Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem

Luan, Fei; Cai, Zongyan; Wu, Shuqiang; Jiang, Tianhua; Li, Fukang; Jiang, Yang

doi:10.3390/math7050384

Cited by 37 publications

(27 citation statements)

References 39 publications

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“…Another GA with a VNS is explained in Zhang et al (2019) and proved with 13 well-known benchmark problems. Another modified metaheuristic is presented in Luan et al (2019) , where the whale algorithm is adapted for the FJSP and proved with 15 test problems. A HA that combines PSO and TS is described in Toshev (2019) and the performance of the algorithm is analyzed in 12 benchmark problems.…”

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

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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.

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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

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show abstract

“…Whale optimization is a new swarm-based algorithm which mimics the hunting behavior of humpback whales in nature. It has also been successfully used to solve jobshop problems [22], as well flexible ones [23], with energy efficiency concerns [22,24]. We took particular interest in the particle swarm optimization method (PSO).…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Sauvey

Trabelsi²,

Sauer

2020

Mathematics

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In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking constraints in the same scheduling problem depending on the intermediate storage among machines, the characteristics of the machines, the technical constraints, and even the jobs. Our objective was to schedule a set of jobs to minimize the makespan. Thus, we propose, for the first time, a mathematical model of the job-shop problem taking into account the general case of mixed blocking constraints, and the results were obtained using Mosel Xpress software. Then, after explaining why and how groups of jobs have to be processed, a blocking constraint conflict-free warranted evaluation function is proposed and tested with the particle swarm optimization and genetic algorithm methods. The results prove that we obtained a near-optimal solution to this problem in a very short time.

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“…The FJSP can be categorized into total FJSP and partial FJSP according to the machine compatibility. Cases 5 and 6 represent the partial FJSP, where each operation can be processed only on some machines [18], whereas case 7 represents the total FJSP, where each operation can be processed on all the machines. For extended experiments, we used a simple BOM structure, as in Figure 6a, and increased the problem size including the numbers of final items, machines, and workers.…”

Section: Extended Experimentsmentioning

confidence: 99%

Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

2019

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This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

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Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem

Cited by 37 publications

References 39 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

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

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