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題名：模糊環境下總體生產計劃之隸屬函數解法
作者：黃文隆
作者(外文)：Huang, Wen-Lung
校院名稱：國立中正大學
系所名稱：企業管理研究所
指導教授：陳世彬
學位類別：博士
出版日期：2015
主題關鍵詞：總體生產規劃；模糊理論；線性規劃；隸屬函數；參數規劃；aggregate production planning；fuzzy sets；linear programming；membership function；parametric programming
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總體生產計劃 (Aggregate Production Planning, APP) 在作業管理扮演重要的角色，引起許多關注於探討模糊環境中的總體生產計劃問題。然而，之前的研究僅提供明確解，容易造成不是低估或者是高估的現象，導致使用者決策錯誤。本研究在探討多期生產下，不同糢糊不確定性型態的總體生產問題。相較於現有其他研究，由於本研究所提出的模式及求解程序可以保有模糊特性，因此所獲得的總體生產計劃問題解更具有效性。本研究首先考量單產品、單目標總體生產計劃模式，主要概念是基於延展原則(extension principle)和α截集(α-cuts)的應用基礎上，將模糊總體生產計劃模式轉換為明確的總體生產計劃模式。再基於延展原則，建構出模糊最小總成本的隸屬函數，並求出模糊解。透過可能性水準建構出一對二階(two-level)參數規劃，再藉由不同的可能性水準α值計算出模糊總體生產計劃總成本的上界和下界，隨之建構模糊總體生產計劃總成本的隸屬函數(membership function)，並舉例以驗證說明本研究所提出方法的有效性。再者，本研究所提出方法在成功地延伸應用在不同特性的多產品總體生產計劃問題後，進而結合「極大-極小」方法，處理不同型態模糊數的多目標總體生產計劃問題。
相較其他研究，本研究所提出的方法可對不明確或模糊參數求出更合理解，因此可提供決策者有關加班、存貨、缺貨、雇用、解雇人力替代策略更寬範圍的決策資訊以因應模糊環境的變動；再者，可協助製造業決策者降低無效率績效(例如缺貨、延遲)的生產影響與無法吻合正確需求產量的風險。因此，本研究結果可提供決策者更多有效益的總體生產計劃與更可能達到最佳分項計劃 (disaggregate plan)。
本研究所提出的方法亦可應用於其他不同結構的總體生產計劃模式，例如多站、不同目標權重、資源利用限制及供應鏈管理上的總體生產-配銷計劃(Aggregate Production-Distribution Planning, ADPD)。
關鍵字：總體生產計劃、模糊理論、線性規劃、隸屬函數、參數規劃
Aggregate production planning (APP) plays a critical role in operations man-agement (OM) and has attracted considerable attention to investigate the APP problem in fuzzy environments. However, previous studies provided only crisp solutions that are prone to either be under- or over-estimated, thus leading its users to make faulty decisions. This study investigates multi-period APP problems with several distinct types of fuzzy uncertainties. In contrast to the existing studies, the modeling and solution procedure proposed in this work conserves the fuzziness such that the obtained APP is more effective. First, the single-product APP with single-objective is considered. the main concept is based on the application ofα-cuts and Zadeh’s extension principle to transform the fuzzy APP model into a family of crisp APP models. The membership function of the fuzzy minimum total cost is constructed based on Zadeh’s extension principle and fuzzy solutions are provided. A pair of two-level mathematical programs parameterized by the possibility level is formulated to calculate the lower and upper bounds of the fuzzy total cost at each possible level of . By enumerating different values of , the membership function of the fuzzy total cost is constructed. To illustrate the validity of the proposed approach, the example studied by Lai and Hwang (1992) using Chanas’s approach is investigated. Next, the proposed approach is applied to APP problems with other characteristics. It is extended to the multi-product APP successfully. Moreover, combining Zimmermann’s max-min operation, the proposed approach is modified for dealing with the multi-objective APP problems with several fuzzy parameters of different types.
Compared with other studies, the proposed approach can obtain a more reasona-ble solution for imprecise/fuzzy parameters, and so more wide-range decision infor-mation on alternative strategies for overtime, inventory, backorder, and hiring and layoffs workers is provided for decision-makers in response to variations in fuzzy environments. Furthermore, the proposed approach helps decision-makers in manufacturing industry reduce the production impacts of performance inefficiency (e.g. backorder, delay) and risks that demand might not be met with the right products. Thus, the results can provide decision-makers with more effective and informative APPs and more chance to achieve the optimal disaggregate plan.
