面對市場對於電子商品的需求與日俱增與顧客的需求行為快速改變，以及全球產業垂直分工發展趨勢之下，電子裝配業業者必須面對眾多料件採購前置時間的不同，生管單位必須透過採購單位掌握各供應商資訊，並且負責各項生產製令與採購訂單的開立、追蹤與跟催工作，最後以確認各料件是否可在需求產生時即時供給滿足之。在面對多樣少量的生產型態及產品生命週期逐漸縮短的趨勢之下，傳統的產能與物料規劃方式已不再適用。為解決以上種種問題，先進生產規劃與排程（Advanced Planning and Scheduling; APS）系統乃應運而生。其中，資源配置(resource allocation)是APS規劃的核心之一，藉由資源配置的規劃便可提供規劃人員一配置的依據，進而降低因預測與實際需求之間落差所造成的儲存成本。 本研究即運用先進生產規劃與排程的概念，提出一應用基因演算法之資源配置決策模式。透過本模式的運作，可同時考量供給訂單挪移所產生的各項成本項目，解決規劃人員依據「經驗法則」來配置物料而忽略了物料本身成本的問題，提供一成本控管的依據。進而提供規劃人員一取消訂單或是更改訂單之依據，以降低企業成本的浪費。最後，本研究以一電子裝配業個案進行實證，驗證本研究所提模式的可行性，進而與依照經驗法則的配置方式進行比較與評估。 Due to the changing environment, the products in electronics industry vary enormously. The common problems faced with the electronics industry as well as other industries in Taiwan included unpredictable demand, different lead time of various material, tight delivery schedule and constant changing of production plan. Electronics industry needs to catch up with the market trend, so that it can responds to customer needs quickly and to overcome the problems caused by the over-centralized industries, products and markets. Besides, traditional production planning method (e.g., MRP) cannot fulfill the requirements of effective production planning. Therefore, Advanced Planning and Scheduling (APS) system is promoted to solve the complex problems that planner encounter in production planning. Resource allocation is the core of APS, it can provide planners the material allocation reference and reduce inventory costs between forcasting order and customer order. In this thesis, a genetic algorithm-based resource allocation model for electronics industry is introduced. The model aims to solve ignoring costs for material allocation by “heuristic rule”. It measures costs such as supply order movement cost, and provides a decision suggestion for planners to cancel or change order. Finally, the model developed in this research is applied to a electronics industry to test it’s applicability in practice.
