近數十年以來，供應鏈管理已被公認為企業成功的重要因素。在能源耗盡與環境保護意識成長的同時，如何在設計供應鏈網路時兼顧成本、服務品質、及環境考量等，愈趨關鍵。同時間，雲端服務與雲端運算在學術研究與業界實務均有革命性發展。透過虛擬機在實體伺服器上的最佳配置，運算資源可有效的運用，使得綠色運算更為可行。本研究將機於建構一結合雲端運算之二階段綠色供應鏈網路設計之進行改良並進行比較。在供應鏈網路設計的問題考慮三個目標方程式：最小化總成本、最小化碳排放量、及最大化服務滿意水準。在供應鏈網路中配置虛擬機的動態多目標模式，考慮三個目標式：最小化能源消耗、最大化實體伺服器的有效運用率。本研究將利用數量實驗以比較模式之差異性。

In the past decades, supply chain management has been recognized as the key factor to business success. With the increasing awareness of energy consumption and environmental protection, how to view the cost, quality of service and environmental issues in supply chain network design has been a key issue. At the same time, cloud services and cloud computing have revolutionized academic research and industrial practices. Computing resources can effectively utilize the optimal allocation of virtual machines (VMs) between physical machines to bring green computing closer to implementation. This study aims to establish a two-level model of green supply chain network design by integrating cloud computing. In the first level, supply chain network design was achieved by considering three goals: minimizing total costs, minimizing carbon emissions and maximizing service satisfaction. In the second level, a model of supply chain node, for virtual machine placement is established through multi-objective programming to minimize energy consumption, and maximize the efficiency of physical machines. Two phases and integrated solution approaches are implemented. This study will use quantitative experiments to verify and compare the approaches.

誌 謝 iii
摘要 v
Abstract vii
目錄 ix
圖目錄 xi
表目錄 xii
第一章、緒論 1
第一節、研究動機 1
第二節、研究目的 1
第二章、文獻探討 3
第一節、綠色供應鏈網路設計 3
第二節、虛擬機與實體機配置 4
第三節、多目標規劃 5
第三章、研究方法 7
第一節、多目標最佳化綠色供應鏈網路設計模型 7
1.問題定義 7
2.模型建構 9
2.1 非協作二階層決策模型 11
2.2 協作整合決策模型 14
2.3多目標規劃的解決過程 15
第二節、求解多目標規劃 17
第四章、研究結果 19
第一節、多目標最佳化綠色供應鏈網路設計個案 19
1.供應鏈模型參數導入 19
2.虛擬機配置模型參數導入 22
第二節、非協作二階段決策模型求解之結果 23
第三節、協作決策模型求解之結果 28
第四節、非協作二階段決策模型與協作決策模型之差異 33
第五章、結論 35
參考文獻 37
附錄一:我國燃料熱值係值表[經濟部能源局出版] 39
附錄二:十次求各項目標最佳解 42
附錄三: LINGO程式碼 43

[1]藍梓恆，結合雲端運算之綠色供應鏈網路設計，台灣商管與資訊研討會2016年。
[2]R.B. Handfield, “Green supply Chain: Best Practices From the Furniture Industry,” Proceedings Annual Meeting of the Decision Sciences Institute., USA. No.3. pp. 1295- 1297, 1996.
[3]B.M. Beamon, “Designing the green supply chain,” Logistics Information Management., vol. 12 Iss: 4, pp.332-342, 1999.
[4]N. Chibeles-Martins, T. Pinto-Varela, A. P. Barbosa-Póvoa, A. Q. Novais, “A multi-objective meta-heuristic approach for the design and planning of green supply chains – MBSA,” Expert Systems with Applications., vol. 47, pp. 71-84, 2016.
[5]A. Tognetti, P.T. Grosse-Ruyken, S. M. Wagner, “Green supply chain network optimization and the trade-off between environmental and economic objectives,” International Journal of Production Economics., vol. 170, Part B, pp. 385-392, 2015.
[6]G. Kannan, J. Ahmad, N. Vahid, “Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic,” Computers & Operations Research., vol. 62, pp. 112-130, 2015.
[7]N. Gholamian, I. Mahdavi, R. Tavakkoli-Moghaddam, N Mahdavi-Amiri, “Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty,” Applied Soft Computing., vol. 37, pp. 585-607, 2015.
[8]A. Ghodratnama, R. Tavakkoli-Moghaddam, A. Azaron, “Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview,” Applied Soft Computing., vol. 37, pp. 255-276, 2015.
[9]F. Wang, X.F Lai, N. Shi, “A multi-objective optimization for green supply chain network design,” Decision Support Systems., vol. 51, Issue 2, pp. 262-269, 2011.
[10]Ş.Y. Balaman, H. Selim, “A decision model for cost effective design of biomass based green energy supply chains,” Bioresource Technology., vol. 191, pp. 97-109, 2015.
[11]M.S. Pishvaee, J. Razmi, “Environmental supply chain network design using multi-objective fuzzy mathematical programming,” Applied Mathematical Modelling., vol. 36, Issue 8, pp. 3433-3446, 2012.
[12]A. Guazzelli, K. Stathatos, M. Zeller, “Efficient deployment of predictive analytics through open standards and cloud computing,” ACM SIGKDD Explorations Newsletter., vol. 11, pp. 32-38, 2009.
[13]C.L.P. Chen and C.Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: a survey on big data,” Information Sciences., vol. 275, pp. 314-347, 2014.
[14]D.J. Abadi, “Data management in the cloud: limitations and opportunities,” IEEE Data Engineering Bulletin., vol. 32, pp. 3-12, 2009.
[15]Y. Gao, H. Guan, Z. Qi, Y. Hou, and L. Liu, “A multi-objective ant colony system algorithm for virtual machine placement in cloud computing,” J. Comput. Syst. Sci., vol. 79, pp. 1230-1242, December 2013.
[16]F. Ramezani, J. Lu, and F.K. Hussain, “Task-based system load balancing in cloud computing using particle swarm optimization,” Int. J. Parallel Prog., vol. 42, pp. 739-754, October 2014.
[17]Y. Zhao, Y. Huang, K. Chen, M. Yu, S. Wang, and D.S. Li, “Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks,” Comput. Net., vol. 80, pp. 109-123, April 2015.
[18]R.C. Coutinho, L.M.A. Drummond, Y. Frota, and D. Oliveira, “Optimizing virtual machine allocation for parallel scientific workflows in federated clouds,” Future Gener. Comput. Syst., vol. 46, pp. 51-68, May 2015.
[19]W. L. Winston, Operations Research: Applications and Algorithms, 4th ed. Canada: Thomson Learning, 2004.
[20]L. A. Zadeh, “Fuzzy sets,” Information and Control 8, pp. 338-353, 1965.
[21]M. Jiménez, M. Arenas, A. Bilbao, M. Victoria Rodrı’guez, “Linear programming with fuzzy parameters: An interactive method resolution,” European Journal of Operational Research., vol. 177, Issue 3, pp. 1599–1609, March 2007.
[22]H. J. Zimmermann, “Fuzzy programming and linear programming with several objective functions,” Fuzzy Sets and Systems., 1, pp. 45-55. 1978.