A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion

Grabowski, J.; Wodecki, Mieczysław

doi:10.1016/s0305-0548(03)00145-x

Cited by 251 publications

(94 citation statements)

References 14 publications

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“…Genetic algorithms for solving the FSP have also appeared in Chen et al (1995), Reeves (1995), Wang and Zheng (2003), and Aldowaisan and Allahvedi (2003). Other algorithms are the path-based method of Werner (1993), the iterated local search of Stützle (1998), two very effective ant-colony optimization algorithms by Rajendran and Ziegler (2004), and a fast Tabu search approach of Grabowski and Wodecki (2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)

Gupta

Kumar

2016

Int. J. Prod. Manag. Eng.

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Abstract:The flow-shop scheduling problem (FSP) has been widely studied in the literature and having a very active research area. Over the last few decades, a number of heuristic/meta-heuristic solution techniques have been developed. Some of these techniques offer excellent effectiveness and efficiency at the expense of substantial implementation efforts and being extremely complicated. This paper brings out the application of a Multi-Criteria Decision Making (MCDM) method known as techniques for order preference by similarity to an ideal solution (TOPSIS) using different weighting schemes in flow-shop environment. The objective function is identification of a job sequence which in turn would have minimum makespan (total job completion time). The application of the proposed method to flow shop scheduling is presented and explained with a numerical example. The results of the proposed TOPSIS based technique of FSP are also compared on the basis of some benchmark problems and found compatible with the results obtained from other standard procedures.

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Section: Literature Reviewmentioning

confidence: 99%

Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)

Gupta

Kumar

2016

Int. J. Prod. Manag. Eng.

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“…Tabu search has been successfully applied to a wide range of applications, and seems to be unrivaled in efficacy for a variety of scheduling problems. See, for example Nowicki and Smutnicki (1996) and Grabowski and Wodecki (2004). For other metaheuristic approaches in scheduling problems we refer the reader to Tan et al (2007), for example, and the references therein.…”

Section: Using Tabu Searchmentioning

confidence: 99%

A tabu search tutorial based on a real-world scheduling problem

Schneider

2010

Cent Eur J Oper Res

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We apply a tabu search method to a scheduling problem of a company producing cables for cars: the task is to determine on what machines and in which order the cable jobs should be produced in order to save production costs. First, the problem is modeled as a combinatorial optimization problem. We then employ a tabu search algorithm as an approach to solve the specific problem of the company, adapt various intensification as well as diversification strategies within the algorithm, and demonstrate how these different implementations improve the results. Moreover, we show how the computational cost in each iteration of the algorithm can be reduced drastically from O(n 3 ) (naive implementation) to O(n) (smart implementation) by exploiting the specific structure of the problem (n refers to the number of cable orders).

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“…The flow shop problem can be defined as follows, using the notation of Nowicki, Smutnicki [1] and Grabowski, Wodecki [2] as a proper one for the problem considered. There are: a set of n jobs J = {1,2,...,«} and a set of m machines M = {1,2, ...,m}.…”

Section: The Flow Shop Problemmentioning

confidence: 99%