臺灣博碩士論文加值系統

隨著全球暖化現象加速，環境保護議題逐漸受到重視，各國自1997年起聯合簽署京都議定書以明訂各國排放目標，並將「排放交易」列為彈性減量機制，俾以提供各締約國可透過國際貿易，採用經濟有效率方式達到溫室氣體排放減量之承諾。除此之外，各國政府亦可能藉由課徵能源稅與碳稅，進而改變產業和企業成本結構，促使能源使用量與二氧化碳排放量有效降低。
總體生產規劃主要目的在於追求生產資源之有效配置，其成效影響企業競爭力和營運績效，然而傳統的產能規劃方式，僅針對生產過程所需的原料和人力成本等做評估，並未考量環境污染與能源消耗之成本，為了使產能規劃更能反應污染者付費原則，因此，本研究提出一個加入二氧化碳配額成本考量之整體生產規劃問題，同時將能源使用量與二氧化碳排放量視為目標之一，建立多目標規劃模型，並採用權重法將不同的目標函數給予不同的權重值，使其轉為單一目標，最後利用LINGO軟體求得可行解，以提供決策者整體性的因應策略。個案探討中利用鋼鐵公司進行模式的驗證，並求得最佳產品組合與資源分配，由於模型中考慮了能源與環境因素，企業將不再隨產能擴充而增加獲利，此外，在考慮環境成本下，企業決策者會在特定的價格下調整生產狀況， 以求得總體規劃為最適配置。

As global warming levels accelerate, environmental protection issues have received even more attention. Since 1997, the Kyoto Protocol has been signed by many countries to specify emission targets and to flexibly lessen emissions by “emission trading”, thereby, providing contract parties with an economic and efficient manner of achieving an agreement for greenhouse gas emission reductions. In addition, governments may change their industrial structures and enterprise cost structures by means of energy taxation and carbon tax levying, resulting in reductions in energy utilization and CO2 emissions.
The purpose of aggregate production planning is to achieve the effective distribution of production resources, leading to increased enterprise competitiveness and operational performance. However, traditional capacity planning only focuses on assessing the needed manpower and raw material of the production process, without taking into consideration the cost of environmental pollution and energy consumption. In order for capacity planning to respond effectively, the polluter must pay the principal; therefore, this study’s objective is to further develop aggregate production planning by increasing the cost of the CO2 emission quota to consider both energy utilization and CO2 emissions. The multi-object problem will be converted into a single objective problem using a Weighting Scheme. LINGO software will then be used to acquire feasible solutions, providing decision makers with responsive strategies. A steel company is used as a case study, with model verification conducted to achieve the best product mix and resource allocation. Owning to taking account of energy and environmental factors, the enterprise didn’t increase the profits as an expansion of capacity. Decision makers will adjust conditions of production under a specific price to achieve the best allocation of aggregate production planning.

摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 V
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究範圍與假設 3
第四節 研究流程 3
第五節 論文大綱 4
第二章 文獻探討 6
第一節 整體生產規劃 6
第二節 環境變遷問題 11
第三節 多目標規劃 14
第四節 綠色生產 18
第五節 小結 19
第三章 生產規劃與二氧化碳成本模式 21
第一節 模式說明 21
第二節 生產規劃與二氧化碳成本模型之建構與推導 24
第三節 生產規劃與二氧化碳成本模型之求解方法與流程 27
第四節 小結 27
第四章 實例驗證與敏感度分析 29
第一節 產業環境與個案公司簡介 29
第二節 個案公司資料說明與驗證 30
第三節 敏感度分析 36
第四節 小結 53
第五章 結論與建議 54
第一節 結論 54
第二節 建議 55
參考文獻 56
附錄 60

