On the integration of reinforcement learning and simulated annealing for the parallel batch scheduling problem with setups

Gustavo Alencar Rolim, Caio Paziani Tomazella and Marcelo Seido Nagano

European Journal of Operational Research, 2025, vol. 326, issue 2, 220-233

Abstract: Motivated by semiconductor applications, where wafer lots are grouped into families and processed on batch machines, this paper addresses a generalized unrelated parallel-batch scheduling problem. The goal is to minimize total completion time (flow time) while considering family- and machine-dependent setup times. We propose a mixed-integer programming formulation, establish a necessary condition for optimal schedules, and develop a polynomial-time heuristic for batching and sequencing. We also evaluate Q-Learning, a model-free reinforcement learning algorithm, for neighborhood selection within two Simulated Annealing-based metaheuristics: Stochastic Local Search (SLS) and Adaptive Large Neighborhood Search (ALNS). Results show that SLS and ALNS achieve better solutions and faster convergence compared to existing approaches. Finally, we conclude that while Q-Learning has the potential to improve solution quality in certain cases, it also increases the complexity of the algorithms, making them harder to configure and scale.

Keywords: Metaheuristics; Reinforcement learning; Simulated annealing; Adaptive large neighborhood search; Stochastic local search (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221725003273
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:326:y:2025:i:2:p:220-233

DOI: 10.1016/j.ejor.2025.04.042

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().