進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2412201315390300
論文名稱(中文) 應用模糊多目標規劃於考量環境成本之總體生產規劃
論文名稱(英文) Applying Fuzzy Multiple Objective Programming Method to the Aggregate Production Planning with the Environmental Issues
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 102
學期 1
出版年 102
研究生(中文) 王柏元
研究生(英文) Po-Yuan Wang
學號 R36014090
學位類別 碩士
語文別 中文
論文頁數 95頁
口試委員 口試委員-王逸琳
口試委員-陳梁軒
口試委員-林君維
指導教授-王泰裕
中文關鍵字 總體生產規劃  模糊多目標規劃  碳權交易  綠色租稅 
英文關鍵字 aggregate production planning  fuzzy multiple objective programming  carbon trading  green tax 
學科別分類
中文摘要 全球暖化造成嚴重的氣候變遷,國際上紛紛提出因應措施以降低溫室氣體的排放量。2005年具有約束力之「京都議定書」正式生效,其所提出之三項彈性減量機制允許國家與企業間進行碳權交易,使用經濟且有效率的方式達到溫室氣體排放減量的目的。此外,各國政府亦可藉由課徵綠色租稅,如能源稅、碳稅,促使各企業之能源使用量與二氧化碳排放量降低。因此,企業於總體生產規劃中應加入環境成本之考量。
本研究將環境成本納入生產規劃決策問題中,並考量決策者對於目標之不確定性與市場環境因素所造成之模糊性,使模型更符合實際情況。本研究將以企業的角度,考慮決策目標、市場需求和環境成本之不確定性,透過模糊多目標規劃方法建構參考模型。由於決策者對於不確定之考量皆不同,本研究延伸參考模型探討兩種特例情境並建構各情境之模型。首先,針對決策者之不確定目標與模糊性參數建立隸屬函數與設定三角可能性分配;接著採用二階段方法,將模糊多目標線性規劃問題轉為確定性單一目標線性問題;最後利用LINGO軟體求得可行解。在模型驗證中,本研究以鋼鐵公司作為驗證對象,針對不同情境模擬資訊的不確定性,求得三種情境之最佳解與最適生產配置。最後針對模型中的參數進行敏感度分析,探討參數變動時對三項目標值及最適生產決策的影響。期望能提供企業在面臨環境法規及不同不確定情境時的輔助決策。
英文摘要 Global warming has caused serious climate change, therefore international organizations proposed some countermeasures to reduce the greenhouse gas emissions. The Kyoto Protocol has been take effect formally in 2005, which included flexible mechanisms to carry out the carbon trading between countries and companies. In addition, governments may impose green tax, such as energy tax and carbon tax, which will urge the enterprises to reduce the energy utilization and carbon dioxide emissions. In short, aggregate production planning should take into consideration the cost of environmental pollution.
In this study, we introduce the environmental costs into production plans and take into account the uncertainty of managers’ goals and fuzzy environment to meet with practical environmental. We will consider about uncertainty of objective, market demand and environmental costs from perspective of enterprise, and formulate the reference model through fuzzy multiple objective programming. Due to different considerations of decision makers, we extended reference model to discuss two special cases and formulated model under different situation. First, establish the membership function of fuzzy goals and coefficients; the fuzzy multiple objective linear programming problems will then be converted into a determinate single objective linear problem; at last, LINGO software will be used to find the feasible solutions. A steel company is used as a validation object for this study. In the final submission, we make analyze the sensitivity on parameters to discuss their influence on the goal values and optimal production plans.
論文目次 摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VIII

第一章 緒論1
第一節 研究背景與動機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4
第五節 論文大綱5

第二章 文獻探討6
第一節 全球暖化問題6
第二節 總體生產規劃10
第三節 模糊數學規劃13
第四節 綠色生產規劃16
第五節 小結19

第三章 模糊生產規劃與環境成本模型20
第一節 問題描述20
第二節 參考模型:考慮目標、市場需求、環境成本不確定之模型26
第三節 特例情境一:考慮目標、市場需求不確定之模型32
第四節 特例情境二:考慮目標不確定之模型35
第五節 模型求解36
第六節 小結40

