A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion

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

doi:10.1016/j.eswa.2015.06.004

Cited by 170 publications

(51 citation statements)

References 35 publications

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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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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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“…Many methods have been presented to solve the FJSP problem, e.g. exact methods [6,5,1], and heuristic methods [15,17,7,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

An extended flexible job shop scheduling problem with parallel operations

Lunardi

Voos

2018

SIGAPP Appl. Comput. Rev.

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Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these techniques. Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines. In this paper, we consider the FJSP with parallel operations (EFJSP) and we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the problem. Several FJSP and EFJSP instances were used to evaluate the performance of the proposed algorithms. Comparisons among our methods and state-of-the-art algorithms are also provided. The experimental results demonstrate that the FA and GA achieved improvements in terms of efficiency and efficacy. Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search. In addition, based on our initial experiments, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than our proposed genetic algorithm for the considered problem.

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“…Authors considered time as main QoS factor. Algorithm produced best total task finishing time, mean task finishing time and load balancing time as compared with ACO.In [23], authors proposed Two-stage Artificial Bee Colony (TABC) algorithm for scheduling andrescheduling with new job(s) inserting. The algorithm is compared with six simple heuristics and proposed hybrid heuristics.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%