以蟻群演算法動態選擇最佳供應商

A Dynamic Decision Approach for Supplier Selection Using AACS

楊曜榮(Yao-Jung Yang)

《興國學報》 12期 (2011/01) Pp. 41-54

https://doi.org/10.29950/JHKUM.201101.0003

摘要

本研究使用AACS（Attribute-based Ant Colony System）平台用以解決尋找最佳供應商的問題，我們紀錄供應商的屬性，依公司提出的交易準則來尋找符合公司需求的最佳供應商。AACS此搜尋機制是建構以蟻群演算法（Ant colony algorithm, ACO）為基礎，將搜尋對象的屬性或特質納入搜尋機制中，此為AACS的搜尋機制。AACS會依公司準則並對每個供應商，此演算法將供應商視為節點，蟻群背負著公司的準則並會尋訪每個節點，並提出建議的最佳供應商作為參考。本研究有兩點限制：第一是公司訂定的準則屬性需與供應商相匹配，第二是屬性的量化須有公司決策人員決定。本研究的主要貢獻除了提供公司可以動態並隨機的因應環境需求調整公司選擇供應商的準則外，又配合AACS提出最佳化的供應商選擇清單以供決策者參考。

關鍵字

供應商 ； 蟻群演算法

並列摘要

This study uses the AACS platform to examine the critical factors for decision making in a dynamic business environment in order to select the appropriate suppliers. This study focuses on how to search for optimal suppliers in a similar fashion to how the optimal route can be found. The AACS is based on the ACO algorithm, which is then modified to achieve the adaptive optimal system used to set the policy for companies to select their suppliers, as the researcher (as like source node) and chosen supplier's attributes to be conditions of research, There are two limitations with this study. First, the criteria for the policy and attribute numbers and sequence for suppliers must be same. Secondly, the score has evaluated by the buyer company before the decision group to use which one policy. The value of this study divides two points; the parameters of AACS platform are adjustment for the buyer decision policy from dynamically business environment. And the AACS can find an optimal solution from the decision policy. AACS according to the decision group's policy to enter parameters in order to find the adaptive solution for buyer business firm to find their finest suppliers.

並列關鍵字

supplier selection ； AACS

參考文獻

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