A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Torabi, S. Ali; Sahebjamnia, Navid; Mansouri, S. Afshin; Bajestani, Maliheh Aramon

doi:10.1016/j.asoc.2013.07.029

Cited by 103 publications

(36 citation statements)

References 72 publications

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“…To optimize three scheduling measures, i.e., the completion time, the total machine load, and the total earliness and tardiness penalties, a decision support toll was also designed. More recently, Torabi et al (2015) discussed a parallel machine scheduling problem with non-zero ready times, sequence and machine-dependent setup times, and secondary resource constraints. They also considered an uncertainty in processing times and due dates with fuzzy natures.…”

Section: Parallel Processor Scheduling Problemmentioning

confidence: 99%

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

Mokhtari

Salmasnia

2015

Expert Systems with Applications

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Section: Parallel Processor Scheduling Problemmentioning

confidence: 99%

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

Mokhtari

Salmasnia

2015

Expert Systems with Applications

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“…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]. For spray booth machine , the setup time obeys the uniform distribution in the interval [0,3].…”

Section: Computational Experiments and Discussionmentioning

confidence: 99%

“…Li and Yang solved the parallel machine problem with minimizing the total weighted completion times by three types of search algorithms [3]. Torabi et al used particle swarm optimization algorithm for a parallel machines scheduling problem with a fuzzy multi-objective [4]. On the basis of this, Ali and Behrooz proposed a discrete particle swarm optimization algorithm to solve the problem [5].…”

Section: Introductionmentioning

confidence: 99%

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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“…On the other hand most of them considered a partial resource constraint, meaning that, they supposed that there is only one secondary resource which is restricted. Torabi et al (2013) addressed unrelated parallel machine scheduling problem with non-zero ready times, sequence dependent setup times, and secondary resource constraints. They proposed a multi-objective practical swarm optimization to tackle this problem.…”

Section: Introductionmentioning

confidence: 99%

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Afzalirad

Shafipour

2015

J Intell Manuf

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This study addresses a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Majority of the traditional scheduling problems in parallel machine environment deal with machine as the only resource. However, other resources such as labors, tools, jigs, fixtures, pallets, dies, and industrial robots are not only required for processing jobs but also are often restricted. Considering other resources makes the scheduling problems more realistic and practical to implement in manufacturing environments. First, an integer mathematical programming model with the objective of minimizing makespan is developed for this problem. Noteworthy, due to NP-hardness of the considered problem, application of meta-heuristic is avoidable. Furthermore, two new genetic algorithms including a pure genetic algorithm and a genetic algorithm along with a heuristic procedure are proposed to tackle this problem. With regard to the fact that appropriate design of the parameters has a significant effect on the performance of algorithms, hence, we calibrate the parameters of these algorithms by using the response surface method. The performance of the proposed algorithms is evaluated by a number of numerical examples. The computational results demonstrated that the proposed genetic algorithm is an effective and appropriate approach for our investigated problem.

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A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Cited by 103 publications

References 72 publications

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

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

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

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