Evolutionary Algorithm for Minmax Regret Flow-Shop Problem

Ćwik, Michał; Józefczyk, Jerzy

doi:10.1515/mper-2015-0021

Cited by 6 publications

(10 citation statements)

References 11 publications

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“…It can be recursively calculated [see e.g. in Pinedo (2008), and Ćwik and Józefczyk (2015) for details]. The optimal schedule is sought, i.e.…”

Section: Problem Statementmentioning

confidence: 99%

“…1983). The former one has been presented in Ćwik and Józefczyk (2015) and Józefczyk and Ćwik (2016) for the first time while the latter one together with the improved version of the evolutionary approach is given in this paper. Both algorithms are accompanied with procedures (auxiliary algorithms) responsible for solving SP1 and SP2.…”

Section: Heuristic Solution Algorithmsmentioning

confidence: 99%

“…The mutation consists of choosing randomly two positions in the permutation and swapping numbers occupying both positions. The full description of EVO can be found in Ćwik and Józefczyk (2015). Now, two improvements have been proposed in comparison with the presented there version.…”

Section: Heuristic Solution Algorithmsmentioning

confidence: 99%

“…The continuation of previous preliminary studies given in Ćwik and Józefczyk (2015) is proposed where the evolutionary algorithm has been presented for only three machines and the exact evaluation of the maximum regret as the criterion. Such a way of the criterion evaluation turned out impossible for more machines due to the time complexity.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic algorithms for the minmax regret flow-shop problem with interval processing times

Ćwik

Józefczyk

2017

Cent Eur J Oper Res

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An uncertain version of the permutation flow-shop with unlimited buffers and the makespan as a criterion is considered. The investigated parametric uncertainty is represented by given interval-valued processing times. The maximum regret is used for the evaluation of uncertainty. Consequently, the minmax regret discrete optimization problem is solved. Due to its high complexity, two relaxations are applied to simplify the optimization procedure. First of all, a greedy procedure is used for calculating the criterion’s value, as such calculation is NP-hard problem itself. Moreover, the lower bound is used instead of solving the internal deterministic flow-shop. The constructive heuristic algorithm is applied for the relaxed optimization problem. The algorithm is compared with previously elaborated other heuristic algorithms basing on the evolutionary and the middle interval approaches. The conducted computational experiments showed the advantage of the constructive heuristic algorithm with regards to both the criterion and the time of computations. The Wilcoxon paired-rank statistical test confirmed this conclusion.

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“…It can be recursively calculated [see e.g. in Pinedo (2008), and Ćwik and Józefczyk (2015) for details]. The optimal schedule is sought, i.e.…”

Section: Problem Statementmentioning

confidence: 99%

Section: Heuristic Solution Algorithmsmentioning

confidence: 99%

Section: Heuristic Solution Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heuristic algorithms for the minmax regret flow-shop problem with interval processing times

Ćwik

Józefczyk

2017

Cent Eur J Oper Res

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“…The case of the problem with only two tasks and m machines was presented in (Averbakh, 2006) where a lineartime algorithm is given. The evolutionary heuristic algorithm for the case of three machines was considered in (Ćwik and Józefczyk, 2015).…”

Section: Introductionmentioning

confidence: 99%

Heuristic Algorithm for Uncertain Permutation Flow-shop Problem

Józefczyk

Ćwik

2016

Proceedings of the 1st International Conference on Complex Information Systems

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A complex population-based solution algorithm for an uncertain decision making problem is presented. The uncertain version of a permutation flow-shop problem with interval execution times is considered. The worst-case regret based on the makespan is used for the evaluation of permutations of tasks. The resulting complex minmax combinatorial optimization problem is solved. The heuristic algorithm is proposed which is based on the decomposition of the problem into three sequential sub-problems and employs a paradigm of evolutionary computing. The proposed algorithm solves the sub-problems sequentially. It is compared with the fast middle point heuristic algorithm via computer simulation experiments. The results show the usefulness of this heuristic algorithm for instances up to five machines.

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