考慮共同料限制下多目標主規劃排程問題之研究
Other Title
A Heuristic Master Planning Algorithm with Multiple Objectives
and Component Commonality
and Component Commonality
Date Issued
2005
Date
2005
Author(s)
DOI
932416H002014
Abstract
This study proposes a heuristic algorithm to solve a general master-planning problem of a supply chain
network with multiple final products. The objectives of this planning algorithm are: (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 composed of five steps: (1) Split nodes in the supply chain network graph by different
functions the nodes perform, and set the initial capacities of all nodes; (2) Transform the capacity units
shown on the graph, based on the unit of the final finished product; (3) Sort all the orders by adopting a
rule-based sorting method to decide the scheduling sequence; (4) Extract sub-networks from original
networks according to final product structure of orders; (5) Finally, for each order, find a minimum cost
production tree under the constraints of the order’s due date. Then, compute the maximum available
capacity of this combination and arrange the suitable quantities of production and transportation. If the
demand cannot be fulfilled before the due date, the order will have to be postponed. Repeating the process
above until the demand is completely fulfilled. The differences among three different ways of sharing
capacities lie on the quota they can allocate for each order: original capacity, average capacity and
proportional capacity. These 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.
Subjects
Advance Planning and Scheduling (APS)
Master Planning
Multiple-Objective
Linear Programming
Linear Programming
Product Commonality
SDGs
Publisher
臺北市：國立臺灣大學資訊管理學系暨研究所
Type
other
File(s)![Thumbnail Image]()
Loading...
Name
932416H002014.pdf
Size
200.73 KB
Format
Adobe PDF
Checksum
(MD5):bb0b36d88a646f641c5e467fa4de7497