A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

Alcan, Pelin; Başlıgil, Hüseyin

doi:10.1016/j.advengsoft.2011.10.004

Cited by 32 publications

(13 citation statements)

References 20 publications

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“…The number of spray booth machine in 4S auto maintenance shop is in the interval [3,6]. The release date obeys discrete uniform distribution in the interval [0,6]. For each maintenance job , obeys the uniform distribution in the interval [4,16].…”

Section: Computational Experiments and Discussionmentioning

confidence: 99%

“…According to the investigation and collection of fundamental data of the 4S auto maintenance shop, we generate the value range of simulation parameter as follows: The number of maintenance jobs each day in 4S auto maintenance shop which need to use spray booth machine is in the interval [12,20]. The number of spray booth machine in 4S auto maintenance shop is in the interval [3,6]. The release date obeys discrete uniform distribution in the interval [0,6].…”

Section: Computational Experiments and Discussionmentioning

confidence: 99%

“…And , → , denotes the processing order, namely the machine is used for job before used for job . Constraint (6) indicates whether the job is processed on machine at time , and it can divided into two cases. Case 1: when the job is processed on machine at time , the value of ( ) is 1.…”

Section: Problem Description and Mathematical Modelmentioning

confidence: 99%

“…On the basis of this, Ali and Behrooz proposed a discrete particle swarm optimization algorithm to solve the problem [5]. Alcan et al considered the fuzzy processing times and applied the genetic algorithm to solve the non-identical parallel machine [6]. Balin proposed a new genetic algorithm revised from an optimum criterion and new crossover operator, and then verify the efficiency of proposed algorithm by simulating result [7].…”

Section: Introductionmentioning

confidence: 99%

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The Dynamic Scheduling of Spray Booth Machine in 4S Auto Maintenance Shop

Li¹,

Li²,

Qin³

2016

Proceedings of the International Symposium on Mechanical Engineering and Material Science

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Abstract. Theory of constraint point out that the bottleneck is a major factor which restricts the system going toward the target. Therefore, the bottleneck is the key of scheduling process in entire system. In fact, due to the factors of long processing time, high cost and few amounts, spray booth machine become the bottleneck in the whole 4S auto maintenance shop. Through reasonable scheduling of spray booth machine, we can improve system's efficiency and customer's satisfaction. First, combining with the real situation, we apply a triplet method to describe the spray booth scheduling problem in 4S maintenance shop. On this basis, a multi-objectives model with multi-constraint is constructed for the scheduling problem. Combining with the real situation, the scheduling model also can be regard as machine in parallel with different speed and machine eligibility restriction and job's characteristic. And then, we design a two-phased heuristic algorithm based on the dispatching rules and particle swarm optimization algorithm to solve it. Finally, we show the experimental results and compares with usual methods to demonstrate the superior performance of the proposed method.

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Section: Computational Experiments and Discussionmentioning

confidence: 99%

Section: Computational Experiments and Discussionmentioning

confidence: 99%

Section: Problem Description and Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Dynamic Scheduling of Spray Booth Machine in 4S Auto Maintenance Shop

Li¹,

Li²,

Qin³

2016

Proceedings of the International Symposium on Mechanical Engineering and Material Science

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“…In order to adapt GA to nonidentical parallel machine scheduling problem, he proposed a new crossover operator and a new optimality criterion. Alcan and Balişgil presented a kind of genetic algorithm based on machine code for minimizing the processing times in nonidentical machine scheduling problem [ 18 ]. Also triangular fuzzy processing times were used in order to adapt the GA to nonidentical parallel machine scheduling problem in that paper.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

Rao

2013

The Scientific World Journal

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For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

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Intelligent Algorithms for Cutting Scheduling Problem

Rao

Luo

2022

Intelligent Algorithms for Packing and Cutting Problem

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A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

Cited by 32 publications

References 20 publications

The Dynamic Scheduling of Spray Booth Machine in 4S Auto Maintenance Shop

The Dynamic Scheduling of Spray Booth Machine in 4S Auto Maintenance Shop

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

Intelligent Algorithms for Cutting Scheduling Problem

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