The Study of Genetic Algorithm to Supply Chain Production-Distribution Optimization Model

• Main Authors: Chou-Sheien Wu, 吳佐賢
• Other Authors: Chian-Son Yu
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/32019813152210058296

碩士 === 實踐大學 === 企業管理研究所 === 90 === The key point for enterprises to elevate competitiveness will be how to take away the thought of “only inside performance”, and how to emphasize on the integrated performance of different enterprise units and procedures to decrease the influence and cost on the whole supply chain, and then increase customers’ satisfaction. In other words, the connection among enterprise’s procedures and its integration benefits is difficult for other competitors to imitate and replace, enterprises should use the concept of whole supply chain optimization to face up oncoming threats and challenges. This research will wholly integrate from suppliers to demanders, and construct a mathematical model to optimize the production and sales in a supply chain. This model includes orders, manufacturing, distribution and inventory among raw material suppliers, manufacturers, distributors, and retailers; in addition, the research uses Divergent assembly supply chain net to be hypotheses. It explains how to minimize the whole supply chain’s cost under the conditions of satisfying the demand and resource limits. The model itself must be able to solve complex and big scale problems, and be easy to correct. According to these characters, this study will adopt for Evolution Rule Genetic Algorithm to be the solution model, and design sixteen different simulation environments with different parameter settings to process, the experiments, uses each parameter combinations to search for the best setting value to prove the capability and usability of this paper.