All a b c d e f g h i j k l m n o p q r s t u v w x y z Other

Previous Page 4 Next

Displaying 61 – 80 of 132

Showing per page

Managing a patient waiting list with time-dependent priority and adverse events

Daiki Min, Yuehwern Yih (2014)

RAIRO - Operations Research - Recherche Opérationnelle

This paper addresses the problem of managing a waiting list for elective surgery to decide the number of patients selected from the waiting list and to schedule them in accordance with the operating room capacity in the next period. The waiting list prioritizes patients not only by their initial urgency level but also by their waiting time. Selecting elective surgery patients requires a balance between the waiting time for urgent patients and that for less urgent patients. The problem is formulated...

Markov decision processes on finite spaces with fuzzy total rewards

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

Kybernetika

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, where...

Markov decision processes with time-varying discount factors and random horizon

Rocio Ilhuicatzi-Roldán, Hugo Cruz-Suárez, Selene Chávez-Rodríguez (2017)

Kybernetika

This paper is related to Markov Decision Processes. The optimal control problem is to minimize the expected total discounted cost, with a non-constant discount factor. The discount factor is time-varying and it could depend on the state and the action. Furthermore, it is considered that the horizon of the optimization problem is given by a discrete random variable, that is, a random horizon is assumed. Under general conditions on Markov control model, using the dynamic programming approach, an optimality...

Mean-variance optimality for semi-Markov decision processes under first passage criteria

Xiangxiang Huang, Yonghui Huang (2017)

Kybernetika

This paper deals with a first passage mean-variance problem for semi-Markov decision processes in Borel spaces. The goal is to minimize the variance of a total discounted reward up to the system's first entry to some target set, where the optimization is over a class of policies with a prescribed expected first passage reward. The reward rates are assumed to be possibly unbounded, while the discount factor may vary with states of the system and controls. We first develop some suitable conditions...

Minimizing risk probability for infinite discounted piecewise deterministic Markov decision processes

Haifeng Huo, Jinhua Cui, Xian Wen (2024)

Kybernetika

The purpose of this paper is to study the risk probability problem for infinite horizon piecewise deterministic Markov decision processes (PDMDPs) with varying discount factors and unbounded transition rates. Different from the usual expected total rewards, we aim to minimize the risk probability that the total rewards do not exceed a given target value. Under the condition of the controlled state process being non-explosive is slightly weaker than the corresponding ones in the previous literature,...

Modèles Analytiques de Routeurs

Emmanuel Besson (2010)

RAIRO - Operations Research

We focus on performance study of routers in high-speed network through a queuing network analytical model. Such a model gives accurate results about classical performance criteria. For example, analytical study of packet loss probabilities in a router uses a product-form queuing network. The analytical results are compared to simulation results, and they provide routers managers with invaluable information for internal memories tuning.

Monotone optimal policies in discounted Markov decision processes with transition probabilities independent of the current state: existence and approximation

Rosa María Flores-Hernández (2013)

Kybernetika

In this paper there are considered Markov decision processes (MDPs) that have the discounted cost as the objective function, state and decision spaces that are subsets of the real line but are not necessarily finite or denumerable. The considered MDPs have a cost function that is possibly unbounded, and dynamic independent of the current state. The considered decision sets are possibly non-compact. In the context described, conditions to obtain either an increasing or decreasing optimal stationary...

Monotonicity of minimizers in optimization problems with applications to Markov control processes

Rosa M. Flores–Hernández, Raúl Montes-de-Oca (2007)

Kybernetika

Firstly, in this paper there is considered a certain class of possibly unbounded optimization problems on Euclidean spaces, for which conditions that permit to obtain monotone minimizers are given. Secondly, the theory developed in the first part of the paper is applied to Markov control processes (MCPs) on real spaces with possibly unbounded cost function, and with possibly noncompact control sets, considering both the discounted and the average cost as optimality criterion. In the context described,...

Nash ϵ -equilibria for stochastic games with total reward functions: an approach through Markov decision processes

Francisco J. González-Padilla, Raúl Montes-de-Oca (2019)

Kybernetika

The main objective of this paper is to find structural conditions under which a stochastic game between two players with total reward functions has an ϵ -equilibrium. To reach this goal, the results of Markov decision processes are used to find ϵ -optimal strategies for each player and then the correspondence of a better answer as well as a more general version of Kakutani’s Fixed Point Theorem to obtain the ϵ -equilibrium mentioned. Moreover, two examples to illustrate the theory developed are presented....

Nonparametric adaptive control for discrete-time Markov processes with unbounded costs under average criterion

J. Minjárez-Sosa (1999)

Applicationes Mathematicae

We introduce average cost optimal adaptive policies in a class of discrete-time Markov control processes with Borel state and action spaces, allowing unbounded costs. The processes evolve according to the system equations x t + 1 = F ( x t , a t , ξ t ) , t=1,2,..., with i.i.d. ℝ k -valued random vectors ξ t , which are observable but whose density ϱ is unknown.

Note on stability estimation in average Markov control processes

Jaime Martínez Sánchez, Elena Zaitseva (2015)

Kybernetika

We study the stability of average optimal control of general discrete-time Markov processes. Under certain ergodicity and Lipschitz conditions the stability index is bounded by a constant times the Prokhorov distance between distributions of random vectors determinating the “original and the perturbated” control processes.

Currently displaying 61 – 80 of 132