# 臺灣博碩士論文加值系統

(44.192.22.242) 您好！臺灣時間：2021/08/01 12:26

:::

### 詳目顯示

:

• 被引用:8
• 點閱:270
• 評分:
• 下載:87
• 書目收藏:0
 生產計劃問題(production planning problem)充滿了許多不確定因素。大部分的生產計劃模型研究為了簡化問題以快速求得生產計劃，通常以確定性的模式來求解生產計劃。本論文將不確定性因素納入考量，對生產計劃問題進行研究探討。Thompson and Wayne【1990】所提出的整合模式方法(integrated modeling approach)僅能找出完美資訊期望值(expected value with perfect information, EVwPI)，無法直接找出一組生產計劃的解。因此本論文利用決策樹分析(decision tree analysis)求解隨機生產計劃問題，以具體的方式表達隨機需求的生產計劃問題。當決策樹建構完畢時，再利用由決策樹的末端往前歸納推算的程序(backward induction procedure)，計算出最佳期望目標值，並找出一組生產計劃的解。每次在做生產計劃時，可以輸入新的產品需求預測，將決策樹重新展開，去找到下一個生產時期的生產計劃。實驗結果顯示，決策樹分析除了能找出一組生產計劃解，且以該方法求得的期望獲利值也非常接近完美資訊期望值。
 Production planning problems have many uncertain factors. Most of production planning models use deterministic parameters to simplify production plan problem and in order to find production plan quickly. This study considers uncertainties in production planning problem.Thompson and Wayne (1990) proposed an integrated modeling approach which can only provide the expected value with perfect information (EVwPI), but it cannot provide a production plan. This study applies decision tree analysis to solve stochastic production planning problem. We express production planning problem with stochastic demand. After a decision tree being constructed, we can compute the expected objective value by using backward induction procedure to find an optimal production plan.When a production plan must be calculated, we can input new product demand forecast information, and reconstruct the decision tree to find the production quantity of the next period. The result of our experiment shows that the decision tree analysis not only be able to find a set of production plan quickly but also make the expected value of the decision tree analysis close to the expected value with perfect information.
 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 21.3 研究方法 31.4 研究架構與大綱 5第二章 文獻探討 72.1 不確定性的建構 72.2 穩健最佳化方法 92.3 整合模式方法 102.4 決策樹分析 132.5 決策樹的基本做法 17 2.5.1 決策樹的建構 17 2.5.2 決策樹的計算 20 2.5.2.1 機會點的計算 20 2.5.2.2 決策點的計算 22 2.5.3 決策樹的結果 232.6 文獻探討之結論 24第三章 方法建構 263.1 問題描述 263.2 問題模式化 273.3 決策樹分析 29 3.3.1 決策樹的建構 30 3.3.1.1 決策樹的展開 30 3.3.1.2 決策點下生產計劃數目的縮減 34 3.3.1.3 決策樹的評估 38 3.3.2 決策樹的計算 423.4 決策樹分析的結果 43第四章 實驗結果與分析 444.1 實例探討 44 4.1.1 決策樹分析 45 4.1.1.1 決策樹的建構 45 4.1.1.2 決策樹的計算 49 4.1.2 決策樹分析的結果 504.2 問題參數設定與說明 514.3 實驗方法與結果分析 56 4.3.1 隨機需求數目 56 4.3.2 需求與產能的比值 59第五章 結論 62文獻參考 63
 參考文獻Ballestero E. (2001), “Stochastic goal programming: a mean-variance approach”, European Journal of Operational Research, Vol. 131, pp. 476-481.Beale E. (1955), “On minimizing a convex function subject to linear inequalities”, Journal of the Royal Statistical Society, B 17, pp. 173-184.Birge J. R. (1997), “Stochastic programming computation and applications”, INFORMS Journal on Computing, Vol. 9, No. 2, pp. 111-133.Charnes A. and Cooper W. W. (1959), “Chance-constrained programming”, Management Science, Vol. 5, pp. 73-79.Chen X. (2000), “Newton-type methods for stochastic programming”, Mathematical and Computer Modeling, Vol. 31, pp.89-98.Coles S. and Rowley J. (1995), “Revisiting decision trees”, Management Decision, Vol. 33, No. 8, pp. 46-50.Danzig G. B. (1955), “Linear programming under uncertainty”, Management Science, Vol. 1, pp. 197-206.Dupa?ova J. (2002), “Applications of stochastic programming: achievements and questions”, European Journal of Operational Research, Vol. 140, pp. 281-290.Hillier F. S. and Lieberman G. J. (2001), Introduction to Operations Research, 7th edition, McGraw-Hill, New York, NY, U.S.A..James M. (1995), “Decision tree analysis - choosing between options by projecting likely outcomes”, Mind Tools, http://www.mindtools.com/dectree.html.Liu B. (1997), “Dependent-chance programming: a class of stochastic programming”, Computers Mathematics Application, Vol. 34, No. 12, pp. 89-104.Liu B. (2001), “Uncertain programming: a unifying optimization theory in various uncertain environments”, Applied Mathematics and Computation, Vol. 120, pp. 227-234.Masaru T. and Masahiro H. (2003), Genetic algorithm for supply planning optimization under uncertain demand, Genetic and Evolutionary Computation-GECCO 2003: Genetic and Evolutionary Computation Conference, Chicago, IL, U.S.A., pp. 2337-2346.Papadopoulos C. E. and Yeung H. (2001), “Uncertainty estimation and Monte Carlo simulation method”, Flow Measurement and Instrumentation, Vol. 12, pp. 291-298.Shapiro J. F. (2001), Modeling the Supply Chain, 1st edition, Duxbury press, CA, U.S.A..Thompson S. D. and Wayne J. D. (1990), “An integrated approach for modeling uncertainty in aggregate production planning”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 5, pp. 1000-1012.Wets R. J-B (1996), “Challenges in stochastic programming”, Mathematical Programming, Vol. 75, pp. 115-135.Zimmermann H. -J. (2000), “An application-oriented view of modeling uncertainty”, European Journal of Operational Research, Vol. 122, pp. 190-198.
 電子全文
 國圖紙本論文
 推文當script無法執行時可按︰推文 網路書籤當script無法執行時可按︰網路書籤 推薦當script無法執行時可按︰推薦 評分當script無法執行時可按︰評分 引用網址當script無法執行時可按︰引用網址 轉寄當script無法執行時可按︰轉寄

