New and 'stronger' job-shop neighbourhoods: a focus on the method of Nowicki and Smutnicki (1996)

Anant Singh Jain, Balasubramanian Rangaswamy, Sheik Meeran

Research output: Contribution to journalArticlepeer-review

50 Citations (SciVal)

Abstract

Examination of the job-shop scheduling literature uncovers a striking trend. As methods for the deterministic job-shop problem have gradually improved over the years, they have come to rely on neighbourhoods for selecting moves that are more and more constrained. We document this phenomenon by focusing on the approach of Nowicki and Smutnicki (Management Science, 1996, 42(6), 797-813), noted for proposing and implementing the most restrictive neighbourhood in the literature. The Nowicki and Smutnicki (NS) method which exploits its neighbourhood by a tabu search strategy, is widely recognised as the most effective procedure for obtaining high quality solutions in a relatively short time. Accordingly, we analyse the contribution of the method's neighbourhood structure to its overall effectiveness. Our findings show, surprisingly, that the NS neighbourhood causes the method's choice of an initialisation procedure to have an important influence on the best solution the method is able to find. By contrast, the method's choice of a strategy to generate a critical path has a negligible influence. Empirical testing further discloses that over 99.7% of the moves chosen from this neighborhood (by the NS rules) are disimproving-regardless of the initial solution procedure or the critical path generation procedure employed. We discuss implications of these findings for developing new and more effective job-shop algorithms.

Original languageEnglish
Pages (from-to)457-480
Number of pages24
JournalJournal of Heuristics
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Sept 2000

Funding

This work has been sponsored by the Royal Society of Edinburgh and the Carneigie Trust for the Universities of Scotland while the first author was a visiting researcher at the University of Colorado, Boulder. The authors would like to thank Dr. Nowicki for providing us with his code from which we learnt a great deal, Professor Frank Werner and Dr. Thomas Tautenhahn for helping us greatly with our implementation of insert and the assistance provided by Professor Fred Glover in the preparation of this work which has greatly enhanced the quality of the paper.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Control and Systems Engineering
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'New and 'stronger' job-shop neighbourhoods: a focus on the method of Nowicki and Smutnicki (1996)'. Together they form a unique fingerprint.

Cite this