This paper uses genetic algorithm to solve the series redundancy optimization problem which is in fuzzy framework. An nonlinear chance constrained programming with fuzzy coefficients occurring in objective is first pr...
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This paper uses genetic algorithm to solve the series redundancy optimization problem which is in fuzzy framework. An nonlinear chance constrained programming with fuzzy coefficients occurring in objective is first presented. fuzzy simulation based genetic algorithm is then employed to solve this kind of nonlinear chance constrained programming. Finally, a numerical example is also given.
The purpose of this research is to develop a methodology for modeling parallel machine scheduling problems with fuzzy processing times. Three novel types of fuzzy scheduling models are presented. A hybrid intelligent ...
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The purpose of this research is to develop a methodology for modeling parallel machine scheduling problems with fuzzy processing times. Three novel types of fuzzy scheduling models are presented. A hybrid intelligent algorithm is also designed for solving these models. Finally, some numerical examples are provided to demonstrate the computational efficiency of the proposed algorithm. (C) 2003 Elsevier Inc. All rights reserved.
In many real decision situations more than one objective has to be considered and different kinds of uncertainty must be handled. The uncertainty is generally of two natures: stochastic uncertainty related to environm...
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In many real decision situations more than one objective has to be considered and different kinds of uncertainty must be handled. The uncertainty is generally of two natures: stochastic uncertainty related to environmental data and fuzzy uncertainty related to expert judgement. This paper proposes a fuzzy chance constrained approach to solve mathematical programs integrating fuzzy and stochastic parameters with multiple objective aspects. Our approach is applied to determine reservoirs releases in the Echkeul basin in Tunisia.
In this paper we study the problem of priority elicitation in the analytic hierarchy process and propose a new approach to deriving crisp priorities from interval pairwise comparison judgements. By introducing linear ...
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In this paper we study the problem of priority elicitation in the analytic hierarchy process and propose a new approach to deriving crisp priorities from interval pairwise comparison judgements. By introducing linear or non-linear membership functions, representing the decision-maker's degree of satisfaction with various crisp priority vectors, the interval judgements are transformed into fuzzy inequality constraints. The interval prioritisation problem is then formulated as a fuzzy mathematical programming problem for obtaining an optimal crisp priority vector that maximises the overall degree of satisfaction. The proposed approach yields linear or non-linear mathematical programs, capable of deriving priorities from consistent and inconsistent interval judgements. The presence of a consistency index that measures the level of inconsistency of interval judgements is an attractive feature of our approach. Another feature, which does not exist in the known prioritisation methods, is the opportunity for additional prioritisation of the initial judgements. Numerical examples are given and comparisons with other interval prioritisation methods are carried out. (C) 2003 Elsevier B.V. All rights reserved.
In this paper, we deal with a real problem on production and transportation in a housing material manufacturer, and consider a production and transportation planning under the assumption that the manufacturer makes mu...
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In this paper, we deal with a real problem on production and transportation in a housing material manufacturer, and consider a production and transportation planning under the assumption that the manufacturer makes multiple products at factories in multiple regions and the products are in demand in each of the regions. First, we formulate mixed zero-one programming problems such that the cost of production and transportation is minimized subject to capacities of factories and demands of regions. Second, to realize stable production and satisfactory supply of the products in fuzzy environments, fuzzy programming for the production and transportation problem is incorporated. Finally, under the optimal planning of production and transportation, we show a profit and cost allocation by applying a solution concept from game theory. Using actual data, we show usefulness of the fuzzy programming and a rational allocation scheme of the profit and cost. (C) 2001 Elsevier Science B.V. All rights reserved.
This paper provides a spectrum of chance-constrained programming as well as chance-constrained multiobjective programming and chance-constrained goal programming with fuzzy rather than crisp decisions, which will seek...
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This paper provides a spectrum of chance-constrained programming as well as chance-constrained multiobjective programming and chance-constrained goal programming with fuzzy rather than crisp decisions, which will seek a fuzzy set from the given reference collection as an optimal solution. The technique of fuzzy simulation is also presented to check fuzzy chance constraints and to handle fuzzy objective and goal constraints. Finally, a fuzzy simulation-based genetic algorithm for solving these models will be designed and illustrated by some numerical examples. (C) 2001 Elsevier Science B.V. All rights reserved.
A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, gov...
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A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to very large-scale optimization models. Decision-making under uncertainty is often further complicated by the presence of integer decision variables to model logical and other discrete decisions in a multi-period or multi-stage setting. This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty. We discuss and contrast the classical recourse-based stochastic programming, robust stochastic programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastic dynamic programming. The advantages and shortcomings of these models are reviewed and illustrated through examples. Applications and the state-of-the-art in computations are also reviewed. Finally, we discuss several main areas for future development in this field. These include development of polynomial-time approximation schemes for multi-stage stochastic programs and the application of global optimization algorithms to two-stage and chance-constraint formulations. (C) 2003 Elsevier Ltd. All rights reserved.
A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, gov...
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A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to very large-scale optimization models. Decision-making under uncertainty is often further complicated by the presence of integer decision variables to model logical and other discrete decisions in a multi-period or multi-stage setting. This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty. We discuss and contrast the classical recourse-based stochastic programming, robust stochastic programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastic dynamic programming. The advantages and shortcomings of these models are reviewed and illustrated through examples. Applications and the state-of-the-art in computations are also reviewed. Finally, we discuss several main areas for future development in this field. These include development of polynomial-time approximation schemes for multi-stage stochastic programs and the application of global optimization algorithms to two-stage and chance-constraint formulations. (C) 2003 Elsevier Ltd. All rights reserved.
This paper presents interactive fuzzy programming for two-level linear fractional programming problems with the essentially cooperative decision makers. Tn our interactive method, after determining the fuzzy goals of ...
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This paper presents interactive fuzzy programming for two-level linear fractional programming problems with the essentially cooperative decision makers. Tn our interactive method, after determining the fuzzy goals of the decision makers at both levels, a satisfactory solution is efficiently derived by updating the satisfactory level of the decision maker at the upper level with considerations of overall satisfactory balance between both levels. In an interactive process, optimal solutions to the formulated programming problems are obtained by combined use of the bisection method and the phase one of linear programming and the variable transformation by Charnes and Cooper. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method. (C) 2001 Elsevier Science B.V. All rights reserved.
Transportation-location problems are generalizations of the classical transportation problem in which, in addition to seeking the quantities to be transported from supply points to demand points by various vehicles, i...
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Transportation-location problems are generalizations of the classical transportation problem in which, in addition to seeking the quantities to be transported from supply points to demand points by various vehicles, it is also necessary to find, at the same time, the "optimal" locations of one or several supply points in Euclidean space with respect to a fixed and known set of demand points, where these new supply points and vehicles are supposed to have certain limitations on their capacity to supply the product. Since the various decision makers involved in the process will have different opinions of the importance of the existing facilities, a multicriteria problem with several objectives arises. In this paper, we use fuzzy techniques to find an optimal compromise solution. We prove that the final compromise solution is weakly Pareto optimal and Pareto optimal, if it is unique.
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