Analyzing and Planning Manufacturing Resource Allocation for a Pull Production System

• Main Authors: Yan-jing Chen, 陳衍璟
• Other Authors: Yau-Ren Shiau
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/45498110587265527554

碩士 === 逢甲大學 === 工業工程與系統管理學研究所 === 100 === In recent years, customer demand has been changed from demand stable to instable, and product categories more numerous. So that guide enterprises toward customer-oriented business model. Enterprises in the face of the demand uncertainty of the production environment in order to create competitive advantage using the push-type production system has been unable to meet market demand. Instead, demand-driven for pull production systems become an important, then production process has been changed from a single to multi-stage processing of the current production patterns and production system is composed by numerous of process, that is Products with multiple quality characteristics. However, enterprises have limited resources cause company will face to processes should choose which classes workstations and inspection station configuration for cost-effective production system problems. It can be seen enterprise manufacturing resource planning and allocation is very important. This paper assumes the production system is imperfect. By analyzing the resources allocation, purchase parts and other considerations and resource constraints derived the cost of internal models for the study included the reasonable expectations of the total production cost model. And find the optimal exhaustive method to compare based on test and verify the developed genetic algorithm. To find more efficient access to the minimum expected total cost of production under the best manufacturing resource planning and allocation, in order to help decision-makers can effectively and clearly the assessment of a reasonable basis. Finally, this study developed a genetic algorithm compare the results of exhaustive method, both algorithms of the best configuration the average total production cost of up to 99.26% of the performance of similarity, and heuristic save 98.01% average execution time. In this study demonstrate the development of genetic algorithm can be more efficient access to the minimum expected total cost of production under the best manufacturing resource planning and allocation, in order to help decision-makers can effectively and clearly the assessment of a reasonable basis.