 1 學生使用數位學習平台之學習行為研究──以BB網路教室為例 2 決策樹於產能分配之應用-以記憶卡封裝測試代工廠為例 3 利用資料挖礦找出影響產品品質之關鍵因素:以LED封裝為例 4 以隨機規劃與決策樹分析求解不確定需求之產能規劃問題 5 以決策樹分析用黑白棋為例檢核國小學生學習能力 6 現值法與決策樹在建築投資應用之研究 7 應用知識發現理論於土石流災害分析-以陳有蘭溪為實證 8 高中選系輔導系統的建置與評估

 無相關期刊

 1 決策樹規則及資料點之排序 2 整合決策樹與關聯規則之資料挖礦架構及其實證研究 3 決策樹運用於銀行之詐騙帳戶 4 以決策樹演算法建構台灣企業財務危機預警模式 5 以決策樹分析顧客滿意度之研究 6 以決策樹分析台灣上市櫃紡織業公司的財務危機 7 從關聯規則集中建立分類決策樹 8 應用決策樹與濾嘴法則於股票投資 9 應用決策樹歸納法探討台灣行動電話市場區隔 10 利用決策樹、邏輯斯迴歸及類神經網路複合模型分析公開微陣列資料集乳癌復發基因及其效能之探討 11 行動電話系統業服務品質滿意度之研究─應用統計分析與決策樹法 12 電腦輔具選用決策樹邏輯之簡化與評估 13 應用CART決策樹與資料視覺技術於低良率晶圓成因探討 14 運用決策樹於腦部核磁共振影像之監督式腦組織分割研究 15 探討糖尿病患服藥前後影響因素---利用決策樹分類評估

 簡易查詢 | 進階查詢 | 熱門排行 | 我的研究室