A Heuristic Master Planning Algorithm for Supply Chain Network

• Main Authors: Zhong-Hui Lin, 林仲輝
• Other Authors: Ching-Chin Chern
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/28940614635056915754

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 92 === This study proposes a heuristic algorithm to solve a general master-planning problem of a supply chain network with multiple final products. The objective of this planning algorithm is (1)To minimize the processing, transportation , and inventory costs under the constraints of the capacity limits of all the nodes in a given supply chain network graph and the quantity and due day requirements of all the orders. (2)To lower the impact of fairness problem of greedy capacity allocation. This study assumed that multi-finished items are made and shipped on the given supply chain which results in common parts on common nodes for different finished items. Three different ways are proposed to solve the sharing capacity problem caused by common components: greedy, average capacity, and proportional capacity. All the three algorithms are basically composed of five steps. (1). They split nodes in the supply chain network graph by different functions the nodes perform, and set the initial capacities of all nodes. (2).They transform the capacity units shown on the graph, based on the unit of the final finished product. (3).They sort all the orders by adopting a rule-based sorting method to decide the scheduling sequence.(4).They extract sub-networks from original networks according to final product structure of orders. (5).Finally, for each order, the algorithms find a minimum cost production tree under the constraints of the order''s due day. They then compute the maximum available capacity of this combination and arranges the suitable quantities of production and transportation. If the demand cannot be fulfilled before the due day, the order will have to be postponed. Repeating the process above until the demand is completely fulfilled. The difference is average capacity and proportional capacity are under constraints of capacity using quota. The three algorithms result in the same optimal solution as the one by "Linear Programming" in eight different dimensions of scenarios when no delayed orders present. In the four cases with delayed orders, the three orders will still work out a near-optimum solution in a shorter time.