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研究生:陳信宏
研究生(外文):Hsin-HungChen
論文名稱:求解多階層最佳化問題之模糊目標規劃法
論文名稱(外文):Fuzzy goal programming approaches for solving hierarchical decision-making problems
指導教授:陳梁軒陳梁軒引用關係
指導教授(外文):Liang-Hsuan Chen
學位類別:博士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:61
中文關鍵詞:多階層線性規劃模糊目標規劃模糊語意變數
外文關鍵詞:Decentralized bi-level linear programmingMulti-level linear programmingFuzzy goalFuzzy variableLinguistic term
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本研究探討模糊環境下之多階層決策問題,此類問題亦常被以多階層數學規劃等最佳化工具來加以解決。在此類最佳化問題中,決策者因所屬階層高低而互為領導者與追隨者,且對於自身目標(goal)通常存在某種程度之不確定性,此外,依單一決策者之追隨者多寡,此類問題亦可區分為多階層及分權式(decentralized)多階層兩種決策結構。考量原始問題定義,下層決策者在自身目標的追求上將受限於上層決策者的目標最佳化,以及實務上,上層決策者對於解決方案通常具有調整之權力,本研究以模糊目標規劃(fuzzy goal programming)為基礎建構最佳化求解模式,並發展合適的決策方法分別解決分權式二階層多目標線性規劃問題,及多階層線性規劃問題。相較於以往文獻方法,本研究得以確保上層決策者相較於其下層能獲得較高的整體(平均)目標滿足程度,此外,採用模糊語意表達相鄰兩階層之目標相對滿足水準,並視為一模糊目標,作為上層決策者欲進一步提升自身或其下層決策者目標滿足程度之依據,除了能夠有效達到調整之目的,亦可反映上層決策者在調整解決方案時的不確定性。為了盡可能提升所建模式之求解效率,本研究亦針對以分數型態表達之限制式進行線性化。最後,透過數值例的測試與比較,驗證本研究所提模糊目標規劃方法之可行性及有效性。
This dissertation studies hierarchical optimization problems in a fuzzy environment. For two main decision structures of (decentralized) multi-level systems, we investigate decentralized bi-level multi-objective linear programming (DBL-MOLP) and multi-level linear programming (MLLP) problems with imprecise goals, and develop proper solution approaches based on fuzzy goal programming (FGP). For DBL-MOLP problems with multiple followers at the second level, decision decentralization is considered, and allowed to be adjusted depending upon the higher-level decision-maker (DM) to determine the linguistic levels of relative satisfaction of lower-level DMs compared to his own. For MLLP problems with more than one DM who can affect the feasible strategies of their subordinate-level DMs, we develop a two-phase FGP approach. In the first phase, a candidate solution that optimally satisfies the fuzzy goals of the DMs with the leader-follower requirement is suggested. In the second phase, a higher-level DM can adjust his satisfaction based on the level of relative satisfaction compared to that of his subordinate DM. Importantly, the proposed approaches ensure the leader-follower relationship in the solution processes by constraining the integrated (average) satisfaction of a DM to be not worse than that of his lower-level DMs based on the equal scales of fuzzy goals. The judgment in the adjustment processes, represented using linguistic terms, reflects the uncertainty in the problem. For greater efficiency, linearization is made for fractionally expressed constraints in the models. Through numerical examples, the feasibility and effectiveness of the proposed approaches are demonstrated.
摘 要 I
Abstract II
Acknowledgements III
Table of Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Objective 2
1.3 Organization 3
Chapter 2 Literature review 6
2.1 MLLP problems 6
2.2 DBL-MOLP problems 8
2.3 Solution methods 9
2.3.1 Traditional approaches 10
2.3.2 Fuzzy approaches 11
2.4 Summary 13
Chapter 3 FGP approach for DBL-MOLP problems 14
3.1 Membership functions of fuzzy goals and decision variables 14
3.2 Fuzzy variable decision decentralization 16
3.3 Fuzzy goal programming model 18
3.4 Solution procedure 21
3.5 DBL-MOLP problem with various levels of importance 22
3.6 Numerical example 24
3.6.1 Solution process without weights 25
3.6.2 Solution process with weights 27
3.6.3 Comparisons with existing approaches 28
3.7 Summary 30
Chapter 4 Two-phase FGP approach for MLLP problems 31
4.1 Phase I 31
4.2 Phase II 34
4.3 Solution procedure 40
4.4 Numerical example 42
4.4.1 Demonstration 42
4.4.2 Comparisons with existing approaches 45
4.5 Summary 48
Chapter 5 Conclusions 49
Appendix A 51
Appendix B 54
References 57

Ahlatcioglu, M. & Tiryaki, F. (2007), “Interactive fuzzy programming for decentralized two-level linear fractional programming (DTLLFP) problems, Omega, 35(4), pp. 432–450.
