A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

2004 ◽  
Vol 42 (7) ◽  
pp. 1419-1441 ◽  
Author(s):  
Maghsud Solimanpur ◽  
Prem Vrat ◽  
Ravi Shankar
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Cellular Manufacturing Systems ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Genetic Algorithm Approach

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 Multi-Objective Optimization of Activation Time and Discharge Time of Thermal Battery Using a Genetic Algorithm Approach

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6477
Author(s):  
Qing Li ◽  
Yu-Qiang Shao ◽  
Huan-Ling Liu ◽  
Xiao-Dong Shao
Keyword(s):  
Genetic Algorithm ◽  
Heat Transfer Analysis ◽  
Activation Time ◽  
Discharge Time ◽  
Thermal Battery ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Thermal Batteries ◽  
Genetic Algorithm Approach ◽  
Bottom Heat

Activation time and discharge time are important criteria for the performance of thermal batteries. In this work a heat transfer analysis is carried out on the working process of thermal batteries. The effects of the thicknesses of heat pellets which are divided into three groups and that of the thickness of insulation layers on activation time and discharge time of thermal batteries are numerically studied using Fluent 15.0 when the sum of the thickness of heating plates and insulation layers remain unchanged. According to the numerical results, the optimal geometric parameters are obtained by using multi-objective genetic algorithm. The results show that the activation time is mainly determined by the thickness of the bottom heat pellet, while the discharge time is determined by the thickness of the heat pellets and that of the insulation layers. The discharge time of the optimized thermal battery is increased by 4.08%, and the activation time is increased by 1.23%.

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