A multi-objective model for designing a group layout of a dynamic cellular manufacturing system

Subject Areas : Mathematical Optimization

Reza Kia ¹ , Hossein Shirazi ² , Nikbakhsh Javadian ³ , Reza Tavakkoli-Moghaddam ⁴

1 - Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, P.O. Box 148, Firoozkooh, Iran
2 - Department of Industrial Management, Qom Branch, Islamic Azad University, P.O. Box 3749113191, Qom, Iran
3 - Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran
4 - Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran

Received: 2020-10-02 Accepted : 2020-10-02 Published : 2013-12-01

Keywords: dynamic cellular manufacturing systems, group layout, Cell formation, Multi-objective model,

Abstract :

This paper presents a multi-objective mixed-integer nonlinear programming model to design a group layout of a cellular manufacturing system in a dynamic environment, in which the number of cells to be formed is variable. Cell formation (CF) and group layout (GL) are concurrently made in a dynamic environment by the integrated model, which incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. Additionally, there are some features that make the presented model different from the previous studies. These features include the following: (1) the variable number of cells, (2) the integrated CF and GL decisions in a dynamic environment by a multi-objective mathematical model, and (3) two conflicting objectives that minimize the total costs (i.e., costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead, machine processing, and forming cells) and minimize the imbalance of workload among cells. Furthermore, the presented model considers some limitations, such as machine capability, machine capacity, part demands satisfaction, cell size, material flow conservation, and location assignment. Four numerical examples are solved by the GAMS software to illustrate the promising results obtained by the incorporated features.