本研究在多個供應商、裝配廠、配銷中心的生產-配送網路加入料品替代策略，探討選擇哪種替代產品及替代零件，使得生產-配送網路總利潤最大化，因此，本研究建立一個具有料品替代策略的兩階供應鏈整數規劃模型(Production-Distribution Integer Model with Item Substitution Strategy,PDISS)。有鑒於PDISS模型為一混整數數學規劃問題，本研究以成本、價格、價值、替代程度四個層面，設計成本啟發式演算法、價格啟發式演算法、價值啟發式演算法、替代啟發式演算法，求得啟發解。
本研究以啟發解品質、啟發解穩定度、執行效率評估所提出之四個啟發式方法。評估結果可知，四種啟發式演算法的執行效率優於Lingo軟體，但啟發解品質仍有改進之空間。實驗發現，四種啟發式演算法的缺貨損失過高，是影響最佳解品質的主因之一，因此改善缺貨損失過高的情況，才能提昇啟發式演算法的啟發解品質。

Using item substitution strategy is a way to cope with the supply uncertainty in production and distribution networks. The substitution strategy provides the flexibility of replacing preset items with substitutes to fulfill demands in a dynamic environment. To use the strategy effectually, a systematic method is required. This study proposes a Production-Distribution Integer Model with Item Substitution Strategy (PDISS) model to formulate the production-distribution network with item substitution strategy. This study is to develop four heuristic algorithms with cost, price, value, degree of substituting. In addition, Heuristic algorithm is applied to solve production-distribution problems of the PDISS model.

Four heuristic methods are appraisal with the heuristic solution quality, the heuristic solution stability, to carry out time of efficiency. The appraisal result may know, four heuristic methods carry out the efficiency to surpass the Lingo software, but the heuristic solution quality still had space of the improvement. The experiment discovered that four heuristic calculating method''s stock outs lose excessively high, is one of influence best solution quality principal factors, therefore the improvement stock out has lost the high situation, can promote the heuristic calculating method the inspiration solution quality.

摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VIII
圖目錄 IX
第一章 緒論 1
1.1研究背景與動機 1
1.2研究問題與目的 1
1.3論文架構 2
第二章 文獻回顧 4
2.1替代策略相關文獻 4
2.1.1產品替代 5
2.1.2零件替代 6
2.2多階層供應鏈網路相關文獻 9
2.2.1單期供應鏈網路 10
2.2.2多期供應鏈網路 11
2.3結論 16
第三章 生產-配送網路料品替代策略之整數規劃模型(Production-Distribution Integer Model with Item Substitution Strategy, PDISS) 17
3.1環境假設 18
3.2數學符號定義 19
3.2.1下標定義 19
3.2.2參數定義 20
3.2.3決策變數定義 21
3.3 PDISS模型 22
3.3.1目標函數 23
3.3.1.1總銷售收入 23
3.3.1.2零件訂購總成本 24
3.3.1.3零件運輸總成本 25
3.3.1.4零件存貨總成本 25
3.3.1.5 產品製造總成本 26
3.3.1.6 產品運輸總成本 26
3.3.1.7 總缺貨損失 27
3.3.1.8目標函數總利潤最大化 27
3.3.2限制式 28
3.3.2.1供應商限制式 28
3.3.2.2裝配廠限制式 28
3.3.2.3配銷中心限制式 30
3.3.2.4替代限制式 32
第四章 啟發式演算法 36
4.1成本導向啟發式演算法(Cost Heuristics) 36
4.2價格導向啟發式演算法(Price Heuristics) 39
4.3價值導向啟發式演算法(Value Heuristics) 42
4.4替代導向啟發式演算法(Sub Heuristics) 45
第五章 啟發式演算法績效評估 49
5.1實驗環境 49
5.2實驗結果評估 52
5.2.1啟發解品質 53
5.2.2啟發解穩定度 57
5.2.3執行效率 58
5.3實驗結果分析 63
5.3.1小供應鏈 63
5.3.2中供應鏈 64
5.3.3 大供應鏈 66
5.3.4結論 67
第六章 結論與未來研究方向 69
6.1結論 69
6.2未來研究方向 70
參考文獻 71
表2.1 替代類型整理定義 7
表2.2 替代相關文獻整理 8
表2.3 供應鏈網路相關文獻整理 13
表2.4 供應鏈文獻比較 15
表5.1 供應鏈範圍大小定義 50
表5.2 參數的設定範圍 51
表5.3 小供應鏈啟發式演算法之Solution -GAP 54
表5.4 中供應鏈啟發式演算法之Solution -GAP 55
表5.5 大供應鏈啟發式演算法之Solution -GAP 56
表5.6 啟發式演算法之變異數. 57
表5.7 小供應鏈啟發式演算法之Time-GAP 60
表5.8 中供應鏈啟發式演算法之Time-GAP 61
表5.9 大供應鏈啟發式演算法之Time-GAP 62
圖2.1 文獻回顧 4
圖3.1 生產-配送網路圖 18
圖5.1 啟發解品質之穩定度 58
圖5.2 小供應鏈啟發式演算法之銷售收入 64
圖5.3 小供應鏈啟發式演算法之各項成本 64
圖5.4 中供應鏈啟發式演算法之銷售收入 65
圖5.5 中供應鏈啟發式演算法之各項成本 66
圖5.6 大供應鏈啟發式演算法之銷售收入 67
圖5.7 大供應鏈啟發式演算法之各項成本 67