Anandalingam, G. (1998), “A mathematical-programming model of decentralized multi-level systems, Journal of the Operational Research Society, 39(11), pp. 1021–1033.
Anandalingam, G. & Apprey, V. (1991), “Multi-level programming and conflicting resolution, European Journal of Operational Research, 51, pp. 233–247.
Arora, S. R. & Gupta, R. (2009), “Interactive fuzzy goal programming approach for bilevel programming problem, European Journal of Operational Research, 194(2), pp. 368–376.
Baky, I. A. (2009), “Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems, Fuzzy Sets and Systems, 160(18), pp. 2701–2713.
Bard, J. F. & Falk, J. E. (1982), “An explicit solution to the multi-level programming problem, Computers & Operations Research, 9, pp.77–100.
Bellman, R. E. & Zadeh, L. A. (1970), “Decision-making in a fuzzy environment, Management Science, 17, pp. 141–164.
Benayed, O. & Blair, C. E. (1990), “Computational difficulties of believe linear programming, Operations Research, 38(3), pp. 556–560.
Bialas, W. F. & Karwan, M. H. (1982), “On two-level optimization, IEEE Transactions on Automatic Control, 27, pp. 211–214.
Bialas, W. F. & Karwan, M. H. (1984), “Two-level linear programming, Management Science, 30, pp. 1004–1020.
Burton, R. M. & Obel, B. (1977), “The multilevel approach to organizational issues of the firm – a critical review, Omega, 5, pp. 395–414.
Candler, W., Fortuny-Amat, J., & McCarl, B. (1981), “The Potential Role of Multilevel Programming in Agricultural Economics, American Journal of Agricultural Economics, 63, pp. 521–531.
Charnes, A. & Cooper, W. W. (1961), Management Models and Industrial Applications of linear programming, Wiley, New York.
Charnes, A. & Cooper, W. W. (1962), “Programming with Linear Fractional Functionals, Naval Research Logistics Quarterly, 9, pp. 181–196.
Chen, L. H. & Tsai, F. C. (2001), “Fuzzy goal programming with different importance and priorities, European Journal of Operational Research, 133(3), pp. 548–556.
Chen, L. H. & Chen, H. H. (2013), “Considering decision decentralizations to solve bi-level multi-objective decision-making problems: a fuzzy approach, Applied Mathematical Modelling, 37, pp. 6884–6898.
Chen, L. H. & Chen, H. H. (2015), “A fuzzy approach with required minimum decision tolerances for multi-level multi-objective decision-making problems, Journal of Intelligent & Fuzzy Systems, 28, pp. 217–224.
Chen, L. H. & Chen, H. H. (2015), “A two-phase fuzzy approach for solving multi-level decision-making problems, Knowledge-Based Systems, 76, pp. 189–199.
Deshpande, P., Shukla, D. & Tiwari, M. K. (2011), “Fuzzy goal programming for inventory management: A bacterial foraging approach, European Journal of Operational Research, 212(2), pp. 325–336.
Hannan, E. L. (1981), “On fuzzy goal programming, Decision Science, 12, pp. 522–531.
Jeroslow, R. G. (1985), “The polynomial hierarchy and a simple model for competitive analysis, Mathematical Programming, 32, pp. 146–164.
Kuo, R. J. & Han, Y. S. (2011), “A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model, Applied Mathematical Modelling, 35, pp. 3905–3917.
Lai, Y. J. (1996), “Hierarchical optimization: A satisfactory solution, Fuzzy Sets and Systems, 77(3), pp. 321–335.
Lee, E. S. & Shih, H. S. (2001), Fuzzy and multi-level decision making: an interactive computational approach, Springer-Verlag, London.
Lotfi, M. M. & Torabi, S. A. (2011), “A fuzzy goal programming approach for mid-term assortment planning in supermarkets, European Journal of Operational Research, 213(2), pp. 430–441.
