An Integrated Model for the Design of Cellular Manufacturing Systems

2012 ◽  
Vol 557-559 ◽  
pp. 2423-2426 ◽  
Author(s):  
Reza Raminfar ◽  
Zulkifli Norzima ◽  
Mohammadreza Vasili ◽  
Sai Hong Tang
Keyword(s):  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Production Environment ◽  
Cellular Manufacturing Systems ◽  
Reducing Costs ◽  
Operational Issues ◽  
Planning Group ◽  
Group Layout ◽  
Production Approach

Cellular manufacturing (CM) is a production approach directed towards reducing costs, as well as increasing system's flexibility in today's small-to-medium lot production environment. Many structural and operational issues should be considered for a successful CM design and implementation; such as cell formation (CF), production planning, group layout (GL), resource allocation and scheduling. Most researchers have addressed these issues sequentially or independently, instead of jointly optimizing a combination of these issues. In order to attain better results to ensure that the system will be capable of remaining efficient in unknown future situations, these issues should be addressed simultaneously. In this paper possibility of developing a comprehensive mathematical model which considers these issues is discussed.

scholarly journals An Integrated Model for Production Planning and Cell Formation in Cellular Manufacturing Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Reza Raminfar ◽  
Norzima Zulkifli ◽  
Mohammadreza Vasili ◽  
Tang Sai Hong
Keyword(s):  
Production Planning ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Rank Order ◽  
Cell Formation ◽  
Integrated Approach ◽  
Production Environment ◽  
Proposed Model ◽  
Reducing Costs ◽  
Operational Issues

Cellular manufacturing (CM) is a production approach directed towards reducing costs, as well as increasing system's flexibility in today's small-to-medium lot production environment. Many structural and operational issues should be considered for a successful CM design and implementation such as cell formation (CF), production planning, and facility layout. Most researchers have addressed these issues sequentially or independently, instead of jointly optimizing a combination of these issues. In order to attain better results to ensure that the system will be capable of remaining efficient in unknown future situations, these issues should be addressed simultaneously. In this paper, a mathematical model is developed using an integrated approach for production planning and cell formation problems in a CM. A set of numerical examples are provided from existing the literature in order to test and illustrate the proposed model. In order to evaluate and verify the performance of the proposed model, it is compared with a well-known cell formation methods (rank order clustering and direct clustering analysis), using group capability index (GCI) measure. The results and comparisons indicate that the proposed model has a significantly higher and satisfactory performance and it is reliable for the design and the analysis of CM systems.

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.

Sign in / Sign up

Export Citation Format

Share Document