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Title: Minimization of makespan through jointly scheduling strategy in production system with mould maintenance consideration
Authors: Fu, X 
Chan, FTS 
Niu, B
Chung, SH 
Bi, Y
Keywords: GA
Jointly scheduling
Machine maintenance
Mould maintenance
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10361, p. 577-586 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Job shop scheduling problem with machine maintenance has attracted the attention of many scholars over the past decades. However, only a limited number of studies investigate the availability of injection mould which is important to guarantee the regular production of plastic industry. Furthermore, most researchers only consider the situation that the maintenance duration and interval are fixed. But in reality, maintenance duration and interval may vary based on the resource age. This paper solves the job shop scheduling with mould maintenance problem (JSS-MMP) aiming at minimizing the overall makespan through a jointly schedule strategy. Particle Swarm Optimization Algorithm (PSO) and Genetic Algorithm (GA) are used to solve this optimization problem. The simulation results show that under the condition that the convergence time of two algorithms are similar, PSO is more efficient than GA in terms of convergence rate and solution quality.
Description: 13th International Conference on Intelligent Computing, ICIC 2017, 7 - 10 August 2017
ISBN: 9783319633084
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-63309-1_51
Appears in Collections:Conference Paper

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