Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns

• Published in: Annals of operations research Vol. 338; no. 1; pp. 801 - 833
• Main Authors: Zambrano-Rey, Gabriel Mauricio, González-Neira, Eliana María, Forero-Ortiz, Gabriel Fernando, Ocampo-Monsalve, María José, Rivera-Torres, Andrea
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Coefficient of variation; Combinatorics; Computer simulation; Dispatching rules; Integer programming; Job shop scheduling; Job shops; Lateness; Linear programming; Methods; Mixed integer; Monte Carlo method; Monte Carlo simulation; Operations Research/Decision Theory; Original Research; Performance evaluation; Scheduling (Management); Tabu search; Theory of Computation; Colombia; Machine breakdowns; Stochastic job shop scheduling; Tabu search; Simheuristics; Maximum lateness
• DOI: 10.1007/s10479-023-05592-z
• ISSN: 0254-5330

This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness. The stochastic nature is modeled by considering uncertain times between failures (TBF) and uncertain times to repair (TTR). To tackle this problem, a simheuristic approach is proposed, which combines a tabu search (TS) algorithm with Monte Carlo simulation. A total of 320 instances were used to conduct multiple experiments. Instances were generated with two distributions to study the behavior of stochastic TTR and TBF under log-normal and exponential distributions. Firstly, the performance of the simheuristic was evaluated for small instances by comparing it with the simulation of optimal solutions obtained with a mixed-integer linear programming (MILP) model. The simheuristic approach demonstrated an average improvement of around 7% compared to the simulation of MILP model solutions. Secondly, the simheuristic performance was evaluated for medium and large-size instances by comparing it with the simulation of the solutions obtained by the earliest due date (EDD) and process time plus work in the next queue plus negative slack (PT + WINQ + SL) dispatching rules. The results showed an average improvement of around 11% compared to EDD and 14% compared to PT + WINQ + SL. Furthermore, the results highlight that even when the two distributions have the same expected value and coefficient of variation, they can yield different expected maximum lateness values. This emphasizes the importance of precise distribution fitting when solving real cases to achieve effective scheduling performance.