Comparing three-step heuristics for the permutation flow shop problem

Ribas, Imma; Companys, Ramón; Tort‐Martorell, Xavier

doi:10.1016/j.cor.2010.02.006

Cited by 55 publications

(34 citation statements)

References 27 publications

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“…The experimental results showed that IG was the best‐performing approach. Some other recent works relating the ILS method to the PFSP can be found in other literatures (Burke et al., ; Pan et al., ; Ravetti et al., ; Ribas et al., ). However, to the best of our knowledge, IG has performed better than any other algorithm (ILS‐based or not) in all PFSP articles using Taillard's benchmarks.…”

Section: Literature Review On the Pfspmentioning

confidence: 99%

Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues

Juan

Lourenço

Mateo

et al. 2013

Int Trans Operational Res

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Iterated local search (ILS) is a powerful framework for developing efficient algorithms for the permutation\ud flow-shop problem (PFSP). These algorithms are relatively simple to implement and use very few parameters,\ud which facilitates the associated fine-tuning process. Therefore, they constitute an attractive solution for real-\ud life applications. In this paper, we discuss some parallelization, parametrization, and randomization issues\ud related to ILS-based algorithms for solving the PFSP. In particular, the following research questions are\ud analyzed: (a) Is it possible to simplify even more the parameter setting in an ILS framework without affecting\ud performance? (b) How do parallelized versions of these algorithms behave as we simultaneously vary the\ud number of different runs and the computation time? (c) For a parallelized version of these algorithms, is it\ud worthwhile to randomize the initial solution so that different starting points are considered? (d) Are these\ud algorithms affected by the use of a “good-quality” pseudorandom number generator? In this paper, we\ud introduce the new ILS-ESP (where ESP is efficient, simple, and parallelizable) algorithm that is specifically\ud designed to take advantage of parallel computing, allowing us to obtain competitive results in “real time”\ud for all tested instances. The ILS-ESP also uses “natural” parameters, which simplifies the calibration process.\ud An extensive set of computational experiments has been carried out in order to answer the aforementioned\ud research questionsPeer ReviewedPostprint (published version

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Section: Literature Review On the Pfspmentioning

confidence: 99%

Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues

Juan

Lourenço

Mateo

et al. 2013

Int Trans Operational Res

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“…Several works address the problem of breaking the ties of the partial makespans when inserting a job, such as e.g. Ribas, Companys, and Tort-Martorell (2010) , Kalczynski and Kamburowski (2007) , Kalczynski and Kamburowski (2008) , Kamburowski (2009) , Dong, Huang, andChen (2008) and FernandezViagas and Framinan (2014) .…”

Section: Literature Reviewmentioning

confidence: 99%

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimise total flowtime

Fernández-Viagas

Leisten

2016

Expert Systems with Applications

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“…CSP has been analysed in many references (some recent works are Ribas et al, 2010;Tzeng and Chen, 2012;Fernandez-Viagas and Framinan, 2014 Since the common due date of jobs in J O is a key parameter for M SP , Section 3 analyses the different methods available in the literature to generate the common due date of jobs in J O , in order to provide a realistic due date for our problem.…”

Section: Problem Statementmentioning

confidence: 99%

Assessing scheduling policies in a permutation flowshop with common due dates

González

2014

International Journal of Production Research

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This paper focuses onto a situation arising in most real-life manufacturing environments when scheduling has to be performed periodically. In such scenario, different scheduling policies can be adopted, being perhaps the most common to assume that, once a set of jobs has been scheduled, their schedule cannot be modified ('frozen' schedule). This implies that, when the next set of jobs is to be scheduled, the resources may not be fully available. Another option is assuming that the schedule of the previously scheduled jobs can be modified as long as it does not violate their due date, which has been already possibly committed to the customer.This policy leads to a so-called multi-agent scheduling problem. The goal of this paper is to discern when each policy is more suitable for the case of a permutation flowshop with common due dates. To do so, we carry out an extensive computational study in a testbed specifically designed to control the main factors affecting the policies, so we analyze the solution space of the underlying scheduling problems.The results indicate that, when the due date of the committed jobs is tight, the multi-agent approach does not pay off in view of the difficulty of finding feasible * This is an Accepted Manuscript of an article published by Taylor and Francis in contrast, when the due date has a medium/high slack, the multi-agent approach is substantially better. Nevertheless, in this latter case, in order to perceive the full advantage of this policy, powerful solution procedures have to be designed, as the structure of the solution space of the latter problem makes extremely hard to find optimal/good solutions.

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Comparing three-step heuristics for the permutation flow shop problem

Cited by 55 publications

References 27 publications

Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues

Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimise total flowtime

Assessing scheduling policies in a permutation flowshop with common due dates

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