Two meta-heuristics for solving a new two-machine flowshop scheduling problem with the learning effect and dynamic arrivals

Hosseini, N.; Tavakkoli–Moghaddam, Reza

doi:10.1007/s00170-012-4216-y

Cited by 20 publications

(3 citation statements)

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“…Heizer and Render [34] showed that the learning rate could range between 0.7 and 0.9 for di erent industries. Also, several studies, such as [35,36], considered the learning rate to be at the interval of [0:7 0:9]. In this paper, this rate is assumed to be 0.85.…”

Section: Computational Resultsmentioning

confidence: 99%

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

et al. 2016

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Abstract. One of the important aspects neglected in the literature related to cell formation problem is human issues. In this study, a bi-objective mathematical model is developed in which human issues and dynamic cell formation are taken into consideration simultaneously. The rst objective function deals with costs associated with machines and human issues. The costs of human issues relate to salary, hiring, ring, reward/penalty policy, and worker assignment. The second objective function takes into account labor utilization as a criterion for reward/penalty policy. Since the available time in di erent real conditions is not constant, we include learning e ect to consider the real workers time. The nature of dynamic cell formation problem is NP-hard, and thus a Linear Programming embedded Genetic Algorithm (LP-GA) is employed to solve the model. In order to improve the performance of the applied GA, its parameters are tuned by means of Central Composite Design (CCD) method. Moreover, to validate the LP-GA, some test problems are solved and the results are compared with those obtained from an exact method and GA. The computational results show that the near optimal solutions yielded by LP-GA are better than GA in large-sized problems.

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Section: Computational Resultsmentioning

confidence: 99%

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

et al. 2016

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“…Learning effects in scheduling are very popular, and researchers have modeled various kinds of scheduling problems with learning effects (e.g. see Biskup 1999Biskup , 2008Hosseini & Tavakkoli-Moghaddam, 2012;Mosheiov, 2001, Qian & Steiner, 2013. The first attempt to consider the learning effect in scheduling problems was by Biskup (1999) who proposed a kind of the learning effect in a single machine scheduling problem.…”

Section: Learning Effectmentioning

confidence: 99%

Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes

Amini

Tavakkoli–Moghaddam

Omidvar

2014

Production & Manufacturing Research

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“…Heuristic Hosseini and Tavakkoli-Moghaddam (2013) show an interesting variant of the twomachine problem with learning effects. They assume dynamic arrivals of jobs within a pre-specified time window.…”

Section: The Flow-shop Environmentmentioning

confidence: 99%

Heuristics and stochastic local search for just-in-time scheduling of parallel machines against common restrictive due windows

Rolim¹

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Two meta-heuristics for solving a new two-machine flowshop scheduling problem with the learning effect and dynamic arrivals

Cited by 20 publications

References 25 publications

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

A bi-objective mathematical model for dynamic cell formation problem considering learning eect, human issues, and worker assignment

Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes

Heuristics and stochastic local search for just-in-time scheduling of parallel machines against common restrictive due windows

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