Mohamed, R. H. (1997), “The relationship between goal programming and fuzzy programming, Fuzzy Sets and Systems, 89(2), pp. 215–222.
Narasimhan, R. (1980), “Goal programming in a fuzzy environment, Decision Sciences, 11, pp. 325–336.
Pal, B. B., Moitra, B. N., & Maulik, U. (2003), “A goal programming procedure for fuzzy multiobjective linear fractional programming problem, Fuzzy Sets and Systems, 139(2), pp. 395–405.
Pramanik, S. & Roy, T. K. (2007), “Fuzzy goal programming approach to multi-level programming problems, European Journal of Operational Research, 176, pp. 1151–1166.
Rubin, P. A. & Narasimhan, R. (1984), “Fuzzy goal programming with nested priorities, Fuzzy Sets and Systems, 14, pp. 115–129.
Sakawa, M. & Nishizaki, I. (2001), “Interactive fuzzy programming for two-level linear fractional programming problems, Fuzzy Sets and Systems, 119(1), pp. 31–40.
Sakawa, M. & Nishizaki, I. (2002), “Interactive fuzzy programming for decentralized two-level linear programming problems, Fuzzy Sets and Systems, 125(3), pp. 301–315.
Sakawa, M., Nishizaki, I., & Hitaka, M. (1990), “Interactive fuzzy programming for multi-level 0-1 programming problems through genetic algorithms, European Journal of Operational Research, 114(3), pp. 580–588.
Sakawa, M., Nishizaki, I., & Uemura, Y. (1998), “Interactive fuzzy programming for multi-level linear programming problems, Computational Mathematics and Applications, 36, pp. 71–86.
Sakawa, M., Nishizaki, I., & Uemura, Y. (2000), “Interactive fuzzy programming for two-level linear fractional programming problems with fuzzy parameters, Fuzzy Sets and Systems, 115(1), pp. 93–103.
Sakawa, M., Nishizaki, I., & Uemura, Y. (2002), “A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming approach, European Journal of Operational Research, 141(1), pp. 167–185.
Shih, H. S. (2002), “An interactive approach for integrated multilevel systems in a fuzzy environment, Mathematical and Computer Modelling, 36, pp. 569–585.
Shih, H. S. & Lee, E. S. (2000), “Compensatory fuzzy multiple level decision making, Fuzzy Sets and Systems, 114, pp. 71–87.
Shih, H. S., Lai, Y. J., & Lee, E. S. (1996), “Fuzzy approach for multi-level programming problems, Computers & Operations Research, 23(1) pp. 73–91.
Shih, H. S., Wen, U. P., Lee, E. S., & Hsiao, H. C. (2004), “A neural network approach to multiobjective and multilevel programming problems, Computers and Mathematics with Applications, 48, pp. 95–108.
Sinha, S. (2003), “Fuzzy mathematical programming applied to multi-level programming problems, Computers & Operations Research, 30, pp. 1259–1268.
Sinha, S. (2003), “Fuzzy programming approach to multi-level programming problems, Fuzzy Sets and Systems, 136, pp. 189–202.
Tanaka, H., Okuda, T., & Asai, K. (1974), “On fuzzy mathematical programming, Journal of Cybernetics, 3, pp. 37–46.
Tiwari, R. N., Mohanty, B. K., & Rao, J. R. (1987), “Fuzzy goal programming — an additional model, Fuzzy Sets and Systems, 24, pp. 27–34.
Tookanlou, M. B., Ardehali, M. M., & Nazari, M. E. (2015), “Combined cooling, heating, and power system optimal pricing for electricity and natural gas using particle swarm optimization based on bi-level programming approach: Case study of Canadian energy sector, Journal of Natural Gas Science and Engineering, 23, pp. 417–430.
Wen, U. P. & Hsu, S. T. (1991), “Linear bi-level programming problems – a review, Journal of the Operational Research Society, 42, pp. 125–133.
White, D. J. & Anandalingam, G. (1993), “A penalty function approach for solving bi-level linear programs, Journal of Global Optimization, 3, pp. 397–419.
Zadeh, L. A. (1965), “Fuzzy sets, Information and Control, 8, pp. 338–353.
Zhang, G., Lu, J., & Gao, Y. (2015), Multi-Level Decision Making - Models, Methods and Applications, Springer.
Zimmermann, H. J. (1978), “Fuzzy programming and linear programming with several objective functions, Fuzzy Sets and Systems, 1, pp. 45–55.

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