The Experts below are selected from a list of 29409 Experts worldwide ranked by ideXlab platform

Masatoshi Sakawa - One of the best experts on this subject based on the ideXlab platform.

  • Stackelberg solutions for fuzzy random bilevel Linear Programming through level sets and probability maximization
    Operational Research, 2012
    Co-Authors: Masatoshi Sakawa, Hideki Katagiri, Takeshi Matsui
    Abstract:

    This paper considers Stackelberg solutions for bilevel Linear Programming Problems under fuzzy random environments. To deal with the formulated fuzzy random bilevel Linear Programming Problem, α-level sets of fuzzy random variables are introduced and an α-stochastic bilevel Linear Programming Problem is defined for guaranteeing the degree of realization of the Problem. Taking into account vagueness of judgments of decision makers, fuzzy goals are introduced and the α-stochastic bilevel Linear Programming Problem is transformed into the Problem to maximize the satisfaction degree for each fuzzy goal. Through probability maximization in stochastic Programming, the transformed stochastic bilevel Programming Problem can be reduced to a deterministic bilevel Programming Problem. An extended concept of Stackelberg solution is introduced and a computational method is also presented. A numerical example is provided to illustrate the proposed method.

  • robust optimization under softness in a fuzzy Linear Programming Problem
    International Journal of Approximate Reasoning, 1998
    Co-Authors: Masahiro Inuiguchi, Masatoshi Sakawa
    Abstract:

    Abstract In this paper, we discuss the softness and the robustness of the optimality in the setting of Linear Programming Problems with a fuzzy objective function. A fuzzy goal defined on the deviation from the optimal value is introduced in order to define the soft-optimal solution. Fuzzy coefficients are regarded as possibility distributions. A necessity measure based on the possibility distribution is used for defining a necessarily optimal solution, i.e., a robust-optimal solution. Since a necessarily optimal solution does not exist in many cases, a necessarily soft-optimal solution is defined. A solution algorithm for the best necessarily soft-optimal solution is proposed.

Harish Garg - One of the best experts on this subject based on the ideXlab platform.

  • multi objective non Linear Programming Problem in intuitionistic fuzzy environment
    Expert Systems With Applications, 2016
    Co-Authors: Deepika Rani, T R Gulati, Harish Garg
    Abstract:

    Multi-objective optimization Problem under intuitionistic fuzzy environment has been considered.Degree of attainability and non-attainability is represented by non-Linear functions.Optimistic and pessimistic aspect of the Problem has been considered.The validity of the Problem has been discussed.multiobjective Problems arising in the transportation and manufacturing systems have been taken for demonstration. The objective of this manuscript is to present an algorithm for solving multi-objective optimization Problem under the optimistic and pessimistic view point. The conflicting natures of the different objective have been handled by defining the membership functions corresponding to it in parabolic intuitionistic fuzzy set environment and thus the Problem becomes parabolic multi-objective non-Linear optimization Programming Problem (PMONLOPP). A Linear and non-Linear membership functions corresponding to each objective has been taken in account. An illustrative examples from transportation as well as in manufacturing systems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Further-more, experimental results indicate that the proposed approach may yield better solutions to these types of Problems than those obtained by using current algorithms.

Shiv Prasad Yadav - One of the best experts on this subject based on the ideXlab platform.

  • modeling and optimization of multi objective non Linear Programming Problem in intuitionistic fuzzy environment
    Applied Mathematical Modelling, 2015
    Co-Authors: Sujeet Kumar Singh, Shiv Prasad Yadav
    Abstract:

    Abstract In dealing with real world practical optimization Problems, a decision maker usually faces a state of uncertainty as well as hesitation, due to various unpredictable factors. Sometimes it is necessary to optimize several non-Linear and conflicting objectives simultaneously. To deal with the uncertain parameters which arise in such situations, intuitionistic fuzzy numbers are utilized. We formulate a multiobjective non-Linear Programming Problem in intuitionistic fuzzy environment. We propose a Linear ranking function and utilize it to convert the intuitionistic fuzzy model into a crisp model. After converting the Problem into equivalent crisp Problem, we propose a non-Linear membership function and develop various approaches for solving it by using different operators and fuzzy Programming technique. We apply our methodologies for justification to a numerical Problem in manufacturing systems.

Masahiro Inuiguchi - One of the best experts on this subject based on the ideXlab platform.

  • robust optimization under softness in a fuzzy Linear Programming Problem
    International Journal of Approximate Reasoning, 1998
    Co-Authors: Masahiro Inuiguchi, Masatoshi Sakawa
    Abstract:

    Abstract In this paper, we discuss the softness and the robustness of the optimality in the setting of Linear Programming Problems with a fuzzy objective function. A fuzzy goal defined on the deviation from the optimal value is introduced in order to define the soft-optimal solution. Fuzzy coefficients are regarded as possibility distributions. A necessity measure based on the possibility distribution is used for defining a necessarily optimal solution, i.e., a robust-optimal solution. Since a necessarily optimal solution does not exist in many cases, a necessarily soft-optimal solution is defined. A solution algorithm for the best necessarily soft-optimal solution is proposed.

Junjian Huang - One of the best experts on this subject based on the ideXlab platform.

  • A Recurrent Neural Network for Solving Bilevel Linear Programming Problem
    IEEE Transactions on Neural Networks and Learning Systems, 2014
    Co-Authors: Xing He, Chuandong Li, Tingwen Huang, Chaojie Li, Junjian Huang
    Abstract:

    In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel Linear Programming Problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.