Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Palacios, Juan José; González-Rodríguez, Inés; Vela, Camino R.; Puente, Jorge de Hoyos

doi:10.1016/j.fss.2014.12.003

Cited by 37 publications

(20 citation statements)

References 58 publications

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“…Additionally, ≤ E coincides with several other ranking methods from the literature as highlighted in [47]. It is also possible to establish a relation with classical interval comparison in the light of imprecise probabilities, with ≤ E coming down to using Hurwicz criterion on upper and lower expectations derived from the fuzzy number [12].…”

Section: Ordering Of Tfnssupporting

confidence: 55%

Benchmarks for fuzzy job shop problems

Palacios

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: Ordering Of Tfnssupporting

confidence: 55%

Benchmarks for fuzzy job shop problems

Palacios

Puente

Vela

et al. 2016

Information Sciences

Self Cite

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“…Palacois et. al [50] suggested a random initialisation method based on the G&T algorithm. The following steps or phases generated initial solutions; operations were scheduled in an insertion mode by manipulating the flexibility, a reasonable insertion interval for an operation expressed by in a machine to be a dedicated time interval , where machine is in idle status and for that can be processed within that particular time interval without violating the preference constraints.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the centre of gravity of its mean value can be attained. As a series of researchers ( [33], [50], [56]- [57]) considered this criterion's detraction of fuzzy makespan ( ). Current study acknowledges this objective function as a crucial decision, which needs to be reached.…”

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confidence: 99%

Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Shaheed¹,

Shukor²,

Abdullah³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Scheduling job shops in the real-world manufacturing environment is a multifarious task that involved various and multiple components, solutions and approach. Fuzzy Job-Shop Scheduling Problems (Fuzzy JSSPs) are most commonly addressed by the population-based Meta-heuristic algorithms. These algorithms usually derive and develop near-optimum results within credible computational times, almost by two main steps; the initialisation and then improvement step. Numerous theoretical studies pointed out that a Meta-heuristic performance is mainly affected by the performance of its initialisation method. The main motivation of this work is to perceive the existing pattern and concerns on population initialisation issues for Fuzzy JSSPs current work by scrutinizing the published articles. Furthermore, this paper focusing on providing comprehension insight and future direction on these methods. Therefore, this paper determined to review and classify the existing literature on Fuzzy JSSPs and analyse the performance of the initialisation methods used to identify their possible limitations. In consequence, previous works outlined three potential methods for initial solutions generation, which are Random-based, priority rules-based, and heuristic methods. However, the current analysis showed that Heuristic-based initialisation approach remains lacking in the Fuzzy JSSPs domain in spite of its successful performance in the crisp JSSP domain, especially, its capability to generate high-quality initial population that consists of optimal or near optimal solutions. Furthermore, this paper identifies probable gaps and reveals several performance limitations in the existing methods, which demands for an urgent solution to develop alternatives. Promising suggestions for future studies are also provided that may lead to new Heuristic Initialisation methods that can be proposed to solve current weaknesses.

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“…They highlight that the use of the fuzzy modeling provides more flexibility and speed in computations. Palacios et al [33] also tackle a variant of the flexible job shop scheduling problem with uncertain task durations modeled as fuzzy numbers with different ranking methods for them. Wu et al [34] propose a fuzzy multi-objective programming model for the evaluation and selection of suppliers which provides alternative tools to improve supplier selection decisions in an uncertain global market.…”

Section: Background Literaturementioning

confidence: 99%

“…Palacios et al [33] also tackle a variant of the flexible job shop scheduling problem with uncertain task durations modeled as fuzzy numbers with different ranking methods for them. Wu et al [34] propose a fuzzy multi-objective programming model for the evaluation and selection of suppliers which provides alternative tools to improve supplier selection decisions in an uncertain global market. Liang [35] applies fuzzy goal programming to project management decisions with multiple goals in uncertain environments.…”

Section: Background Literaturementioning

confidence: 99%