Reinforcement learning and stochastic dynamic programming for jointly scheduling jobs and preventive maintenance on a single machine to minimise earliness-tardiness

• Published in: International journal of production research Vol. 62; no. 3; pp. 705 - 719
• Main Authors: Sabri, Abderrazzak, Allaoui, Hamid, Souissi, Omar
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.02.2024; Taylor & Francis LLC
• Subjects: Advanced planning and scheduling systems; Algorithms; Computer Science; Computing time; Deep learning; Dynamic programming; Lateness; Machine learning; Preventive maintenance; Run time (computers); Scheduling; Stochastic programming
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2172472

This paper addresses the problem of stochastic jointly scheduling of resumable jobs and preventive maintenance on a single machine, subject to random breakdowns, to minimise the earliness-tardiness cost. The main objective is to investigate using trending machine learning-based methods compared to stochastic optimisation approaches. We propose two different methods from both fields as we solve the same problem firstly with a stochastic dynamic programming model in an approximation way, then with an attention-based deep reinforcement learning model. We conduct a detailed experimental study according to solution quality, run time, and robustness to analyse their performances compared to those of an existing approach in the literature as a baseline. Both algorithms outperform the baseline. Moreover, the machine learning-based algorithm outperforms the stochastic dynamic programming-based heuristic as we report up to 30.5% saving in total cost, a reduction of computational time from 67 min to less than $ 1s $ 1 s on big instances, and a better robustness. These facts highlight clearly its potential for solving such problems.