第四章 模型驗證與分析42
第一節 產業背景簡介42
第二節 個案公司資料說明與驗證43
第三節 結果討論54
第四節 敏感度分析56
第五節 小結88

第五章 結論與建議90
第一節 結論90
第二節 未來研究方向與建議91
參考文獻92

表目錄
表2-1 模糊多目標規劃模式整理14
表4-1 市場價格44
表4-2 單位生產成本(正常工時)44
表4-3 單位生產成本(超額工時)44
表4-4 存貨成本44
表4-5 缺貨候補成本44
表4-6 能源耗用係數45
表4-7 二氧化碳排放係數45
表4-8 產品需求量45
表4-9 最大缺貨量45
表4-10 最大存貨量45
表4-11 機器產能限制(正常工時)45
表4-12 機器產能限制(超額工時)46
表4-13 期初存貨與缺貨候補46
表4-14 產品需求量(三角模糊數)47
表4-15 產品需求量(三角模糊數轉換為單一值)48
表4-16 環境成本(三角可能性分配)48
表4-17 參考模型理想目標值與彈性容忍範圍49
表4-18 參考模型三項目標之最佳可行解49
表4-19 參考模型各產品各期最佳生產狀況50
表4-20 特例一理想目標值與彈性容忍範圍51
表4-21 特例一三項目標之最佳可行解51
表4-22 特例一各產品各期最佳生產狀況52
表4-23 特例二理想目標值與彈性容忍範圍53
表4-24 特例二三項目標之最佳可行解53
表4-25 特例二各產品各期最佳生產狀況54
表4-26 三個情境之最佳可行解比較55
表4-27 三個情境之產品生產組合比較55
表4-28 高爐產能之敏感度分析57
表4-29 電爐產能之敏感度分析60
表4-30 軋鋼產能之敏感度分析63
表4-31 高爐生產成本之敏感度分析66
表4-32 電爐生產成本之敏感度分析69
表4-33 軋鋼生產成本之敏感度分析72
表4-34 碳稅之敏感度分析76
表4-35 能源稅之敏感度分析79
表4-36 碳權價格之敏感度分析82
表4-37 目標權重變動分析85

圖目錄
圖1-1 研究流程圖4
圖2-1 碳權交易示意圖9
圖2-2 長、中、短期生產規劃11
圖3-1 模型建構流程圖21
圖3-2 本章情境關聯圖22
圖3-3 求解模糊極大化總利潤之策略29
圖3-4 市場需求之三角可能性分配32
圖4-1 高爐產能之敏感度分析圖1 58
圖4-2 高爐產能之敏感度分析圖2 59
圖4-3 電爐產能之敏感度分析圖1 61
圖4-4 電爐產能之敏感度分析圖2 62
圖4-5 軋鋼產能之敏感度分析圖1 64
圖4-6 軋鋼產能之敏感度分析圖2 65
圖4-7 高爐生產成本之敏感度分析圖1 67
圖4-8 高爐生產成本之敏感度分析圖2 68
圖4-9 電爐生產成本之敏感度分析圖1 70
圖4-10 電爐生產成本之敏感度分析圖2 71
圖4-11 軋鋼生產成本之敏感度分析圖1 73
圖4-12 軋鋼生產成本之敏感度分析圖2 74
圖4-13 碳稅之敏感度分析圖1 77
圖4-14 碳稅之敏感度分析圖2 78
圖4-15 能源稅之敏感度分析圖1 80
圖4-16 能源稅之敏感度分析圖2 81
圖4-17 碳權價格之敏感度分析圖1 83
圖4-18 碳權價格之敏感度分析圖2 84
圖4-19 目標權重變動分析圖1 86
圖4-20 目標權重變動分析圖2 87
參考文獻 國際能源署International Energy Agency http://www.iea.org

氣候交易所European Climate Exchange http://www.ecx.eu

政府間氣候變化專家委員會IPCC http://www.ipcc.ch

傅和彥(2008),生產與作業管理:建立產品與服務標竿,台北市:前程文化。

陳怡璇(2009),考量環境成本之總體生產規劃,國立成功大學,台南市。

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141-164.

Carraro, C., & Favero, A. (2009). The economic and financial determinants of carbon prices. Finance a úvěr-Czech Journal of Economics and Finance, 59(5), 396-409.

Checallier, J. (2011). Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model. Economic Modelling, 28(1-2), 557-567.

Chen, H. K., & Chou, H. W. (1996). Solving multiobjective linear programming problems- a generic approach. Fuzzy Sets and Systems, 82(1), 35-38.

Coase, R. H. (1960). The Problem of Social Cost. The Journal of Law and Economics, 3(1), 1-44.

