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.

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.

Cellular manufacturing systems design using Tabu search

2000 ◽  
Vol 214 (2) ◽  
pp. 169-172 ◽  
Author(s):  
G V Songore ◽  
V Songore
Keyword(s):  
Tabu Search ◽  
Manufacturing Systems ◽  
Family Formation ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Real Life ◽  
Systems Design ◽  
Computational Time ◽  
Cellular Manufacturing Systems ◽  
Manufacturing Systems Design

Several techniques have appeared in the literature for the cell formation problem. However, most of these heuristics have the disadvantages that they cannot solve real-life problems typical of manufacturing firms in reasonable computational time, and deal with the part family formation and cell grouping problems separately. The Tabu search meta-heuristic is presented for resolving these problems. The minimization of intercellular movements as an objective function is demonstrated, although minimization of cell load variation and the multiobjective function option exist in the procedure developed. The results discussed for some problems taken from the literature show that the Tabu search is a promising technique for solving the cellular manufacturing systems design problem for both medium and large problem instances.

An Improved Fuzzy C-Means Algorithm for Cell Formation Problems with Alternative Routes

2015 ◽  
Vol 4 (4) ◽  
pp. 15-30
Author(s):  
İhsan Erozan ◽  
Ozden Ustun ◽  
Orhan Torkul
Keyword(s):  
Manufacturing Systems ◽  
Cell Formation ◽  
Test Problems ◽  
Uncertain Information ◽  
Cellular Manufacturing Systems ◽  
Fuzzy C Means ◽  
Realistic Approach ◽  
Alternative Routes ◽  
Better Than ◽  
Improved Algorithm

Cell formation is one of the most important problems faced in designing cellular manufacturing systems. Fuzzy c-means (FCM) has been successfully used to solve a variety of the cell formation problems because it allows the representation of uncertain information. Many products or parts to be manufactured in the real world have alternative routes. To ignore the routes is not a realistic approach. However, most FCM approaches used to form a cellular system in the literature have ignored or avoided using the alternative routes because of its complexity. In this paper, an improved FCM algorithm has been proposed to overcome the computational complexity of the alternative routes. The improved algorithm presents an easy and practical way to solve the cell formation problems with alternative routes. An experiment was designed to test and compare the performance of the improved algorithm. The results of the experiment have shown that most of the obtained results are close to the test problems and better than the conventional crisp methods in the literature.

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