Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

Puente, Jorge de Hoyos; Vela, Camino R.; Hernández-Arauzo, Alejandro; González-Rodríguez, Inés

doi:10.1007/978-3-642-14264-2_23

Current Topics in Artificial Intelligence

2010

DOI: 10.1007/978-3-642-14264-2_23

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Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

Jorge de Hoyos Puente

Camino R. Vela

Alejandro Hernández-Arauzo

et al.

Abstract: Abstract. We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results ill… Show more

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“…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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mentioning

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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show abstract

Fuzzy job-shop scheduling problems: A review

Abdullah

Abdolrazzagh-Nezhad

2014

Information Sciences

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A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling

Sun

Lin

Gen

et al. 2019

IEEE Trans. Fuzzy Syst.

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Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

Cited by 3 publications

References 15 publications

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

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

Fuzzy job-shop scheduling problems: A review

A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling

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