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    题名: Construction of an integration supply chain network design with the strategic and tactical planning
    其它题名: 建構整合策略及戰術層級供應鏈網路設計之研究
    作者: 何衛中;Ho, Wei-Chung
    贡献者: 淡江大學管理科學學系博士班
    廖述賢;謝佳琳;Liao, Shu-Hsieh
    关键词: Genetic Algorithms;inventory-routing;location-inventory;Supply chain network design;存貨-路徑;供應鏈網路設計;定址-存貨;遺傳演算法;SCND
    日期: 2017
    上传时间: 2018-08-03 14:48:43 (UTC+8)
    摘要: 供應鏈網路設計在供應鏈管理中占有很重要的地位,其目的在於提供供應鏈管理一個發揮效應的工作平台。供應鏈網路設計若依規劃時間的長短,可區分為戰術、戰略及操作等不同層級,其決策內容分別為設施定址,存貨及運輸規劃。以策略層級而言,因為投入的資源較大,決定後就不易改變,故一般多運用單期模式。以戰術層級而言,其目的係運用較近期的需求資訊,可依顧客的需求,劃分多期,求解最佳的運送及存貨規劃。由於供應鏈網路設計決策的時間軸不同,很難以同一模式建構完整的供應鏈網路,故本論文發展兩階層求解方法,第一階層為策略階段,求出最適的設施定址。第二階段為戰術階段,續依據策略階段的定址決策,發展最適的存貨與運送規劃。另基於策略階段決策,影響日後系統運作深遠,故本論文以不同的假設,設計三個不同的策略模式,因為本論文所探討的問題涵括了定址-存貨、存貨-路徑整合問題及路徑等問題,均屬困難性問題,囿於精確求解法只限求小、中型的問題,所以在本論文採用以遺傳演算法為基石求解上述所陳問題。
    Supply chain network design (SCND) which provides an optimal configuration of platform is an important task in supply chain management. The SCND can be classified as strategic, tactical and operational level decisions depending on the time horizons, those decisions include the number, location of facilities, inventory policy and transportation plan. The strategic decision requires the vast investment; the decision is expected to operate for a long term. Therefore, it is reasonable to use single-period model. In the tactical and operational level, the demand information is more accurately, therefore, the decision maker needs a short-term inventory and transportation planning which generate the product flows in the network. In this dissertation, we attempt to integrate a strategic and tactical plan and develop two stage models to construct the optimal SCND. In first stage, three strategic models are presented according to the various scenarios. In second stage, we propose a tactical model which is multi period inventory routing model for determining the time and amount of goods to deliver. Because these models consider location-inventory and inventory-routing problem, both belong to NP-hard problem, exact methods only can solve small or medium size problem, therefore a various genetic algorithms base heuristic are proposed the solve these problems.
    显示于类别:[管理科學學系暨研究所] 學位論文

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