یک مدل یکپارچه برای تصمیمات انتخاب تامین کنندگان در تغییرات پیکربندی

Configuration change management provides a way for a manufacturer to become more competitive. Because of the short life and the large variety involved in commercial products, they must be configured accordingly. It is a task for the configuration change management. This paper presents an integrated model for modeling the change behavior of product parts, and for evaluating alternative suppliers for each part by applying fuzzy theory, T transformation technology, and genetic algorithms. The proposed model is based on the concepts of part change requirements, fuzzy performance indicators, and the integration of different attributes, to allow the part supplier selection of a specific commercial product to be explored and modeled. The application of this approach is illustrated through a case study of a TFT-LCD product for part change optimization. In terms of change performance, experimental analyses with different genetic parameters allowed the potential alternative suppliers for the product parts to be evaluated. The results of the experimental analyses show that this proposed methodology is a suitable approach and provides a quality solution for products with a complex configuration. In addition, the numerical results obtained from the new approach were compared with the results obtained by linear programming. The result shows that the proposed algorithm is reliable and robust.

Mark and Reidelbach (1991) believed that a high customization strategy makes for a closer relationship between business and consumers, and Zhang and Li (1999) stated that product design is reduced to the determination of an appropriate configuration. Many companies today are putting more and more effort into modifying and improving existing products in order to satisfy customer demand and/or processing requirements. If a configuration needs to be changed then it must be done efficiently based on the specifications of the customer, production conditions and source constraints, etc. This has simply become a must for any commercial product that wants to make it and stay in today’s market with its razor sharp competition. In order to enhance customer’s satisfaction, part changes in product configuration are required so as to improve a product’s functions and increase added value. In a real-life production situation, manufacturers must invest a lot of time and capital to carry out these product part changes. When making changes, in order to save resources and costs, the first impulse of producers is to adopt existing product design patterns. Thus the manufacturer must research and develop a new technology with lower costs to design and produce new parts to improve his product. The term ‘product configuration’ refers to the fact that many individual suppliers are involved, and that appropriate parts are provided and assembled so as to substantially improve the value of the product or service sin order to meet the requirement of the customer. Therefore, in today’s rapidly growing and changing environment, the main challenge for the configuration management group is to respond as quickly as possible to the uncertain and forever changing customer requirements. In order to increase the competitiveness of a product in the commercial environment, companies focus on building up their core competencies, and on selecting the appropriate part suppliers, in order to decrease the level of risk in their business, and to improve the efficiency of any required product configuration change. Fulfilling customer requirements means the need for part change planning, including the resolving of issues of optimal cost and quality, etc. of the end product. Consequently, supplier selection is an essential part of the efficient management of configuration change, which is necessary for the effective operation of the organization. Hence, in this paper, we focus on the development and assessment of a model to determine the optimal configuration change strategy using the most suitable part suppliers. This paper develops an innovative optimization algorithm designed specifically for supplier selection in a configuration change. The proposed optimization algorithm adopts the optimization concept of genetic algorithms (GAs), and the uncertainty decision making method of the Fuzzy theory. This proposed algorithm is capable of considering cost and quality attributes with uncertainty values in determining an optimal solution. The remainder of this paper is organized as follows: In the next section, the literatures of product part changes are reviewed. The problem is defined in Section 3, and in Section 4 the mathematical model for the product configuration change is developed. The proposed model and procedures for solving the configuration change problem is described in Section 5. In Section 6, a real product example is provided to illustrate the practicability of the proposed model, and a comparison of the computational results obtained from linear programming (LP) with the proposed algorithm is analyzed. Finally, a brief summary is presented and we draw our conclusions in Section 7.

This paper proposed an integrated assessing model for manufacturers to solve the complex product configuration change problem efficiently and effectively. This model is focused on finding the fundamental supplier combination that will best minimize the cost–quality score, if and when proposed by the customer and/or engineer. It combines the fuzzy theory, T transformation technology, and genetic algorithm. The fuzzy theory is used to quantify the fuzzy cost and quality data, the T transformation technology is employed to deal with the defuzzied data for integrating the different attributes data such as cost and quality, and a genetic algorithm is applied to search intelligently for the optimal part change solution. In order to illustrate the capability of this proposed model, it was applied to a real case study for the stand module of a TFT-LCD. Based on the analytic results, the proposed model can provide a quality solution and can be readily applied to real-life applications such as commercial products with a large number of parts and complex assembly configuration. Furthermore, the optimization results obtained by the proposed model were compared with those obtained by the LP. The optimization results show that the proposed approach is reliable and robust. Further research should be carried out with nonlinear situations such as the purchase/manufacture cost changes as a function of purchase/manufacture quantity, in which nonlinear programming is required, which makes the modeling and computation even more complex. In addition it would be worthwhile to further explore the time factors such as assembly time and purchase/manufacture time.