A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems

Alicastro, Mirko, Ferone, Daniele, Festa, Paola, Fugaro, Serena and Pastore, Tommaso (2021) A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Computers and Operations Research, 131. ISSN 0305-0548

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Abstract

Additive manufacturing – also known as 3D printing – is a manufacturing process that is attracting more and more interest due to high production rates and reduced costs. This paper focuses on the scheduling problem of multiple additive manufacturing machines, recently proposed in the scientific literature. Given its intractability, instances of relevant size of additive manufacturing (AM) machine scheduling problem cannot be solved in reasonable computational times through mathematical models. For this reason, this paper proposes a Reinforcement Learning Iterated Local Search meta-heuristic, based on the implementation of a Q-Learning Variable Neighborhood Search, to provide heuristically good solutions at the cost of low computational expenses. A comprehensive computational study is conducted, comparing the proposed methodology with the results achieved by the CPLEX solver and to the performance of an Evolutionary Algorithm recently proposed for a similar problem, and adapted for the AM machine scheduling problem. Additionally, to explore the trade-off between efficiency and effectiveness more deeply, we present a further set of experiments that test the potential inclusion of a probabilistic stopping rule. The numerical results evidence that the proposed Reinforcement Learning Iterated Local Search is able to obtain statistically significant improvements compared to the other solution approaches featured in the computational experiments.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2021 Elsevier Ltd
Uncontrolled Keywords:	additive manufacturing,iterated local search,machine scheduling,q-learning,reinforcement learning,general computer science,modelling and simulation,management science and operations research ,/dk/atira/pure/subjectarea/asjc/1700/1700
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	12 May 2026 10:26
Last Modified:	14 May 2026 15:16
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102959
DOI:	10.1016/j.cor.2021.105272

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