Greedy randomized adaptive search for dynamic flexible job-shop scheduling

Baykasoğlu, Adil; Madenoğlu, Fatma Selen; Hamzadayı, Alper

doi:10.1016/j.jmsy.2020.06.005

Cited by 74 publications

(18 citation statements)

References 59 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%

“…A fuzzy version of the FJSP is studied in Vela et al (2020) , where an evolutionary algorithm is proposed, using a TS again for optimizing a due-date cost. Dynamic flexibility in FJSP is analyzed in Baykasoğlu, Madenoğlu & Hamzaday (2020) with a greedy randomized adaptive search. The efficiency of the proposed algorithm is proved with three different sets of benchmark test problems.…”

Section: State Of the Art Of Fjspmentioning

confidence: 99%

See 1 more Smart Citation

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

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

Section: Resultsmentioning

confidence: 99%

“…A fuzzy version of the FJSP is studied in Vela et al (2020), where an evolutionary algorithm is proposed, using a TS again for optimizing a due-date cost. Dynamic flexibility in FJSP is analyzed in Baykasoglu et al (2020) with a greedy randomized adaptive search. The efficiency of the proposed algorithm is proved with 3 different sets of benchmark test 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.

show abstract

“…In general, single heuristics provide higher chances for near optimal solutions to escape local optima, which makes it more appropriate to be extensively utilized throughout a followed stage to the population based heuristics [68,85]. TS and SA used at JSSP are based as supplemented algorithms.…”

Section: Neighborhood Searching Algorithmmentioning

confidence: 99%

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Awad

Abd-Elaziz

2021

Processes

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In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

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Greedy randomized adaptive search for dynamic flexible job-shop scheduling

Cited by 74 publications

References 59 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)"

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

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