An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Gao, Kai; Suganthan, Ponnuthurai Nagaratnam; Pan, Quan-Ke; Chua, Tay Jin; Chong, Chin Soon; Cai, Tian Xiang

doi:10.1016/j.eswa.2016.07.046

Cited by 135 publications

(35 citation statements)

References 29 publications

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“…It has been a research hotspot for decades and many research works have been carried out. [2][3][4][5][6][7][8][9][10] In FJSP, operation-sequence-constraint and machine-resources-constraint are considered, but the constraints imposed by the workers are ignored. However, in reality, operations cannot be processed if workers are not available or if workers lack requisite skills.…”

Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker's learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a threedimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

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Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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“…A good overview of these methods can be found in [20,21]. Different variants of genetic algorithm [22][23][24], colony intellect method [25,26], ant colony [27], bee colony methods [28][29][30], and DE algorithm [31,32] have been proposed for solving the RCPSP problem.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Model for Optimal Planning of Information Security Incident Response Operations

İmamverdiyev¹

2018

JPIT

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A quick and adequate response to handling of information security incidents is critical for ensuring business continuity. To handle such incidents, special CERT commands are required, but the cost of maintaining them is a burden for most organizations, and they prefer to use the services of special CERT service providers. This study proposes a model for the optimal distribution of information security incident response operations between CERT groups; the model is formulated as an optimization problem, and differential evolution algorithm is developed to solve it.

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“…For assembly-line scheduling problem, there are swarm optimization algorithm [35], genetic algorithm [36], migratory bird optimization algorithm [37], hybrid algorithm [38,39], and so on. As for the current popular multi-variety and small-batch personalized customized production process, scheduling optimization is more widely applied and the corresponding research results are quite abundant [40][41][42][43]. However, these traditional scheduling methods consider separately processing and assembly, and the product is divided into multi jobs, the constraint relationships with the process are ignored.…”

Section: Introductionmentioning

confidence: 99%

The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

Dai

et al. 2019

Entropy

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In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process ∆H base when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach ∆H is used to evaluate the assembly risk by comparing with the ∆H base . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

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An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Cited by 135 publications

References 29 publications

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

A Model for Optimal Planning of Information Security Incident Response Operations

The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

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