Population-based iterated local search for batch scheduling on parallel machines with incompatible job families, release dates, and tardiness penalties Population-based iterated local search

• Published in: Optimization letters Vol. 19; no. 1; pp. 193 - 210
• Main Authors: Medeiros, José Maurício Fernandes, Subramanian, Anand, Queiroga, Eduardo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
• Subjects: Computational Intelligence; Mathematics; Mathematics and Statistics; Numerical and Computational Physics; Operations Research/Decision Theory; Optimization; Original Paper; Simulation; Population iterated local search; Parallel machines; Combinatorial optimization; Batch scheduling
• ISSN: 1862-4472; 1862-4480
• DOI: 10.1007/s11590-024-02116-x

This work addresses a parallel batch machine scheduling problem subject to tardiness penalties, release dates, and incompatible job families. In this environment, jobs of the same family are partitioned into batches and each batch is assigned to a machine. The objective is to determine the sequence in which the batches will be processed on each machine with a view of minimizing the total weighted tardiness. To solve the problem, we propose a population-based iterated local search algorithm that makes use of multiple neighborhood structures and an efficient perturbation mechanism. The algorithm also incorporates the time window decomposition (TWD) heuristic to generate the initial population and employs population control strategies aiming to promote individuals with higher fitness by combining the total weighted tardiness with the contribution to the diversity of the population. Extensive computational experiments were conducted on 4860 benchmark instances and the results obtained compare very favorably with those found by the best existing algorithms.