An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Gu, Xiaolin; Huang, Ming; Xu, Liang

doi:10.3390/a12110243

Cited by 21 publications

(9 citation statements)

References 24 publications

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“…This data set was produced by Brandimart (1993) and includes 10 samples [21]. The parameters of each of the problems in this dataset are generated randomly using a uniform distribution between the two limits [22,23]. The number of tasks is defined from 10 to 20, the number of machines from 4 to 15, the number of operations for each task from 5 to 15 and the number of operations for all tasks from 55 to 240.…”

Section: Resultsmentioning

confidence: 99%

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

Rezaeipanah¹,

Mojarad

2021

JAIT

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This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

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Section: Resultsmentioning

confidence: 99%

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

Rezaeipanah¹,

Mojarad

2021

JAIT

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“…In order to more accurately evaluate the proposed method, we provide further comparisons in Table 8. This comparison includes the genetic algorithm (GA) [21], the neighborhood-based genetic algorithm (NGA) [4], the hybrid evolutionary algorithm (HEA) [31], the genetic algorithm with a tabu search in a holonic multi-agent model (GATS+HM) [20], and the improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) [10]. The data reported in this table was collected from the corresponding literature.…”

Section: Resultsmentioning

confidence: 99%

“…The spacing criterion is used to show the compatibility of the distance between the solutions in the Pareto front [10]. Lower values of the distance criterion indicate that the distance stability between the solutions is higher.…”

Section: Evaluation Criteriamentioning

confidence: 99%

A Meta-Heuristic Approach Based on the Genetic and Greedy Algorithms to Solving Flexible Job-Shop Scheduling Problem

Rezaeipanah¹,

Sarhangnia

Abdollahi³

2021

csci

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In today’s competitive business world, manufacturers need to accommodate customer demands with appropriate scheduling. This requires efficient manufacturing chain scheduling. One of the most important problems that has always been considered in the manufacturing and job-shop industries is offering various products according to the needs of customers in different periods of time, within the shortest possible time and with rock-bottom cost. Job-Shop Scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in various aspects of construction and design. In addition, these systems are identified as Cellular Manufacturing Systems (CMS). Today, applying CMS and the use of its benefits have been very important as a possible way to increase the speed of the organization’s response to rapid market changes. In this paper, a meta-heuristic method based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated in a greedy algorithm and several elitist operators are used to improve the solutions. The greedy algorithm which is used to improve the generation of the initial population prioritizes the cells and the job in each cell, and thus offers quality solutions. The proposed algorithm is tested over P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality and run-time criteria were used in a multi-objective function. The results of simulation indicate better performance of the proposed method compared to NRGA and NSGA-II methods.

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“…In this paper, the parallel hybrid particle swarm optimization (PHPSO), IA and PSO algorithms are set to the same number of iterations of 50. C best is the optimal value of the operation, A ver is the average value of the operation and the relative deviation of the value dev [28]. Among them, dev1 is the comparison between PHPSO and IA and dev2 is the comparison between PHPSO and PSO.…”

Section: Instance Verification 41 Examples Of Mixed Mixed-flow Workhop Schedulingmentioning

confidence: 99%

Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

2021

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In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

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An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Cited by 21 publications

References 24 publications

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

A Meta-Heuristic Approach Based on the Genetic and Greedy Algorithms to Solving Flexible Job-Shop Scheduling Problem

Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

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