The proposed approach can also be applied to other APP models with different structures, such as multi-site, different goal priorities, resource utilization constraint, and aggregate production-distribution planning (ADPD) in supply chain management (SCM).
Keywords:aggregate production planning; fuzzy sets; linear programming; member-ship function; parametric programming
Abass, S.A., Gomaa, M.A., Elsharawy, G. A., & Elsaid, M. S. (2010). Generalized production planning problem under interval uncertainty. Egyptian Informatics Journal, 11 (1), 27-31.new window
Aghezzaf, E., Sitompul, C., & Najid, N. M. (2010). Models for robust tactical plan-ning in multi-stage production systems with uncertain demands. Computers and Operations Research, 37 (5), 880-889.
Al-e, S. M.J.M., Aryanezhad, M.B., & Sadjadi, S. J. (2012). An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. The International Journal of Advanced Manufacturing Technology, 58 (5-8), 765-782.
Aliev, R.A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177 (20), 4241-4255.
Baykasoglu, A. (2001). MOAPPS 1.0: Aggregate production planning using the mul-tiple-objective tabu search. International Journal of Production Research, 39 (16), 3685-3702.new window
Baykasoglu, A., & Göçken, T. (2006). A tabu search approach to fuzzy goal programs and an application to aggregate production planning. Engineering Optimization, 38, 155-77.
Baykasoglu, A., & Göcken, T. (2010). Multi-objective aggregate production planning with fuzzy parameters. Advances in Engineering Software, 41 (9), 1124-1131.
Bazaraa, M.S., Jarvis, J.J., & Sherali, H. D. (2005). Linear programming and network flows. 3rd ed. Hoboken, NJ: John Wiley & Sons.
Bazaraa, M.S., Sherali, H.D., & Shetty, C. M. (1993). Nonlinear programming: theory and algorithm. 2nd ed. Singapore: John Wiley & Sons.
Bellman, R.E., & Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management Science, 17 (4), 141-164.
Bialas, W.F., & Karwan, M.H. (1984). Two-level linear programming. Management Science, 30 (8), 1004-1020.
Bin Li, Hongwei Wang, Jianbo Yang, Min Guo & Chao Qi. (2013). A belief-rule-based inference method for aggregate production planning under uncertainty. In-ternational Journal of Production Research, 51 (1), 83-105.new window
Bowman, E.H. (1956). Production scheduling by the transportation method of linear programming. Operations Research, 4 (1), 100-103.new window
Bowman, E.H. (1963). Consistency and optimality in managerial decision making. Management Science, 9 (2), 310-321.
Buxey, G. (1995). A managerial perspective on aggregate planning. International Journal of Production Economics, 41 (1-3), 127-133.
Buxey, G. (2003). Strategy not tactics drives aggregate planning. International Jour-nal of Production Economics, 85 (3), 331-346.
Cadenas Figueredo, J.M., & Jiménez Barrionuevo, F. (1994). Interactive decision-making in multiobjective fuzzy programming. Mathware & soft computing. 1 (3), 219-230.new window
Carlos Gomes da Silva, José Figueira, João Lisboa & Samir Barman. (2006). An in-teractive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming. Omega, 34 (2), 167-177.
Chakrabortty, R., & Hasin, M. (2013a). Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. Interna-tional Journal of Industrial Engineering Computations, 4 (1), 1-12.new window
Chakrabortty, R.K., & Hasin, M.A.A. (2013b). Solving an aggregate production plan-ning problem by fuzzy based genetic algorithm (FBGA) approach. International Journal of Fuzzy Logic Systems, 3 (1), 1-16.new window
Chanas, S. (1983). The use of parametric programming in fuzzy linear programming. Fuzzy Sets and Systems, 11 (1-3), 229-241.
Chanas, S., & Kulej, M. (1984). A fuzzy linear programming problem with equality constraints. Control and Cybernetics, 13, 195-201.
Charnes, A., & Cooper, W.W. (1961). Management Models and Industrial Applica-tions of Linear Programming. New York: Wiley.
Chase, R.B., & Aquilano, N.J. (1992). Production & operations management: a life cycle approach. Boston, MA: Richard D. Irwin.
Chen, S.H., & Chang, S.M. (2008). Optimization of fuzzy production inventory mod-el with unrepairable defective products. International Journal of Production Eco-nomics, 113 (2), 887-894.
