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系統識別號 U0002-0107200916190500
中文論文名稱 以滾動週期可允諾存量為基礎之訂單競標決策:模糊方法與遺傳演算法之應用
英文論文名稱 Bidding Decision Based on a Rolling Horizon Available-to-Promise Mechanism: Solution by Fuzzy Approach and Genetic Algorithm
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 97
學期 2
出版年 98
研究生中文姓名 吳孟聰
研究生英文姓名 Meng-Tsung Wu
學號 696630309
學位類別 碩士
語文別 中文
口試日期 2009-06-07
論文頁數 77頁
口試委員 指導教授-鄭啟斌
委員-張昭憲
委員-謝文恭
委員-蕭育如
中文關鍵字 逆向拍賣  競標  進階可允諾量  模糊理論  遺傳演算法 
英文關鍵字 Reverse Auction  Bidding  Advanced Available-to-Promise  Fuzzy Set Theory  Genetic Algorithm 
學科別分類 學科別社會科學管理學
學科別社會科學資訊科學
中文摘要 本研究之目的在以競標方式爭取訂單的供應鏈環境中,提供賣方(供應商)決定其競標價格的決策機制。為了提升供應商之利潤與得標機會,本研究整合競標決策、訂單允諾機制與生產計劃,以交期允諾及生產成本做為競標價格決策之基礎。其中,訂單允諾機制乃以進階可允諾量(Advanced Available-to-Promise, AATP)之觀念為基礎,亦即考慮未來可應用產能之最佳配置。本研究以混合整數規劃(Mixed Integer Programming, MIP)模型來描述競標決策問題,模型中並包含模糊限制式以表達決策者在制定標價時對顧客價格容忍度的認知。在本研究之規劃環境中,訂單的允諾乃採批次處理與滾動規劃週期的設計,亦即每隔一固定期間,供應商會重新審視所收到而未允諾或已允諾但尚未完成生產之訂單,並重新執行上述之決策模式以更新生產計畫。模型之求解過程包含以模糊方法搜尋最大利潤與最大得標機會之妥協解,並以遺傳演算法(Genetic Algorithm, GA)實作求解程序。本研究以電腦模擬的方式進行實驗以驗證本研究方法之績效。
英文摘要 This study integrates the bidding decision and production planning based on the concept of advanced available-to-promise (AATP) inventory with a rolling planning horizon. Customer requests arrive in a random fashion, and bidding decisions are made for a batch of requests collected over a batching interval. This decision process repeated for every specified batching interval, and the current decision-making must take into account the previously committed orders in earlier phases. The problem is formulated as a mixed integer programming model with fuzzy constraints, which express the decision-maker’s subjective judgment regarding customer’s price tolerance. The proposed model embeds the AATP concept to support accurate computation of profit and customer order promising. A genetic algorithm is developed to solve the problem. Performance of the proposed approach is evaluated through experiments conducted by computer simulation.
論文目次 目錄
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究步驟與流程 3
第二章 文獻探討 5
2.1 電子商務(Electronic Commerce, EC) 5
2.2 逆向拍賣(Reverse Auction) 7
2.3 可允諾量(Available-to-Promise, ATP) 11
2.3.1 傳統可允諾量 11
2.3.2 進階可允諾量(Advanced Available-to-Promise, AATP) 12
第三章 研究方法 15
3.1 競標決策模型的建立 15
3.1.1 模型假設 16
3.1.2 混合整數規劃的競標決策模型 16
3.1.3 模糊方法的求解 23
3.2 演算法 26
3.2.1 遺傳演算法(Genetic Algorithms, GA) 26
3.2.2 演算法流程 28
3.2.2.1 主演算法(Main Algorithm)流程 29
3.2.2.2 遺傳演算法(Genetic Algorithm)流程 31
3.2.2.3 調整基因解(Solution Adjustment)流程 36
3.3 範例說明 43
3.3.1 遺傳演算法執行範例 43
3.3.2 調整基因解調整範例 46
3.3.3 滾動規劃執行範例 52
第四章 實驗與模擬分析 55
4.1 單期規劃 56
4.1.1 實驗參數設計 57
4.1.2 實驗結果與分析 59
4.1.2.1 問題規模 59
4.1.2.2 產能大小 61
4.1.2.3 顧客要求交單的數量區間 62
4.1.2.4 顧客要求的交單時間區間 63
4.2 滾動週期規劃 64
4.2.1 實驗參數設計 65
4.2.2 實驗結果與分析 67
第五章 結論與建議 70
參考文獻 72

圖目錄
圖1 1研究流程架構圖 4
圖2 1依交易對象分類的電子商務類型 6
圖2 2逆向拍賣流程(Cheng, 2008) 9
圖3 1買方對競標價格的歸屬函數 23
圖3 2目標值P之歸屬函數 25
圖3 3演算法流程 29
圖3 4一個考慮T期、生產N張訂單的染色體 32
圖3 5調整基因解流程 38
圖3 6產能重新分配之過程 48
圖3 7產能移除的過程 49
圖3 8訂單移除的過程 51
圖4 1不同滾動規劃週期對淨利潤的影響 67
圖4 2不同滾動規劃週期對訂單拒絕成本的影響 68
圖4 3不同滾動規劃週期對持有成本的影響 69

表目錄
表2 1各種拍賣的機制(Brandt, 2003) 7
表3 1範例1的參數設定 44
表3 2遺傳演算法得出的生產排程計畫(摘錄α=0.1與α=0.2) 45
表3 3 max-min下的最佳決策 46
表3 4範例2的參數設定 47
表3 5調整前的生產排程計畫(摘錄α=0.1) 47
表3 6範例3第一次新進來的訂單參數設定 52
表3 7滾動規劃第一次檢視的最佳結果 53
表3 8範例3第二次新進來的訂單參數設定 53
表3 9滾動規劃第二次檢視的最佳結果 54
表4 1環境設定 55
表4 2實驗群組之參數設定(Cheng, 2009b) 57
表4 3實驗群組1的計算結果 59
表4 4實驗群組2的計算結果 61
表4 5實驗群組3的計算結果 62
表4 6實驗群組4的計算結果 63
表4 7各期進來的訂單數 66

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