Robust Job Shop Scheduling with Condition-Based Maintenance and Random Breakdowns

This paper presents a simulation-optimization approach for solving the Job Shop Scheduling Problem (JSSP) under both planned and unplanned machine unavailability. Two maintenance policies are considered: Condition-Based Maintenance (CBM) and Corrective Maintenance (CM). The real makespan objective function is first approximated using several surrogate functions that are independently optimized using Genetic Algorithm (GA), before their best solutions are evaluated through simulation with stochastic degradation of machines, random breakdowns, and uncertain CBM and CM duration. To ensure schedule robustness, a weighted average of the expected makespan and its 90th percentile is used as the evaluation criterion, and the best schedule is added to an elite list to initiate the next iteration. A stopping rule inspired by Simulated Annealing (SA) is employed to prevent premature conversion. Numerical experimentation on random instances showed that the proposed approach can reach high-quality solutions effectively. Copyright (C) 2022 The Authors.