Chen, S.P., & Huang, W.L. (2010). A membership function approach for aggregate production planning problems in fuzzy environments. International Journal of Production Research, 48 (23), 7003-7023.
Chen, S.P., & Huang, W.L. (2014). Solving Fuzzy Multiproduct Aggregate Production Planning Problems Based on Extension Principle. International Journal of Mathematics and Mathematical Sciences.
Dai, L., Fun, L., & Sun, L. (2003). Aggregate production planning utilizing a fuzzy linear programming. Journal of Integrated Design & Process Science, 7 (4), 81-95.
Dantzig, G.B. (1955). Linear programming under uncertainty. Management Science, 1 (3-4), 197-206.new window
Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9 (6), 613-626.
Fung, R.Y.K., Tang, J., & Wang, D. (2003). Multiproduct aggregate production plan-ning with fuzzy demands and fuzzy capacities. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, 33 (3), 302-313.
Gen, M., Tsujimura, Y., & Ida, K. (1992). Method for solving multiobjective aggre-gate production planning problem with fuzzy parameters. Computers & industrial engineering, 23 (1), 117-120.new window
Graves, S.C. (2011). Uncertainty and Production Planning. In Kempf, K.G., Keski-nocak, P., Uzsoy, R. (Eds.), Planning Production and Inventories in the Extended Enterprise: a State of the Art Handbook (pp. 83-101). Springer.
Guu, S.M., & Wu, Y.K. (1997). Weighted coefficients in two-phase approach for solving the multiple objective programming problems. Fuzzy Sets and Systems, 85, 45-48.
Guu, S.M., & Wu, Y.K. (1999). Two-phase approach for solving the fuzzy linear pro-gramming problems. Fuzzy Sets and Systems, 107, 191-195.
Hillier, F.S., & Lieberman, G.J. (2001). Introduction to Operations Research, 7th ed. New York: McGraw-Hill..
Ho, C. (1989). Evaluating the impact of operating environments on MRP system nervousness. International Journal of Production Research, 27 (7), 1115–1135.
Holt, C.C., Modigliani, F., & Simmon, H.A. (1955). A linear decision rule for produc-tion and employment scheduling. Management Science, 2 (1), 1-30.new window
Jain, A., & Palekar, U.S. (2005). Aggregate production planning for a continuous re-configurable manufacturing process. Computers & Operations Research, 32 (5), 1213-1236.
Jamalnia, A., & Soukhakian, M.A. (2009). A hybrid fuzzy goal programming ap-proach with different goal priorities to aggregate production planning. Computers & Industrial Engineering, 56 (4), 1474-1486.
Jones, C. (1967). Parametric production planning. Management Science, 13 (11), 843-866.
Kaufmann, A. (1975). Introduction to the Theory of Fuzzy Subsets. Vol. 1. New York: Academic Press.new window
Klir, G., & Yuan, B. (2002). Fuzzy Sets Theory and Fuzzy Logic: Theory and Appli-cation. Taiwan: Pearson Education.
Krajewski, L.J., Ritzman, L.P., & Malhotra, M.K. (2007). Operation Management: Processes and Value Chains. New Jersey: Prentice Hall.
Lai, Y.J., & Hwang, C.L. (1992). Fuzzy Mathematical Programming: Methods and Applications. Berlin: Springer.
Lee, Y.Y. (1990). Fuzzy set theory approach to aggregate production planning and inventory control. Dissertation (PhD). Department of I.E., Kansas State University.
Leung, S.C.H., & Chan, S.S.W. (2009). A goal programming model for aggregate production planning with resource utilization constraint. Computers & Industrial Engineering, 56 (3), 1053-1064.
Leung, S.C.H., Wu, Y., & Lai, K.K. (2006). A stochastic programming approach for multi-site aggregate production planning. Journal of the Operational Research Society, 57 (2), 123-132.
Li, B., Wang, H., Yang, J., Guo, M., & Qi, C. (2013). A belief-rule-based inference method for aggregate production planning under uncertainty. International Jour-nal of Production Research, 51 (1), 83-105.new window
Liang, T.F. (2007). Application of possibilistic linear programming to multi-objective distribution planning decisions. Journal of the Chinese Institute of Industrial En-gineers, 24 (2), 97-109.
Liang, T.F. (2010). Applying fuzzy goal programming to project management deci-sions with multiple goals in uncertain environments. Expert Systems with Applications, 37, 8499-8507.
Liang, T.F. (2011). Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains. Information Sciences, 181, 842-854.
