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論文中文名稱:倉儲儲位指派問題之穩健最佳化方法 [以論文名稱查詢館藏系統]
論文英文名稱:A Robust Storage Assignment Approach for Warehouse Management [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與運籌管理研究所
畢業學年度:99
出版年度:100
中文姓名:蘇福泰
英文姓名:Fu-Tai Su
研究生學號:98938014
學位類別:碩士
語文別:中文
口試日期:2011-06-27
論文頁數:46
指導教授中文名:吳建文
口試委員中文名:羅淑娟;陳鵬文
中文關鍵詞:儲位指派不確定性穩健最佳化萬用啟發式演算法
英文關鍵詞:storage assignmentuncertaintiesrobust optimizationmetaheuristic
論文中文摘要:倉儲是供應鏈中不可或缺的一個環節,因為一個良好的倉儲可以幫助供應商對於供應鏈系統的決策上有更好的彈性發揮空間,可是管理一個完善倉儲的代價非常昂貴。在倉儲一連串的活動中,揀貨作業占了所有作業的百分之五十五,因此為了降低揀貨成本,如何做出良好的倉儲儲位指派決策是一個非常重要的問題。可是在現實生活中,有許多不確定性因素讓訂單中商品出現的機率受到干擾,而增加儲位指派決策的難度。為了妥善地將實際狀況的不確性因素通盤地考慮進去,本篇論文針對不確定性因素提供一個倉儲儲位指派問題的穩健最佳化方法。穩健最佳化是用來處理不確定性資料的最佳化問題,利用後悔值的概念可以保證即使在最糟糕的不確定情境下,我們儲位指派方式的效能仍然可以逼近此情境下最佳的效能。此外,本篇論文還針對計算後悔值部分提供一個可以在O(n^3)時間內完成的演算法,進而提升穩健最佳化在所有情境中找尋所有可能決策之最大後悔值的計算效率。最後本篇論文根據萬用啟發式演算法的概念利用貪婪演算法結合迭代區域搜尋演算法來計算倉儲儲位指派之穩健最佳化問題的最佳解。除了所求的可行解可以更逼近理想的最佳解,而且此可行解即使在最糟糕的不確定性情境下仍然有不錯的表現。
論文英文摘要:The warehouse is an important part of supply chain. Among all the warehousing activities, order picking is estimated to account for 55% of the total operating costs. Therefore, storage assignment, a critical component of order picking, deserves serious attentions. In real applications, uncertainties on picking frequencies exist and complicate the order picking task. In this paper, we propose a robust storage assignment approach. And we propose an algorithm whose complexity is O(n^3) to calculate the maximum regret of the robust storage assignment problem. Finally, we propose a metaheuristic which combines iterated local search algorithm with greedy algorithm for the solutions. The solution algorithm guarantees near optimal performance even in the worst possible scenario.
論文目次:摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
第二章 文獻探討 5
2.1 倉儲管理 5
2.2 倉儲儲位指派決策 6
2.2.1 隨機儲位指派政策 7
2.2.2 體積計量儲位指派政策 8
2.2.2.1 COI 儲位指派方法 8
2.2.2.2 DTI儲位指派方法 8
2.2.3 分級儲位指派政策 8
2.3 在單一階層生產下的穩健排程最佳化問題 9
2.4 迭代區域搜尋演算法 11
第三章 問題描述與研究方法 14
3.1 問題描述 14
3.1.1 機率的不確定性 14
3.1.2 面臨的障礙 16
3.1.2.1 如何妥善處理儲位指派決策的機率不確定性 16
3.1.2.2 本研究為NP-hard問題 17
3.1.3 實際應用範圍 17
3.2 參數定義與數學模型 18
3.2.1 基本假設 18
3.2.1.1 倉儲矩陣 18
3.2.2 數學模型 20
3.3 最大後悔值演算法 24
3.4 研究方法 28
3.4.1 演算方法選擇 29
3.4.1.1 一般性 29
3.4.1.2 有效性 29
3.4.1.3 與排程問題的相似性 30
3.4.2 產生初始解 30
3.4.2.1 貪婪演算法 30
3.4.3 迭代區域搜尋演算法 32
3.4.3.1 鄰居挑選 34
3.4.3.2 更新最佳解 34
3.4.3.3 擾動最佳解 35
3.4.4 研究方法演算流程 36
第四章 實驗結果與分析 37
4.1 實驗設計 37
4.2 實驗結果 37
4.3 實驗分析 40
4.3.1 項目數的影響 40
4.3.2 改善空間 41
第五章 結論與展望 42
5.1 結論 42
5.2 展望與建議 43
5.2.1 結合其他的初始解產生方法 43
5.2.2 利用其他的資料產生方式 43
參考文獻 44
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