Communication Dans Un Congrès Année : 2025

Reinforcement learning-based iterated local search for the unrelated parallel machine scheduling problem

Résumé

In this work, we propose an optimization method that combines Iterated Local Search (ILS), Variable Neighborhood Descent (VND) and Reinforcement Learning (RL) to solve the earliness and tardiness unrelated parallel machine scheduling problem with sequence and machine-dependent setup times that arises in just-in-time manufacturing systems. In this problem, a set of non-identical parallel machines are available to process a given set of tasks. Each task has a due date, two penalty weights (earliness and tardiness) and requires a machine-dependent processing time to be completed. In addition, a setup operation is necessary between the operation of two consecutive jobs. The objective is to schedule all tasks so that each machine process at most a job at time and that minimizes the total weighted earliness and tardiness penalty. The state-of-the-art meta-heuristic method in the literature to solve this problem is a combination of ILS and a randomized VND where the neighborhoods inside the VND are explored in a random order. Thus, in this work we propose a RL-based method to define, at each iteration of the ILS, the exploration order of the neighborhood structures inside the VND instead of considering totally fixed or totally random orders. We model this decision problem as a Markov decision process and consider different policies and reward functions. Experimental tests show that the method is able to find solutions within 2% from the best-known solutions in the literature.

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Dates et versions

hal-05248205 , version 1 (28-11-2025)

Identifiants

  • HAL Id : hal-05248205 , version 1

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Arthur Kramer, Ruan Myller Magalhães de Oliveira, Thiago Alves de Queiroz. Reinforcement learning-based iterated local search for the unrelated parallel machine scheduling problem. 34th European Conference on Operational Research: 50th Anniversary of EURO, Jun 2025, Leeds, United Kingdom. ⟨hal-05248205⟩
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