A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem

Zarrouk, Rim; Bennour, Imed Eddine; Jemai, Abderrazak

doi:10.1007/s11721-019-00167-w

Cited by 47 publications

(24 citation statements)

References 43 publications

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“…The GLNSA was compared with other nine algorithms, proposed between 2015 and 2020, to show the effectiveness of the proposed method. These algorithms are the efficient PSO and gravitational search algorithm (ePSOGSA) ( Bharti & Jain, 2020 ), the greedy randomized adaptive search procedure (GRASP) ( Baykasoğlu, Madenoğlu & Hamzaday, 2020 ), the HA ( Li & Gao, 2016 ), the improved Jaya algorithm (IJA) ( Caldeira & Gnanavelbabu, 2019 ), the self-learning GA (SLGA) ( Chen et al, 2020 ), the scatter search with PR algorithm (SSPR) ( González, Vela & Varela, 2015 ), the teaching-learning-based optimization (TLBO) ( Buddala & Mahapatra, 2019 ), the two-level PSO (TlPSO) ( Zarrouk, Bennour & Jemai, 2019 ), and the VNS-based GA (VNSGA) ( Zhang et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…Hybridization is continuously applied to propose new and improved algorithms for the FJSP ( Gao et al, 2019 ). One proposal is in Zarrouk, Bennour & Jemai (2019) , where a two-level PSO algorithm is presented and tested with 16 benchmark problems. Another GA with a VNS is explained in Zhang et al (2019) and proved with 13 well-known benchmark problems.…”

Section: State Of the Art Of Fjspmentioning

confidence: 99%

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A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Hybridization is continuously applied to propose new and improved algorithms for the FJSP (Gao et al, 2019). One proposal is in Zarrouk et al (2019), where a two-level PSO algorithm is presented and tested with 16 benchmark problems. Another GA with a VNS is explained in Zhang et al (2019) and proved with 13 well-known benchmark problems.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #2 of "A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem (v0.1)"

2021

View full text Add to dashboard Cite

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new meta-heuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli and Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using 4 benchmark sets and 101 test problems.

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“…The production management of HFS has a great practical significance [1,2]. Proper production management can improve the machine utilization, reduce the waiting time of jobs, rationalize the allocation of production resources, and lower job-shop production cost [3,4].…”

Section: Introductionmentioning

confidence: 99%

Production Management of Hybrid Flow Shop Based on Genetic Algorithm

Chen¹,

Zhao²

2021

Int. j. simul. model.

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The production management of hybrid flow shop (HFS) has a great practical significance. Proper production management can improve the machine utilization and shorten the makespan in a complex production control environment. However, the relevant research has not paid enough attention to realistic constraints like multi-period control, and job transport time. To solve the problem, this paper explores the production management of HFS based on improved genetic algorithm (GA). Specifically, several assumptions were proposed for the multi-objective optimization problem of HFS production management, and new constraints like multi-period control, and job transport time were introduced to the problem. Then, the authors established a multi-objective optimization model for HFS production management, and improved the traditional GA to solve the model more rapidly and accurately. The proposed model and algorithm were proved effective through experiments.

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A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem

Cited by 47 publications

References 43 publications

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Peer Review #2 of "A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem (v0.1)"

Production Management of Hybrid Flow Shop Based on Genetic Algorithm

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