Evolutionary algorithms for job-shop scheduling

Khaled Mesghouni; Slim Hammadi; Pierre Borne

International Journal of Applied Mathematics and Computer Science (2004)

  • Volume: 14, Issue: 1, page 91-103
  • ISSN: 1641-876X

Abstract

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This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).

How to cite

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Mesghouni, Khaled, Hammadi, Slim, and Borne, Pierre. "Evolutionary algorithms for job-shop scheduling." International Journal of Applied Mathematics and Computer Science 14.1 (2004): 91-103. <http://eudml.org/doc/207683>.

@article{Mesghouni2004,
abstract = {This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).},
author = {Mesghouni, Khaled, Hammadi, Slim, Borne, Pierre},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {job-shop scheduling; evolutionary algorithms; parallel representation},
language = {eng},
number = {1},
pages = {91-103},
title = {Evolutionary algorithms for job-shop scheduling},
url = {http://eudml.org/doc/207683},
volume = {14},
year = {2004},
}

TY - JOUR
AU - Mesghouni, Khaled
AU - Hammadi, Slim
AU - Borne, Pierre
TI - Evolutionary algorithms for job-shop scheduling
JO - International Journal of Applied Mathematics and Computer Science
PY - 2004
VL - 14
IS - 1
SP - 91
EP - 103
AB - This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).
LA - eng
KW - job-shop scheduling; evolutionary algorithms; parallel representation
UR - http://eudml.org/doc/207683
ER -

References

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Citations in EuDML Documents

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  1. Djamel Berkoune, Khaled Mesghouni, Besoa Rabenasolo, Lower bounds for the scheduling problem with uncertain demands
  2. Horacio González-Vélez, Maryam Kontagora, Performance evaluation of MapReduce using full virtualisation on a departmental cloud
  3. Joanna Kołodziej, Fatos Xhafa, Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids
  4. Robert Schaefer, Aleksander Byrski, Maciej Smołka, The island model as a Markov dynamic system
  5. Marlene Arangú, Miguel A. Salido, A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems
  6. Rafał Szłapczyński, Joanna Szłapczyńska, Customized crossover in evolutionary sets of safe ship trajectories

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