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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/33769

Title: 事件導向分解式演算法求解隨機規劃問題之研究
Authors: 湯慶輝
Contributors: 國立臺灣海洋大學:運輸科學系
Keywords: 隨機性規劃;事件分解;基因改良策略;航空貨物裝櫃問題;隨機性貨物需求
Date: 2010-08
Issue Date: 2013-05-30T01:46:20Z
Publisher: 行政院國家科學委員會
Abstract: 摘要:在實際營運時,隨時會有許多隨機因素的擾動,使原規劃化結果降低其實際之績效,因 此近年來隨機性規劃問題日漸受到重視。然而,以往隨機規劃問題多偏重於隨機模式之發展 與改進,即著重在模式化的過程中如何考慮未來可能之隨機事件,以使模式能反應真實之隨 機環境。理論上,求解方法的設計與規劃結果的成效影響甚大,求解品質的好壞更會影響模 式之績效表現與適用性。因此,如何發展一有效率之求解方法與架構,以提升隨機模式之求 解品質與求得一較佳之規劃結果,進而增加隨機模式之適用性,實為目前隨機規劃問題中相 當重要的課題。 本研究擬針對隨機性規劃問題,以一般隨機問題常使用的模式架構為對象,發展一以隨 機事件為導向之事件分解式演算法,其中在演算法尋優過程中,將引入基因改良策略,以建 立一套有效率之求解架構。為驗證本研究演算法的可行性,本研究擬以隨機性貨物需求下之 航空貨物裝櫃問題為應用對象,將所發展之演算法應用於該問題中,同時分析比較本研究演 算法與過去研究之求解績效,進一步幫助業者在隨機需求環境中,規劃最佳的貨物裝櫃決 策,以降低裝櫃之營運成本。最後,根據研究的結果,提出結論與建議。
Abstract:Stochastic disturbances often occur in actual operations, which affect the performance of the original planned results. Stochastic programming problems have been garnering increasing interest in recent years. However, in the past, the focus for planning problems with stochastic factors has been on the development and improvement of stochastic models. That is, the stress has been how to consider future stochastic scenarios in the modeling so as to give the stochastic model the ability to reflect the actual stochastic environment. In theory, the solution method has a great influence on the planning result. The solution quality is dependent on the performance and the adoptability of the stochastic model. How to develop an effective and efficient solution method and framework to enhance solution quality, and the adaptability of the stochastic model are importance issues for stochastic problems. We will employ a stochastic scenario oriented concept to develop a scenario decomposition method based on the common stochastic model for stochastic programming problems. In particular, we incorporate genetic improvement strategies into our optimal solution process to develop a systematic solution framework. In order to evaluate how well the solution framework performs in practice, we perform an application to air cargo container loading problems with stochastic cargo demands. We also compare the performance of our solution method with a related past study. The proposed method is expected to be useful in helping carriers solve for better container loading plans and to reduce operating costs under stochastic demand environment. Finally, some conclusions and suggestions will be given.
Relation: NSC99-2628-H424-001
URI: http://ntour.ntou.edu.tw/handle/987654321/33769
Appears in Collections:[運輸科學系] 研究計畫

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