Ali Benmerzouga

Bayesian Group Replacement Policies

Much research has been performed in finding optimal group replacement policies for production systems consisting of parallel components, where the failure times of the components are independent identically distributed exponential random variables with a common parameter λ. This paper introduces a class of decision rules that utilizes the statistical information obtained during operation of the components. Two forms of statistical input are allowed. We assume that a prior distribution over the possible values of λ is available. It is not required that this prior distribution be in conjugate form. Statistical information that is provided by the actual failure times of the components is incorporated into the decision rule via the sufficient statistics for the problem. This results in group replacement policies that are intuitively attractive, easy to implement, and mathematically tractable.

Optimal m-failure policies with random repair time

The failure times of n machines are i.i.d. exponential random variables with parameter @l. This paper extends Assaf and Shanthikumars (1987) repair and replacement model to the case where repairtime is a nonnegative random variable. The behaviour of the optimal policy as a function of the cost parameters is investigated. The cost function is shown to be unimodal and an easily implemented algorithm for finding optimal policies is developed.

Optimal (m-FailureP-Repairmen) policies with random repair time

The failure times of N machines are i.i.d. exponential random variables with parameter λ. This paper extends the repair model given by [3] to the case where the repair crew is more than one repairmen (P⩾1). We developed and implemented an algorithm for finding optimal policies for the new model. The experiments show that bringing more than one repirman is always the optimal solution

Finite-Dimensional Filtering and Control for Continuous-Time Nonlinear Systems

Abstract A partially observed stochastic control problem is considered in continuous time where both the state and observation processes are given by non-linear dynamics. Measure change techniques applied to the cost process allow both state and observation processes to be thought of as linear. If the cost is given a special form, this transformation changes the original non-linear problem into a linear one.

Optimal filters for a hidden Markov random field model

A Markov random field (MRF) is a useful technical tool for modeling dynamics systems exhibiting some type of spatio-temporal variability. In this paper, we propose optimal filters for the states of a partially observed temporal Markov random field. We also discuss parameters estimation. This generalizes an earlier work by Elliott and Aggoun [1].