Some heuristics for the hybrid flow shop scheduling problem with setup and assembly operations

Hosseini, Seyedmohsen; Jolai, Fariborz

doi:10.5267/j.ijiec.2013.03.004

Cited by 9 publications

(9 citation statements)

References 37 publications

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“…Also, they proposed a new solution representation method to solve the FAJSS problem using the proposed algorithm. Fattahi et al (2013a, 2013b) studied an assembly flexible flow shop scheduling problem. They presented a mathematical model with the objective of minimizing the completion time of all products (makespan).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These authors proposed the artificial immune system algorithm (AIS), heuristic algorithms and hybrid improved artificial immune system (HAIS). They presented a lower bound and compared the performance of proposed LB with the proposed LB by Fattahi et al (2013a, 2013b). The results show the proposed LB outperforms the proposed LB by Fattahi et al (2013a, 2013b).…”

Section: Literature Reviewmentioning

confidence: 99%

“…They presented a lower bound and compared the performance of proposed LB with the proposed LB by Fattahi et al (2013a, 2013b). The results show the proposed LB outperforms the proposed LB by Fattahi et al (2013a, 2013b). Fattahi et al (2013a, 2013b) studied a hybrid flow shop scheduling problem scheduling problem with setup followed by an assembly stage.…”

Section: Literature Reviewmentioning

confidence: 99%

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A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Fattahi

Rad

Daneshamooz

et al. 2020

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Purpose The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced. Design/methodology/approach As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes. Findings Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms. Practical implications Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper. Originality/value This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Fattahi

Rad

Daneshamooz

et al. 2020

Self Cite

View full text Add to dashboard Cite

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“…The heuristics proposed by Fattahi et al (2013b) are outperformed by a metaheuristic proposed by Komaki et al (2016), who also propose some constructive heuristics for the problem. Two extensions of the previous problem are presented by Fattahi et al (2013a) and Fattahi et al (2014). The first reference proposes metaheuristics for the case with machine dependent setups and product-batch restrictions, whereas in the second reference a branch and bound and a metaheuristic are proposed for the problem with machine dependent setups.…”

Section: Flowshopmentioning

confidence: 99%

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

Framiñán

2019

European Journal of Operational Research

104

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Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.

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“…Pour et al (2013) presented an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. Fattahi et al (2013) presented a two-stage hybrid flow shop scheduling problem with setup and assembly operations. A combinatorial algorithm is proposed using heuristic, genetic algorithm (GA), simulated annealing (SA), NEH and Johnson's algorithm to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

A heuristic algorithm for scheduling in a flow shop environment to minimize makespa

Gupta¹,

Chauhan²

2015

10.5267/j.ijiec

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Scheduling 'n' jobs on 'm' machines in a flow shop is NP-hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of machines at each stage to generate different combination of 'm-1' sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993) [Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.]. The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer's heuristic, Campbell's CDS heuristic, and Dannenbring's rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.

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Some heuristics for the hybrid flow shop scheduling problem with setup and assembly operations

Cited by 9 publications

References 37 publications

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

A heuristic algorithm for scheduling in a flow shop environment to minimize makespa

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