A Heuristic Master Planning Algorithm Considering Fixed Lot Size for Supply Chain Management

• Main Authors: Chang-You Chen, 陳昌佑
• Other Authors: Ching-Chin Chern
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/43554850064994554173

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 96 === In a competitive business environment, the partners in a supply chain take responsibility for different tasks, and depending on the tasks it performs, each partner organization generates its own costs (e.g., production, transportation, or inventory). It is in the best interest of the whole supply chain to meet all demands by allotting the resources in the most efficient way, thus minimizing the supply chain’s overall costs. Partners of a supply chain prefer to produce or transport mass quantities of products in order to benefit from economies of scale. However, inconsistent lot-sizing policies taken by different partners of a supply chain may result in high inventory cost and huge setup cost. Considering multiple final products, multi-level BOM, setup cost, and capacitated fixed lot size, this study focuses on solving master planning problems of an “Advanced Planning and Scheduling” system, which is designed to determine a production and distribution plan of a supply chain network to fulfill all demands. The objective of this study is to produce optimal production plans that will satisfy all demands while minimizing delay penalties and minimizing the costs of setup, materials, production, processing, transportation, and inventory holding—all while respecting the fixed lot-sizing policies, the capacity limitations, and demand deadlines of everyone involved in a given supply chain network. In this study, lot-sizing policy taken by each member of a supply chain is the most important factor that affects the cost of setup and inventory holding. Mixed Integer Programming (MIP) is a popular way to solve this type of master planning problems. However, as such problems increase in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. In response to the difficulty of solving the planning problem, this study proposes a heuristic algorithm, called the Lot-Sizing Master Planning Algorithm or LSMPA. First, LSMPA groups and sorts demands according to the required products and the imposed due dates. Then, the LSMPA plans the demands individually, choosing the best planning production tree from all possible production trees and using BSATA (Backward Searching Available Time Algorithm) to lower the setup cost and FSADA (Forward Searching Available Demand Algorithm) to optimize lot-sizing economic effect. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses to demonstrate the power of the algorithm.