Curkovic, S. (2003). Environmentally responsible manufacturing: the development and validation of a measurement model. European Journal of Operational Research, 146(1), 130-155.

Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations research for green logistics- an overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671-679.

Gen, M., Tsujimura, Y., & Ida, K. (1992). Method for solving multiobjective aggregate production planning problem with fuzzy parameters. Computers & Industrial Engineering, 23(1-4), 117-120.

Goodman, D. (1974). Goal programming approach to aggregate planning of production and work force. Management Science, 20(12), 1569-1575.

Hannan, E. L. (1981). Linear programming with multiple fuzzy goals. Fuzzy Sets and Systems, 6(3), 235-248.

Jaehn, F., & Letmathe, P. (2010). The emissions trading paradox. European Journal of Operational Research, 202(1), 248-254.

Lai, Y. J., & Hwang, C. L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121-133.

Leberling, H. (1981). On finding compromise solutions in multicriteria problems using the fuzzy min-operator. Fuzzy Sets and Systems, 6(2), 105-118.

Lee, C. F., Lin, S. J., Lewis, C., & Chang, Y. F. (2007). Effects of carbon taxes on different industries by fuzzy goal programming: A case study of the petrochemical-related industries, Taiwan. Energy Policy, 35(8), 4051-4058.

Lee, E. S., & Li, R. J. (1993). Fuzzy multiple objective programming and compromise programming with Pareto optimum. Fuzzy Sets and Systems, 53(2), 275-288.

Lee, S. M. (1973). Goal Programming for Decision Analysis. USA: Averbach Publisher.

Letmathe, P., & 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 objective. Production Planning & Control, 18(7), 548-560.

Liang, T. F., & Cheng, H. W. (2011). Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial and Management Optimization, 7(2), 365-383.

Liang, T. F., Cheng, H. W., Chen, P. Y., & Shen, K. H. (2011). Application of fuzzy sets to aggregate production planning with multiproducts and multitime periods. IEEE Tractions on Fuzzy Systems, 19(3), 465-477.

Luhandjula, M. K. (1982). Compensatory operators in fuzzy linear programming with multiple objectives. Fuzzy Sets and Systems, 8(3), 245-252.

Mahdavi, I., Taghizadeh, K., Bagherpour, M., & Solimanpur, M. (2012). Modelling of multi-period multi-product production planning considering production routes. International Journal of Production Research, 50(6), 1749-1766.

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.

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.

Radulescu, M., Radulescu, S., & Radulescu, C. (2009). Sustainable production technologies which take into account environmental constraints. European Journal of Operational Research, 193(3), 730-740.

Sakawa, M. (1988). An interactive fuzzy satisficing method for multiobjective linear fractional programming problems. Fuzzy Sets and Systems, 28(2), 129-144.

Tsai, W. H., Lin, W. R., Fan, Y. W., Lee, P. L., Lin, S. J., & Hsu, J. L. (2012). Applying a mathematical programming approach for a green product mix decision. International Journal of Production Research, 50(4), 1171-1184.

Ulhøi, J. P. (1995). Corporate environmental and resource management: In search of a new managerial paradigm. European Journal of Operational Research, 80(1), 2-15.

Verderame, P. M., Elia, J. A., Li, J., & Floudas, C. A. (2010). Planning and scheduling under uncertainty: a review across multiple sectors. Industrial & Engineering Chemistry Research, 49(9), 3993-4017.

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.

Wang, R. C., & Liang, T. F. (2004). Application of fuzzy multi-objective linear programming to aggregate production planning. Computers & Industrial Engineering, 46(1), 17-41.

Wu, C. C., & Chang, N. B. (2004). Corporate optimal production planning with varying environmental costs: A grey compromise programming approach. European Journal of Operational Research, 155(1), 68-95.

Wu, C. C., & Chang, N. B. (2008). Evaluation of environmentally benign production program in the texile-dyeing industry(II): a multi-objective programming approach. Civil Engineering and Environmental Systems, 25(1), 1-28.

Wu, Y. K., & Guu, S. M. (2001). A compromise model for solving fuzzy multiple objective linear programming problems. Journal of the Chinese Institute of Industrial Engineers, 18(5), 87-93.

Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3-28.

Zhou, M., Pan, Y., Chen, Z., Yang, W., & Li, B. (2012). Selection and evaluation of green production strategies: analytic and simulation models. Journal of Cleaner Production, 26, 9-17.

Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2023-12-31起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw