Development of an integrated model for production planning and cell formation in cellular manufacturing systems

• Main Author: Raminfar, Reza
• Format: Thesis
• Language: English
• Published: 2010
• Online Access: http://psasir.upm.edu.my/id/eprint/40863/1/FK%202010%2031R.pdf; http://psasir.upm.edu.my/id/eprint/40863/
• Institution: Universiti Putra Malaysia

A cellular manufacturing system (CMS) is a manufacturing structure organized based on the group technology (GT) concept. CMS manufacturing system has many advantages compared to job shop or flow line manufacturing system. The design of CMS involves many structural and operational issues. One of the most important design steps is the formation of part families and machine cells or cell formation problem (CF), another problem is “how to determine the optimal quantities of different part-types” or in the other word production planning problem in CMS. Over the last decades, many researchers have worked on CF problem and suggested many heuristic and mathematical algorithms to obtain the best results. On the other hand, various manufacturing production planning and inventory control problems have been studied by many production management researchers. However, only a few studies explicitly and simultaneously incorporate the CMS context, production planning and cell formation problem. In this thesis, a mathematical programming model is developed using an integrated approach for production planning and CF problems in a CMS. This non-linear mixed integer mathematical model minimizes the inter-cell material handling (with respect to the different travel distances between cells), finished-goods inventory cost and machine set-up cost (considering the machine set up cost for every operation). This non-linear model is then converted to a linear model. Numbers of numerical examples are provided from existing literature to test and illustrate the proposed model. These examples are written with lingo codes in LINGO software. In order to evaluate and verify the performance of the proposed model, it is compared with a well-known cell formation method (Rank order clustering, ROC), using group capability index (GCI) measure. ROC method and GCI measure have been frequently used and suggested in previous literature. The results and comparisons between GCI measures of ROC method and proposed CF model in last two examples shows 23%, 50% improvement, which indicates that the proposed CF model has a higher and satisfactory performance. This also should be considered that the proposed model is more applicable while it takes into account the production planning problem too.