An Interval-Parameter Fuzzy Approach for Multiobjective Linear Programming Under Uncertainty
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An interval-parameter fuzzy linear programming method (IFMOLP) is proposed in this study for multiple objective decision-making under uncertainty. As a hybrid of interval-parameter and fuzzy methodologies, the IFMOLP incorporates interval-parameter linear programming and fuzzy multiobjective programming approaches to form an integrated optimization system. The method inherits advantages of interval-parameter programming, and allows uncertainties and decision-makers’ aspirations to be effectively communicated into its programming processes and resulting solutions. Membership functions for both objectives and constraints are formulated to reflect uncertainties in different system components and their interrelationships. An interactive solution procedure has been developed based on solution approaches of the interval-parameter and fuzzy programming techniques, plus necessary measures for handling the multiobjective feature. A didactic example is provided in the paper to illustrate the detailed solution process. Possibilities of further improvements by seeking Pareto optimum and incorporating flexible preference within constraints are also discussed.
Key wordsfuzzy set interval parameter linear programming multiobjective uncertainty
Mathematics Subject Classifications (2000)90C05 90C29
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- 3.Goicoechea, A., Hansen, D.R., Duckstein L.: Multiobjective Decision Analysis with Engineering and Business Applications. Wiley, New York (1982)Google Scholar
- 4.Huang, G.H.: IPWM: An interval parameter water management model. Eng. Optim. 26, 79–103 (1996)Google Scholar
- 5.Huang, G.H., Baetz, B.W., Patry, G.G.: A grey linear programming approach for municipal solid waste management planning under uncertainty. Civ. Eng. Syst. 9, 319–335 (1992)Google Scholar
- 6.Huang, G.H., Baetz, B.W., Patry, G.G.: Waste flow allocation under independent stipulation uncertainties. Civ. Eng. Syst. 11, 209–243 (1994)Google Scholar
- 16.Urli, B., Nadeau, R.: Multiobjective stochastic linear programming with incomplete information: A general methodology. In: Slowinski, R., Teghem, J. (eds.) Stochastic vs. Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty, pp. 131–161. Kluwer, Dordrecht (1990)Google Scholar
- 17.Yeomans, J.S., Huang, G.H.: An evolutionary grey, hop, skip, and jump approach: Generating alternative policies for the expansion of waste management facilities. J. Environ. Inform. 1, 37–51 (2003)Google Scholar