Displaying similar documents to “An extended version of average Markov decision processes on discrete spaces under fuzzy environment”

Markov decision processes on finite spaces with fuzzy total rewards

Karla Carrero-Vera, Hugo Cruz-Suárez, Raúl Montes-de-Oca (2022)

Kybernetika

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The paper concerns Markov decision processes (MDPs) with both the state and the decision spaces being finite and with the total reward as the objective function. For such a kind of MDPs, the authors assume that the reward function is of a fuzzy type. Specifically, this fuzzy reward function is of a suitable trapezoidal shape which is a function of a standard non-fuzzy reward. The fuzzy control problem consists of determining a control policy that maximizes the fuzzy expected total reward,...

A genetic algorithm for the multistage control of a fuzzy system in a fuzzy environment.

Janusz Kacprzyk (1997)

Mathware and Soft Computing

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We discuss a prescriptive approach to multistage optimal fuzzy control of a fuzzy system, given by a fuzzy state transition equation. Fuzzy constraints and fuzzy goals at consecutive control stages are given, and their confluence, Bellman and Zadeh's fuzzy decision, is an explicit performance function to be optimized. First, we briefly survey previous basic solution methods of dynamic programming (Baldwin and Pilsworth, 1982) and branch-and-bound (Kacprzyk, 1979), which are plagued by...

An approach to solve a fuzzy bi-objective multi-index fixed charge transportation problem

Maroua Hakim, Rachid Zitouni (2024)

Kybernetika

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In this paper, we propose a novel approach for solving a fuzzy bi-objective multi-index fixed-charge transportation problem where the aim is to minimize two objectives: the total transportation cost and transportation time. The parameters of the problem, such as fixed cost, variable cost, and transportation time are represented as fuzzy numbers. To extract crisp values from these parameters, a linear ranking function is used. The proposed approach initially separates the main problem...

Solving a possibilistic linear program through compromise programming.

Mariano Jiménez López, María Victoria Rodríguez Uría, María del Mar Arenas Parra, Amelia Bilbao Terol (2000)

Mathware and Soft Computing

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In this paper we propose a method to solve a linear programming problem involving fuzzy parameters whose possibility distributions are given by fuzzy numbers. To address the above problem we have used a preference relationship of fuzzy numbers that leads us to a solving method that produces the so-called α-degree feasible solutions. It must be pointed out that the final solution of the problem depends critically on this degree of feasibility, which is in conflict with the optimal value...