A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

Fazlelahi, Forough Zarea; Pournader, Mehrdokht; Gharakhani, Mohsen; Sadjadi, Seyed Jafar

doi:10.1177/0954405415615728

Cited by 16 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Partially mapped crossover (PMX) method, which is an order based crossover technique, is the first alternative [20]. It chooses randomly two pivot indexes from chromosomes and changes the parts between two pivot points.…”

Section: Crossover Methodsmentioning

confidence: 99%

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

Tağtekin,

Öztürk,

Sezer

2021

Preprint

View full text Add to dashboard Cite

Nowadays, DevOps pipelines of huge projects are getting more and more complex. Each job in the pipeline might need different requirements including specific hardware specifications and dependencies. To achieve minimal makespan, developers always apply as much machines as possible. Consequently, others may be stalled for waiting resource released. Minimizing the makespan of each job using a few resource is a challenging problem. In this study, it is aimed to 1) automatically determine the priority of jobs to reduce the waiting time in the line, 2) automatically allocate the machine resource to each job. In this work, the problem is formulated as a multi-objective optimization problem. We use GA algorithm to automatically determine job priorities and resource demand for minimizing individual makespan and resource usage. Finally, the experimental results show that our proposed priority list generation algorithm is more effective than current priority list producing method in the aspects of makespan and allocated machine count.

show abstract

Section: Crossover Methodsmentioning

confidence: 99%

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

Tağtekin,

Öztürk,

Sezer

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…GA has been proven to be effective to generate suboptimal solutions for large-scale dynamic facility layout problems. Fazlelahi et al [12] devised a customized permutationbased robust genetic algorithm in dynamic manufacturing environments, which is expected to be generating a unique robust layout for all the manufacturing periods.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem

Peng

Zeng

Fan

et al. 2018

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

This paper deals with stochastic dynamic facility layout problem under demand uncertainty in terms of material flow between facilities. A robust approach suggests a robust layout in each period as the most frequent one falling within a prespecified percentage of the optimal solution for multiple scenarios. Mont Carlo simulation method is used to randomly generate different scenarios. A mathematical model is established to describe the dynamic facility layout problem with the consideration of transport device assignment. As a solution procedure for the proposed model, an improved adaptive genetic algorithm with population initialization strategy is developed to reduce the search space and improve the solving efficiency. Different sized instances are compared with Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the proposed genetic algorithm. The experiments calculating the cost deviation ratio under different fluctuation level show the good performance of the robust layout compared to the expected layout.

show abstract

“…It replaces the traditional random crossover and effectively generates excellent offspring [21]. The traditional genetic algorithm often gets into a local optimum [22]. The method can remedy the defects in GA [23], and this study employs an optimum orthogonal array of the Taguchi method [22], so as to implement the decision process of optimum parameter design [24,25].…”

Section: Hybrid Taguchi-genetic Algorithmmentioning

confidence: 99%

Using the Taguchi-Genetic Algorithm to Improve Lithographic Photoresist Operating Conditions of Touch Panels to Upgrade After-Develop Inspection

Wang

2018

Applied Sciences

View full text Add to dashboard Cite

In order to use touch control products more conveniently, a general objective is to develop lighter and smaller touch panels. A touch panel using the one glass solution (OGS) is an important development. The black matrix (BM) in an OGS touch panel is used as a black frame. The photoresist is divided into a positive photoresist and a negative photoresist. The BM photoresist is negative. After coating, exposure, and development in the BM process, after-develop inspection is implemented to check if the appearance is abnormal. It is quite difficult to rework the negative photoresist process. There is still room for improving the BM photoresist process capability Cpk. Thus, in order to reduce the customer complaint rate and enhance stability, the photolithography process is improved to enhance Cpk. Among the BM black negative photoresist forming process conditions of OGS products, the pre-baking time is the most important process control factor. The method set up herein improves the original Cpk = 0.90. This study employs the fast messy genetic algorithm (fmGA) to select the optimum orthogonal array of the Taguchi method, so as to implement the decision process of optimum parameter design. The Cpk of the optimum parameter is 2.12.

show abstract

A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

Cited by 16 publications

References 55 publications

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem

Using the Taguchi-Genetic Algorithm to Improve Lithographic Photoresist Operating Conditions of Touch Panels to Upgrade After-Develop Inspection

Contact Info

Product

Resources

About