Metaheuristic methods in hybrid flow shop scheduling problem

Choong, F.; Phon-Amnuaisuk, Somnuk; Alias, Mohamad Yusoff

doi:10.1016/j.eswa.2011.01.173

Cited by 21 publications

(17 citation statements)

References 21 publications

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“…They proposed an improved cuckoo search algorithm for this problem. Choong, Phon-Amnuaisuk, and Alias [14] proposed two hybrid algorithms based on PSO, SA, and TS for the FFS scheduling problem. Chung and Liao [15] considered an FFS scheduling problem.…”

Section: Literature Reviews 21 Metaheuristic Algorithms For Flexiblementioning

confidence: 99%

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Flexible flow shop scheduling problem to minimize makespan with renewable resources

2019

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This paper deals with a flexible flow shop (FFS) scheduling problem with unrelated parallel machines and renewable resource shared among the stages. The FFS scheduling problem is one of the most common manufacturing environment in which there is more than a machine in at least one production stage. In such a system, to decrease the processing times, additional renewable resources are assigned to the jobs or machines, and it can lead to decrease the total completion time. For this purpose, a mixed integer linear programming (MILP) model is proposed to minimize the maximum completion time (makespan) in an FFS environment. The proposed model is computationally intractable. Therefore, a particle swarm optimization (PSO) algorithm as well as a hybrid PSO and simulated annealing (SA) algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large size test problems.

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Section: Literature Reviews 21 Metaheuristic Algorithms For Flexiblementioning

confidence: 99%

“… ), cannot exceed the maximum renewable resources in stage t . Finally, the constraint sets (12)-(14) show the range of the decision variables.…”

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confidence: 99%

Flexible flow shop scheduling problem to minimize makespan with renewable resources

2019

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“…Test results show that the proposed ATPPSO algorithm is a valuable and effective PSO model for solving complex optimization problems. Choong et al [2] proposed two hybrid heuristic algorithms that combine particle swarm optimization with simulated annealing (SA) and tabu search. Experimental results demonstrated that the method solves the problem with efficiency and produced improved solutions over conventional methods with faster convergence.…”

Section: Introductionmentioning

confidence: 99%

Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

2014

Applied Soft Computing

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“…For example, Wang et al (2011) have used a local search technique to simulated annealing (SA) to improve its efficiency while solving FFSP. Choong et al (2011) have proposed hybrid algorithms combining tabu search (TS) and simulated annealing (SA) to particle swarm optimization (PSO) to improve efficiency of PSO in solving FFSP. Liao et al (2012) have used a local search technique to PSO.…”

Section: Introductionmentioning

confidence: 99%

Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

Buddala

Mahapatra

2017

J Ind Eng Int

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Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having 'g' operations is performed on 'g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NPhard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain nearoptimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.Keywords Flexible flow shop Á JAYA algorithm Á Makespan Á Meta-heuristics Á Teaching-learning-based optimization J Ind Eng Int (2018) 14:555-570 https://doi.

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Metaheuristic methods in hybrid flow shop scheduling problem

Cited by 21 publications

References 21 publications

Flexible flow shop scheduling problem to minimize makespan with renewable resources

Flexible flow shop scheduling problem to minimize makespan with renewable resources

Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

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