王小璠 (2005) 多準則決策分析，滄海
許志義 (2003) 多目標決策，五南
台灣碳排放交易推廣協會 http://www.teta.org.tw/
IPCC http://www.ipcc.ch/
Barbarosoglu, G. (2000). An integrated supplier-buyer model for improving supply chain coordination. Production Planning & Control, 11(8), 732-741.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B141-B164.
Bowman, E. H. (1956). Production scheduling by the transportation method of linear programming. Operations Research, 4(1), 100-103.
Bowman, E. H. (1963). Consistency and optimality in managerial decision making. Management Science, 9(2), 310-321.
Cakravastia, A., & Takahashi, K. (2004). Integrated model for supplier selection and negotiation in a make-to-order environment. International Journal of Production Research, 42(21), 4457-4474.
Coase, R. H. (1960). The Problem of Social Cost. The Journal of Law and Economics, 3(1), 1.
Curkovic, S. (2003). Environmentally Responsible Manufacturing: The development and validation of a measurement model. European Journal of Operational Research 146(1), 130-155.
Demirtas, E. A., & Ustun, O. (2008). An integrated multiobjective decision making process for supplier selection and order allocation. Omega-International Journal of Management Science, 36(1), 76-90.
Freitas, A.A., (2005). A critical review of multi-objective optimization in data mining: a position paper. SIGKDD Explorations, 6(2), 77–86.
Holcomb, T. R., & Hitt, M. A. (2007). Toward a model of strategic outsourcing. Journal of Operations Management, 25(2), 464-481.
Holt, C. C., Modigliani, F., & Simon, H. A. (1955). A linear decision rule for production and employment scheduling. Management Science, 2(1), 1-30.
Jones, C. H. (1967). Parametric production planning. Management Science, 13(11), 843-866.
Kanyalkar, A. P., & Adil, G. K. (2007). Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment. International Journal of Production Research, 45, 5329-5353.
Koroneos, C., Michailidis, M., & Moussiopoulos, N. (2004). Multi-objective optimization in energy systems: the case study of Lesvos Island, Greece. Renewable and Sustainable Energy Reviews, 8(1), 91-100.
Kurtulus, I., & Davis, E. W. (1982). Multi-project scheduling-categorization of heuristic rukes performance. Management Science, 28(2), 161-172.
Letmathe, P., and Balakrishnan, N. (2005). Environmental considerations on the optimal product mix. European Journal of Operational Research 167(2), 398-412.
Liang, T. F. (2007). Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives. Production Planning & Control, 18(7), 548-560.
Madlener, R., Antunes, C. H., & Dias, L. C. (2009). Assessing the performance of biogas plants with multi-criteria and data envelopment analysis. European Journal of Operational Research 197(3), 1084-1094.
Malueg, D. A. (1989). Emission credit trading and the incentive to adopt new pollution abatement technology. Journal of Environmental Economics and Management, 16(1), 52-57.
Masud, A. S. M., & Hwang, C. L. (1980). An aggregate production planning model and application of three multiple objective decision methods. International Journal of Production Research, 18(6), 741-751.
Mazzola, J. B., Neebe, A. W., & Rump, C. M. (1998). Multiproduct production planning in the presence of work-force learning. European Journal of Operational Research, 106(2-3), 336-356.
Oliveira, C., & Antunes, C. H. (2004). A multiple objective model to deal with economy-energy-environment interactions. European Journal of Operational Research, 153(2), 370-385.
Padillo, J. M., & Diaby, M. (1999). A multiple-criteria decision methodology for the make-or-buy problem. International Journal of Production Research, 37(14), 3203 - 3229.
Porkka, P., Vepsalainen, A. P. J., & Kuula, M. (2003). Multiperiod production planning carrying over set-up time. International Journal of Production Research, 41(6), 1133-1148.
Taubert, W. H. (1968). A search decision rule for the aggregate scheduling problem. Management Science, 14(6), B343-B359.
Wang, C., Larsson, M., Ryman, C., Grip, C. E., Wikstrom, J. O., & Johnsson, A. (2008). A model on CO2 emission reduction in integrated steelmaking by optimization methods. International Journal of Energy Research, 32(12), 1092-1106.
Wang, R.-C., & Fang, H.-H. (2001). Aggregate production planning with multiple objectives in a fuzzy environment. European Journal of Operational Research, 133(3), 521-536.
Wu, C.-C., & Chang, N.-B.(2008). Evaluation of environmentally benign production program in the textile-dyeing industry (II): a multi-objective programming approach. Civil Engineering and Environmental Systems, 25(1), 1-28.
Zhou, P., Ang, B. W., & Poh, K. L. (2006). Decision analysis in energy and environmental modeling: An update. Energy, 31(14), 2604-2622.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.