Complete local search with limited memory algorithm for no-wait job shops to minimize makespan Zhu, Jie; Li, Xiaoping; Wang, Qian

User activity

Share to:
View the summary of this work
Authors
Zhu, Jie ; Li, Xiaoping ; Wang, Qian
Appears In
European Journal of Operational Research
Subjects
Computer Science; Algorithms; Business, general
Audience
Academic; Trade
Summary
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.09.015 Byline: Jie Zhu, Xiaoping Li, Qian Wang Keywords: No-wait; Job shop; Timetabling; Sequencing Abstract: In this paper, no-wait job shop problems with makespan minimization are considered. It is well known that these problems are strongly NP-hard. The problem is decomposed into the sequencing and the timetabling components. Shift timetabling is developed for the timetabling component. An effective method, CLLM (complete local search with limited memory), is presented by integrating with shift timetabling for the sequencing component. Experimental results show that CLLM outperforms all the existing effective algorithms for the considered problem with a little more computation time. Author Affiliation: School of Computer Science and Engineering, Southeast University, 210096 Nanjing, PR China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, 210096 Nanjing, PR China Article History: Received 19 October 2007; Accepted 16 September 2008
Bookmark
http://trove.nla.gov.au/work/9831
Work ID
9831

2 editions of this work

Find a specific edition
Thumbnail [View as table] [View as grid] Title, Author, Edition Date Language Format Libraries

User activity


e.g. test cricket, Perth (WA), "Parkes, Henry"

Separate different tags with a comma. To include a comma in your tag, surround the tag with double quotes.

Be the first to add a tag for this work

Be the first to add this to a list

Comments and reviews

What are comments? Add a comment

No user comments or reviews for this work

Add a comment


Show comments and reviews from Amazon users