New and "Stronger" Job-Shop Neighbourhoods: A Focus on the Method of Nowicki and Smutnicki (1996 (original) (raw)

The strategies and parameters of tabu search for job-shop scheduling

Journal of Intelligent Manufacturing, 2004

This paper presents a tabu search approach for the job-shop scheduling problem. Although the problem is NP-hard, satisfactory solutions have been obtained recently by tabu search. However, tabu search has a problem-specific and parametric structure. Therefore, in the paper, we focussed on the tabu search strategies and parameters such as initial solution, neighborhood structure, tabu list, aspiration criterion, elite solutions list, intensification, diversification and the number of iteration. In order to compare some neighborhood strategies and tabu list length methods, a computational study is done on the benchmark problems.

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

PeerJ Computer Science

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets an...

A synergetic combination of small and large neighborhood schemes in developing an effective procedure for solving the job shop scheduling problem

SpringerPlus, 2014

This paper presents an effective procedure for solving the job shop problem. Synergistically combining small and large neighborhood schemes, the procedure consists of four components, namely (i) a construction method for generating semi-active schedules by a forward-backward mechanism, (ii) a local search for manipulating a small neighborhood structure guided by a tabu list, (iii) a feedback-based mechanism for perturbing the solutions generated, and (iv) a very large-neighborhood local search guided by a forward-backward shifting bottleneck method. The combination of shifting bottleneck mechanism and tabu list is used as a means of the manipulation of neighborhood structures, and the perturbation mechanism employed diversifies the search. A feedback mechanism, called repeat-check, detects consequent repeats and ignites a perturbation when the total number of consecutive repeats for two identical makespan values reaches a given threshold. The results of extensive computational exper...

A REVIEW ON NON TRADITIONAL ALGORITHMS FOR JOB SHOP SCHEDULING

iaeme

A great deal of research has been focused on solving the job-shop problem, over the last fifty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve job shop scheduling problem. JSSP is stated as a NP Hard problem [36, 37] so that as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine the specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive. In addition this work suggests guide-lines on features that should be incorporated to create a good job shop scheduling system. Finally the possible direction for future work is highlighted so that current barriers within job shop scheduling problem may be surmounted as we approach the 21st Century.