[1] 林上襄(2002)，即時回應機制下最佳替代策略之建構，碩士論 文，國立中央大學工業管理系，中壢。
[2] 詹宗泰(2001)，考慮服務水準下具替代效果之多品項間的最佳訂價及訂貨政策，碩士論文，國立中央大學工業管理系，中壢。
[3] C. Arntzen, G. Brown, P. Harrison, and L. Trafton(1995), ”Global supply chain management at Digital Equipment Corporation,” Interfaces, Vol. 25, No. 1, pp. 69-93.
[4] A. Balakrishnan, and J. Geunes(2000), “Requirement Planning with Substitutions:Exploring Bill-of-Materials Flexibility in Production Planning,” Manufacturing &Service Operations Management, Vol.2, pp.166-185.
[5] B. Bilge(2010), ”Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem,” Expert Systems with Applications, Vol.37, pp. 4488–4495.
[6] S. Chand, E. James, and K. Weng(1994), “A parts selection model with one-way substitution,” European J. Operation Research, Vol.73, pp. 65-69.
[7] H. Christian, and K. Josef(2000), ” Optimal planning in large multi-site production networks,” European Journal of Operational Research, Vol. 126, No. 2, pp. 422-435.
[8] F. David, and A. Morris(1994), “Multiproduct production-distribution systems,” European Journal of Operational Research, Vol. 74, No. 1, pp. 18-49.
[9] B. Fahimnia., L. Luong, and R. Marian(2008), ”An integrated model for the optimization of a two-echelon supply network,” Journal of Achievements in Materials and Manufacturing Engineering, Vol. 31, No. 2, pp. 477-484.
[10] P. Guruprasad(2009), “Joint cyclic production and delivery scheduling in a two-stage supply chain,” Int. J. Production Economics, Vol. 119, pp. 55–74.
[11] P. Hasan,, and J. Vaidyanathan(1998), ”A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution,” Computers & Operations Research, Vol. 25, No. 10, pp. 869-878.
[12] S. Wang(2009), “A two-phase ant colony algorithm for multi-echelon defective supply chain network design,” European Journal of Operational Research, Vol. 192, pp. 243–252.
[13] J. Boissie`re, Y. Frein, and C. Rapine(2008), “Optimal stationary policies in a 3-stage serial production-distribution logistic chain facing constant and continuous demand,” European Journal of Operational Research, Vol. 186, pp. 608–619.
[14] K. Das, and S. Sengupta(2009), “A hierarchical process industry production–distribution planning model,” Int. J. Production Economics, Vol. 117, pp. 402–419.
[15] T. Panagiotis, and P. Lazaros(2008), “Optimal production allocation and distribution supply chain networks,” International Journal of Economics, Vol.111, No. 2, pp. 468-483.
[16] F. Reza, and E. Mahsa(2008), “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain,” Int. J. Production Economics, Vol. 111, pp. 229-243.
[17] S. Stephen, and A. Narendra(2000),“Management of muti-item retail inventory systems with demand substitution,” Operation Research, Vol.148, No.1, pp. 50-64.
[18] L. Tien, and C. Hung(2009), ”Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains,” Expert Systems with Applications, Vol. 36, pp. 3367–3377.
[19] L. Young, and K. Sook(2002), “Production–distribution planning in supply chain considering capacity constraints,” Computers & Industrial Engineering, Vol.43, No. 1-2, pp. 169-190.
[20] K. Young, Y. Choamun, and P. Seung(2008), “An integrated model of supply network and production planning for multiple fuel products of multi-site refineries,” Computers and Chemical Engineering, Vol. 32, pp. 2529–2535.
[21] G.. Vahidreza, A. Hamid, H. Amir, and R. Meisam(2010), “A heuristic approach for designing a distribution network in a supply chain system,” African Journal of Business Management, Vol. 4, No. 3, pp. 308-311.