Models and Solution Methods for Cell Formation Problems

• Main Author: 張欽智
• Other Authors: 鍾淑馨
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/32529803488714246456

博士 === 國立交通大學 === 工業工程與管理學系 === 99 === Cell formation problem (CFP) is the first and most difficult aspect of constructing a preliminary cellular manufacturing system (CMS). The CFP can be classified into two main categories: the standard CFP represented by a binary machine-part incidence matrix and the generalized CFP with more factors and system constraints considerations. Although many studies have been done on standard CFP, generalized CFP had received less attention. Very little has been done to integrate the three basic steps (e.g., cell formation, cell layout, and intracellular machine layout) in the design of CMS. Based on the above discussion, a two-stage hybrid algorithm merging a similarity coefficient method (SCM)-based clustering algorithm and meta-heuristics, including simulated annealing (SA), water flow-like algorithm (WFA) and tabu search (TS) is first presented to solve standard CFP quickly and effectively. In regard to the generalized CFP, a two-stage multi-objective mathematical programming model is first formulated to integrate cell formation, cell layout, and intracellular machine layout simultaneously with the considerations of alternative process routings, operation sequences, production volume, production times, and machine reliability. A two-stage hybrid approach integrating a generalized SCM-based clustering algorithm and SA/TS/WFA method is then proposed to solve this generalized CFP model quickly and effectively. Unlike most existing methods, the proposed approach not only integrates the three basic steps in the design of CMS but also automatically calculates and determines the number of cells (NC) to achieve the best objective value. Illustrative examples, comparisons, and experimental analyses demonstrate the effectiveness of the proposed models and solution algorithms. The proposed approaches can be used to solve real-life CFP in factories by providing robust manufacturing cell formation in a short execution time.