Paper Info
Title
Minimization Of Makespan Through Jointly Scheduling Strategy In Production System With Mould Maintenance Consideration
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.
Year
DOI
Venue
2017
10.1007/978-3-319-63309-1_51
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I
Keywords
Field
DocType
Jointly scheduling, PSO, GA, Machine maintenance, Mould maintenance
Particle swarm optimization,Convergence (routing),Mathematical optimization,Job shop scheduling,Computer science,Scheduling (computing),Minification,Rate of convergence,Optimization problem,Genetic algorithm
Conference
Volume
ISSN
Citations 
10361
0302-9743
0
PageRank 
References 
Authors
0.34
8
5
Name
Order
Citations
PageRank
Xiaoyue Fu110.70
Felix T. S. Chan21267113.20
Ben Niu323544.62
Sai Ho Chung47014.18
Ying Bi5216.07