Paper Info
Title
A Hybrid Grey Wolf Optimization For Job Shop Scheduling Problem
Abstract
This paper aims to develop a hybrid grey wolf optimization algorithm (HGWO) for solving the job shop scheduling problem (JSP) with the objective of minimizing the makespan. Firstly, to make the GWO suitable for the discrete nature of JSP, an encoding mechanism is proposed to implement the continuous encoding of the discrete scheduling problem, and a ranked-order value (ROV) rule is used to conduct the conversion between individual position and operation permutation. Secondly, a heuristic algorithm and the random rule are combined to implement the population initialization in order to ensure the quality and diversity of initial solutions. Thirdly, a variable neighborhood search algorithm is embedded to improve the local search ability of our algorithm. In addition, to further improve the solution quality, genetic operators (crossover and mutation) are introduced to balance the exploitation and exploration ability. Finally, experimental results demonstrate the effectiveness of the proposed algorithm based on 23 benchmark instances.
Year
DOI
Venue
2018
10.1142/S1469026818500165
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS
Keywords
DocType
Volume
Job shop scheduling, makespan, hybrid grey wolf optimization, variable neighborhood search, genetic operator
Journal
17
Issue
ISSN
Citations 
3
1469-0268
1
PageRank 
References 
Authors
0.35
9
1
Name
Order
Citations
PageRank
Tianhua Jiang174.48