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

2021 ◽  
Author(s):  
Amin Rezaeipanah ◽  
Musa Mojarad
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Programming Model ◽  
Heuristic Method ◽  
Cell Formation ◽  
Mixed Integer ◽  
Test Problems ◽  
Cellular Manufacturing Systems ◽  
Machine Grouping

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.

scholarly journals Nested bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

2019 ◽  
Author(s):  
Bardia Behnia ◽  
Babak Shirazi ◽  
Iraj Mahdavi ◽  
Mohammad Mahdi Paydar
Keyword(s):  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Life Cycles ◽  
Strategic Decision ◽  
Computational Time ◽  
Test Problems ◽  
Special Role ◽  
Cellular Manufacturing Systems ◽  
Level Model

Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.

A New Genetic Algorithm for Designing Cellular Manufacturing Systems with Labor and Tools Issues

2011 ◽  
Vol 110-116 ◽  
pp. 4307-4314
Author(s):  
Anis Gharbi ◽  
Abdulrahman M. Al-Ahmari ◽  
Mohamed Kais Msakni ◽  
Hisham Al-Khalefah
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Numerical Results ◽  
Cellular Manufacturing Systems ◽  
Suggested Procedure ◽  
Process Plans ◽  
Practical Constraints

This paper considers the problem of designing cellular manufacturing systems (CMS) with the presence of alternate process plans, tools and workers. The objective is to minimize the total costs of machine installation, operations, tools and workers with a number of identified practical constraints. A genetic algorithm is designed in order to efficiently solve medium and large sized problems. Preliminary numerical results show the worth of implementing the suggested procedure.

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