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Erschienen in: Neural Computing and Applications 1/2017

21.05.2016 | Original Article

Local search algorithm with path relinking for single batch-processing machine scheduling problem

verfasst von: Xin Zhang, Xiangtao Li, Jianan Wang

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

The single batch-processing machine problem is to minimize makespan, which has broad applications, including engineering fundamentals and theoretical background. The machine can process several jobs as a batch simultaneously, and every job has three different attributes: job size, processing time, and job arrival. In this paper, a hybrid local search algorithm with path relinking (LP) is devised to solve the problem. We not only generate an optimal initial solution firstly but also pay more attention to improving the solution quality. Three kinds of local searches are applied to enhance the diversity of solutions; the path relinking is adopted to explore better solutions through local tracks connecting the current solution and the best in the elite set. With these approaches, it can keep a balanced rate between exploratory and exploitative capacity. Computational experiments on 40 benchmark instances indicate that LP has the superior convergence and robust performance and it surpasses the current state-of-the-art methods such as genetic algorithm and ant colony optimization, especially for large instances.

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Metadaten
Titel
Local search algorithm with path relinking for single batch-processing machine scheduling problem
verfasst von
Xin Zhang
Xiangtao Li
Jianan Wang
Publikationsdatum
21.05.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2339-z

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