An improved genetic algorithm for single-machine inverse scheduling problem
type: Item
instance of: scholarly article
title: An improved genetic algorithm for single-machine inverse scheduling problem
zbMATH Open document ID: 1407.90369
DOI: 10.1155/2014/370560
author: Jianhui Mou
author: Xinyu Li
author: Liang Gao
author: Chao Lu
author: Guo-Hui Zhang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP) is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA) with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90B35
zbMATH DE Number: 7016282
Wikidata QID: Q59064731
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/370560
OpenAlex ID: W2154352383
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description: scientific article; zbMATH DE number 7016282