Fuzzy job-shop scheduling problems: A review

Abdullah, Salwani; Abdolrazzagh-Nezhad, Majid

doi:10.1016/j.ins.2014.03.060

Cited by 81 publications

(52 citation statements)

References 76 publications

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“…Similar results on the same instances for GPSO, RKGA and a new ant bee colony (ABC) algorithm are given in [71]. Notice that these are the instances referred to as "Lei's" in [1], since the same original instances and fuzzification method is used by one of the authors in [39]; however, the results reported in this late paper correspond to a variant of FJSP considering the additional constraint of preventive maintenance, together with the corresponding additional data for the benchmark instances. HDPSO [41] (max R , < R ) Y RKGA [36] (max R , < R ) MA1 [25] (max I , < E ) HDPSO [41] (max R , < R ) Y RKGA [36] (max R , < R ) MA1 [25] (max I , < E )…”

Section: Fuzzified Instancesmentioning

confidence: 63%

“…A first review of benchmark datasets for fuzzy job shop is already included in [1]; here we elaborate on the information given in that survey and complement it with data that allows for fair and rigorous comparisons of different proposals from the literature. We shall analyse the results reported for those instances, taking into account both the objective function considered in each case as well as the fuzzy arithmetic used (maximum approximation and order relation), this being a key issue for meaningful comparisons.…”

Section: Review Of Existing Benchmarksmentioning

confidence: 99%

“…Finally, not all instances reviewed in [1] are included in Table 1 and in the posterior analysis. This is either because the instances are not available (directly or via a fuzzification method) as those from [33] or [50], or because they do not correspond to the standard FJSP as presented in Section 2, but to variants thereof.…”

Section: Fuzzified Instancesmentioning

confidence: 99%

“…There are in fact numerous research papers where uncertain durations are represented as fuzzy numbers, mostly triangular fuzzy numbers, among others, for single machine scheduling [10], [32], parallel machine scheduling [4,49], flow shop scheduling [9,31], open shop scheduling [46], job shop scheduling [18,24,35,45,52,54,55,60,71] and [41], and for flexible job shop scheduling in [40,48,64,65] to mention but a few. Indeed, the recent review of metaheuristic algorithms to solve fuzzy job shop problems in [1] highlights the relevance of this topic. Additionally, fuzzy numbers and, in general, fuzzy intervals, can be linked with traditional intervals [20], which constitute an alternative approach to modelling uncertain durations [21].…”

Section: Introductionmentioning

confidence: 99%

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Benchmarks for fuzzy job shop problems

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Puente

Vela

et al. 2016

Information Sciences

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Highlights• The fuzzy job shop scheduling problem with makespan minimization is revised• We survey existing metaheuristic methods and test beds for the problem • More challenging benchmark instances with LBs and competitive results are proposed• We publish old and new test beds to facilitate reproducibility and future research 1 AbstractThe fuzzy job shop scheduling problem with makespan minimisation is a problem with a significant presence in the scientific literature. However, a common meaningful comparison base is missing for such problem. This work intends to fill the gap in this domain by reviewing existing benchmarks as well as proposing new benchmark problems. First, we shall survey the existing test beds for the fuzzy job shop, analysing whether they are sufficiently varied and, most importantly, whether there is room for improvement on these instances -an essential requirement if the instances are to be useful for the scientific community in order to compare and develop new solving strategies. In the light of this analysis, we shall propose a new family of more challenging benchmark problems and provide lower bounds for the expected makespan of each instance as well as reference makespan values obtained with a memetic algorithm from the literature. The resulting benchmark will be made available so as to facilitate experiment reproducibility and encourage research competition.

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Section: Fuzzified Instancesmentioning

confidence: 63%

Section: Review Of Existing Benchmarksmentioning

confidence: 99%

Section: Fuzzified Instancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Benchmarks for fuzzy job shop problems

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Puente

Vela

et al. 2016

Information Sciences

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“…This model has been extensively used in the fuzzy scheduling literature [1]. However, when TFNs are to be used to extend the job shop to handle uncertainty, two issues must be addressed: the establishment of an order relation between TFNs and the means of extending the arithmetic operations of addition and maximum to work with TFNs (so to compute starting and completion times).…”

Section: Uncertain Durationsmentioning

confidence: 99%

On Maintaining Diversity in MOEA/D

Alonso

Derbel

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Moea/d is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this paper, we consider applying Moea/d to solve a bi-objective scheduling combinatorial problem in which task durations and due-dates are uncertain. Surprisingly, we find that the conventional Moea/d implementation provides poor performance in our application setting. We show that this is because the replacement strategy underlying Moea/d is suffering some shortcomes that lead to low population diversity, and thus to premature convergence. Consequently, we investigate existing variants of Moea/d and we propose a novel and simple alternative replacement component at the aim of maintaining population diversity. Through extensive experiments, we then provide a comprehensive analysis on the relative performance and the behavior of the considered algorithms. Besides being able to outperform existing Moea/d variants, as well as the standard Nsga-ii algorithm, our investigations provide new insights into the search ability of Moea/d and highlight new research opportunities for improving its design components.

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