) with MILP to solve this NP-hard problem. The paper aims to provide a unified and efficient mat-heuristic based aided decision making framework that optimizes the sum of maintenance, operating, holding, and ordering costs by synchronizing preventive maintenance schedules and spare parts inventory for a set of independent machines. Its efficiency is illustrated through an extensive computational study. The HMA outperforms standalone MILP solvers, solving 36% of instances to optimality within a computational time of less than 141 s, and achieving a maximum gap of 5% for 46% of instances within less than 200 s. Our findings highlight the computational efficiency and solution quality offered by the HMA, thereby advancing the state-of-the-art in integrated spare parts inventory management and preventive maintenance scheduling.