Liang, T.F., & Cheng, H.W. (2009). Application of fuzzy sets to manufactur-ing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36, 3367-3377
LINDO Systems. (1999). Lingo user’s guide. Chicago, IL: LINDO Systems.
Madadi, N., & Wong, K.Y. (2014). A Multiobjective Fuzzy Aggregate Production Planning Model Considering Real Capacity and Quality of Products. Mathemati-cal Problems in Engineering.
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), 741-752.
Mirzapour Al-e-hashem, S.M.J., Aryanezhad, M. B., & Sadjadi, S. J. (2012). An effi-cient algorithm to solve a multiobjective robust aggregate production planning in an uncertain environment. Int. J. adv. Manuf. Technol., 58, (5-8), 765-782.
Mirzapour Al-e-hashem, S.M.J., Baboli, A., & Sadjadi, S. J., Aryanezhad, M.B. (2011). A multi-objective stochastic production-distribution planning problem in an uncertain environment considering risk and workers productivity. Mathemati-cal problems in engineering.
Mirzapour Al-e-hashem, S.M.J., Baboli, A., Aryanezhad, M. B., & Sazvar, Z. (2011). Aggregate production planning in a green supply chain by considering flexible lead times and multi breakpoint purchase and shortage cost functions, 41st Inter-national Conference on Computers & Industrial Engineering. Los Angeles, CA USA.
Mirzapour Al-e-hashem, S.M.J., Malekly, H., & Aryanezhad, M.B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate pro-duction planning in a supply chain under uncertainty. International Journal Pro-duction Economics, 134 (1), 28-42.new window
Mohamed K. Omar, Muzalna Mohd Jusoh & Mohd Omar. (2013). Fmilp Formulation for Aggregate Production Planning, World Applied Sciences Journal 21 (Mathe-matical Applications in Engineering), 68-72.
Mortezaei, N., Zulkifli, N., Hong, T. S., & Yusuff, R. M. (2013). Multi-objective ag-gregate production planning model with fuzzy parameters and its solving methods. Life Science Journal, 10 (4).
Mula, J., Poler, R., Garcia-Sabater, J.P., & Lario, F.C. (2006). Models for production planning under uncertainty: a review. International Journal of Production Eco-nomics, 103 (1), 271-285.new window
Nam, S. J., & Logendran, R. (1992). Aggregate production planning–a survey of models and methodologies. European Journal of Operational Research, 61 (3), 255-272.
Nikolaos V. Sahinidis. (2004). Optimization under uncertainty state-of-the-art and opportunities. Computers & Chemical Engineering, 28 (6-7), 971-983.
Özdamar, L., Bozyel, M. A., & Birbil, S. I. (1998). A hierarchical decision support system for production planning (with case study). European Journal of Opera-tional Research, 104 (3), 403-422.
Özgen, D., Önüt, S., Gülsün, B., Tuzkaya, U. R., & Tuzkaya, G. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information Sciences, 178 (2), 485-500.
Pan L., & Kleiner B.H. (1995). Aggregate planning today. Work Study, 44 (3), 4-7.
Phruksaphanrat, B. (2011). Preemptive possibilistic linear programming: Application to aggregate production planning. World Academy of Science. Engineering and Technology, 80, 473-480.
Ramezanian, R., Rahmani, D., & Barzinpour, F. (2012). An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert Systems with Applications, 39 (1), 1256-1263.new window
Rinks, D.B. (1982). The performance of fuzzy algorithm models for aggregate plan-ning and under differing cost structures. In: M.M. Gupta & M.M. Sachez (Eds.), Fuzzy Information and Decision Processes. Amsterdam: North-Holland, 267-278.
Rommelfanger, H. (1996). Fuzzy linear programming and applications. European Journal of Operational Research, 92 (3), 512-527.
Saad, G. (1982). An overview of production planning model: structure classification and empirical assessment. International Journal of Production Research, 20 (1), 105-114.new window
Sakawa, M., Inuiguchi, M., & Sawada, K. (1996). A fuzzy satisficing method for large-scale multiobjective linear programming problems with block angular structure. Fuzzy sets and Systems, 78 (3), 279-288.
Sakawa, M., Nishizaki, I., & Uemura, Y. (2001). Interactive fuzzy programming for two-level linear and linear fractional production assignment problems: a case study. European Journal of Operational Research, 135 (1), 142-157.new window
Sarkar, S., & Pathak, S. (2011). Possibilistic Linear Programming Approach to the Multi-item Aggregate Production Planning. International Journal of Pure and Applied Sciences and Technology, 7 (2), 117-131.
