Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76312
Title: A hybrid evolutionary approach for the single-machine total weighted tardiness problem
Authors: Ding, JW
Lu, ZP 
Cheng, TCE 
Xu, LP
Keywords: Heuristics
Single-machine total weighted tardiness
Hybrid evolutionary algorithm
Fast neighbourhood search
Buffer technique
Issue Date: 2017
Publisher: Pergamon Press
Source: Computers and industrial engineering, 2017, v. 108, p. 70-80 How to cite?
Journal: Computers and industrial engineering 
Abstract: This paper presents a hybrid evolutionary algorithm (HEA) for solving the single-machine total weighted tardiness problem, which incorporates several distinctive features such as a fast neighbourhood search and a buffer technique. HEA solves all the standard benchmark problem instances with 40, 50, and 100 jobs from the literature within 0.04 s. For larger instances with 150, 200, 250, and 300 jobs, HEA obtains the optimal solutions for all of them within four minutes. To the best of our knowledge, HEA is the only metaheuristic algorithm that can obtain the optimal solutions for all the 25 instances with 1000 jobs within an average time of 3.97 h, demonstrating the efficacy of HEA in terms of both solution quality and computational efficiency. Furthermore, some key features of HEA are analyzed to identify its critical success factors.
URI: http://hdl.handle.net/10397/76312
ISSN: 0360-8352
EISSN: 1879-0550
DOI: 10.1016/j.cie.2017.04.006
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Citations as of Nov 13, 2018

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
Citations as of Nov 12, 2018

Page view(s)

14
Last Week
1
Last month
Citations as of Nov 11, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.