MetaCartSign in to MyCiteSeer

Include Citations | Advanced Search | Help

Include Citations | Advanced Search | Help

  . Sheik Meeran

Download:
Download as a PDF | Download as a PS
by Anant Singh Jain, Balasubramanian Rangaswamy
http://www.dundee.ac.uk/~asjain/papers/joh2a.ps
Add To MetaCart

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 with a historical sketch of job-shop neighborhoods, which leads us to focus particularly on the approach of Nowicki and Smutnicki (1996), 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. 09/10/98 New and "stronger " job-shop neighbourhoods: A focus on the method of NS (1996) page (2)

Citations

354 Tabu search – Glover, Laguna - 1995
302 Performance of Various Computers Using Standard Linear Equations Software, (Linpack Benchmark Report – Dongarra - 1998
283 Greedy Randomized Adaptive Search Procedure – Feo, Resende - 1995
224 Introduction to Sequencing and Scheduling – Baker - 1974
81 A Fast Taboo Search Algorithm for the Job Shop Problem – Nowicki, Smutnicki - 1996
70 Probabilistic learning combinations of local job-shop scheduling rules – Fisher, Thompson - 1963
65 Applying tabu search to the job shop scheduling problem – M, Turbian - 1993
65 Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (supplement – Lawrence - 1984
64 Adjustment of heads and tails for the job-shop problem – Carlier, Pinson - 1994
53 Parallel taboo search technique for the job shop scheduling problem. Research Report ORWP 89/11, Ecole Polytechnique Federal de Lausanne, Department de Mathematiques – Taillard - 1989
51 A Survey of Scheduling Rules – Panwalkar, Iskander - 1977
50 Job shop scheduling by simulated annealing – Laarhoven, Aarts, et al. - 1992
50 Algorithms for solving production scheduling problems – Giffler, Thompson - 1960
45 A branch and bound algorithm for the job-shop scheduling problem – Brucker, Jurisch
42 Deterministic job shop scheduling: Past, present and future – Jain, Pearson, et al. - 1999
41 Job Shop Scheduling by Local Search – Vaessens, Aarts, et al. - 1996
35 Guided Local Search with Shifting Bottleneck for Job Shop Scheduling – Balas, Vazacopoulos - 1998
26 A Controlled Search Simulated Annealing Method for the General Jobshop Scheduling Problem – Matsuo, Suh, et al. - 1988
26 Sequencing and Scheduling: An Introduction to the – French - 1982
22 Combining the large-step optimization with tabu-search: Application to the job-shop scheduling problem – Lourenço, Zwijnenburg - 1996
16 Evolutionary Search and the Job Shop: Investigations on Genetic Algorithms for Production Scheduling – Mattfeld - 1996
14 Some new results on simulated annealing applied to the job shop scheduling problem – Kolonko - 1999
12 Benchmarks for shop scheduling problems – Demirkol, Mehta, et al. - 1998
9 Scheduling Resource-Constrained Projects Competitively at Modest Memory Requirements – Sprecher - 2000
8 Machine Scheduling via Disjunctive Graphs: An Implicit Enumeration Algorithm – Balas - 1969
8 A heuristic algorithm for the m-machine n-job flow-shop sequencing problem – Ham - 1983
7 A block approach for single machine scheduling with release dates and due dates – Grabowski, Nowicki, et al. - 1986
7 A Multi-Level Hybrid Framework for the Deterministic Job-Shop Scheduling problem – Jain - 1998
6 Ranking Dispatching Rules by Data Envelopment Analysis in a Job-Shop Environment – Chang, Sueyoshi, et al. - 1996
5 A Beam Search Based Algorithm for the Job Shop Scheduling Problem, Research Report – Sabuncuoglu, Bayiz - 1997
4 Meta-heuristics Combined with Branch & Bound – Thomsen - 1997
3 The Job-Shop Scheduling Problem – Blazewicz, Domschke, et al. - 1996
1 Block Algorithm for Scheduling Operations – Grabowski, Nowicki, et al. - 1988
1 stronger" job-shop neighbourhoods: A focus on the method of NS – New - 1996
1 Job Shop Scheduling by Simulated Annealing, Report OS-R8809, Centrum voor Wiskunde en Informatica – Laarhoven, Aarts, et al. - 1988