Silva J. Lisboa J., & Huang Ph. (2000). A labor-constrained model for aggregate pro-duction planning. International Journal Production Research, 38 (9) 2143-2152.
Singhvi, A., & Shenoy, U.V. (2002). Aggregate planning in supply chains by pinch analysis. Chemical Engineering Research and Design, 80 (6), 597-605.
Stevenson, W.J., (2002). Operations Management. 7th ed., New York: McGraw-Hill.
Taha, H.A. (2011). Operations Research: An Introduction. 9th ed., New Jersey: Pren-tice-Hall.
Tang, J., Fung, R.Y.K., & Yung, K.L. (2003). Fuzzy modelling and simulation for ag-gregate production planning. International Journal of Systems Science, 34 (12), 661-673.
Tang, J., Wang, D., & Fang, R.Y.K. (2000). Fuzzy formulation for multi-product ag-gregate production planning. Production Planning and Control, 11 (7), 670-676.
Taubert, W.H. (1968). A search decision rule for the aggregate scheduling problem. Management Science, 14 (6), 343-359.
Tavakkoli-Moghaddam, R., Rabbani, M., Gharehgozli, A. H., & Zaerpour, N. (2007), December). A fuzzy aggregate production planning model for make-to-stock environments. In Industrial Engineering and Engineering Management, 2007 IEEE International Conference on (pp. 1609-1613). IEEE.
Torabi, S.A., Ebadian, M., & Tanha, R. (2010). Fuzzy hierarchical production plan-ning (with a case study). Fuzzy Sets and Systems, 161 (11), 1511-1529.
Veeramani, C., Duraisamy, C., & Nagoorgani, A. (2011). Solving Fuzzy Multi-Objective Linear Programming Problems With Linear Membership Functions. Journal of Applied Sciences Research, 7 (8).
Wang, D.W., & Fang, S.C. (1997). A genetics-based approach for aggregate produc-tion planning in a fuzzy environment. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 27 (5), 636-645.
Wang, H.F., & Zheng, K.W. (2013). Application of fuzzy linear programming to ag-gregate production plan of a refinery industry in Taiwan. Journal of the Opera-tional Research Society, 64 (2), 169-184.
Wang, Q., & Fang, S.C. (1997). A genetics-based approach for aggregated production planning in a fuzzy environment. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 27 (5), 636-645.
Wang, R.C., & Fang, H.H. (2000). Aggregate production planning in a fuzzy envi-ronment. International Journal of Industrial Engineering, 7 (1), 5-14.new window
Wang, R.C., & Fang, H.H. (2001). Aggregate production planning with multiple ob-jectives in a fuzzy environment. European Journal of Operational Research, 133 (3), 521-536.
Wang, R.C., & Liang, T.F. (2004). Application of multi-objective linear programming to aggregate production planning. Computers and Industrial Engineering, 46 (1), 17-41.new window
Wang, R.C., & Liang, T.F. (2005). Applying possibilistic linear programming to ag-gregate production planning. International Journal of Production Economics, 98 (3), 328-341.
Wang, R.C., & Liang, T.F. (2005). Aggregate production planning with multiple fuzzy goals. The International Journal of Advanced Manufacturing Technology, 25 (5-6), 589-597.
Xue, G., Offodile, O.F., Zhou, H., & Troutt, M. D. (2011). Integrated production plan-ning with sequence-dependent family setup times. International Journal of Pro-duction Economics, 131 (2), 674-681.
Yager, R.R. (1986). A characterization of the extension principle. Fuzzy Sets and Sys-tems, 18 (3), 205-217.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8 (3), 338-353.
Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1 (1), 3-28.new window
Zimmermann, H.J. (1976). Description and optimization of fuzzy systems. Interna-tional Journal of General Systems, 2 (1), 209-216.new window
Zimmermann, H.J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1, 45-56.new window
Zimmermann, H.J. (1996). Fuzzy Set Theory and Its Application. Boston: Kluwer.
Zimmermann, H.J. (1997). Fuzzy Linear Programming. In T. Gal, & H. J. Greenberg (Eds.), Advances in sensitivity analysis and parametric programming (pp. 15.1-15.40). Boston: Kluwer.
Zimmermann, H.J. (2001). Fuzzy Sets Theory and Its Applications. 4th ed., Boston, MA: Kluwer-Nijhoff.

 
 
 
 
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