Monte Carlo (MC) is widely used for the simulation of discrete time Markov chains. We consider the case of a \(d\)-dimensional continuous state space and w...

Randomized Quasi-Monte Carlo (RQMC) methods provide unbiased estimators whose variance often converges at a faster rate than standard Monte Carlo as a functi...

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (Q...

We consider the problem of estimating the density of a random variable \(X\) which is the output of a simulation model. We show how an unbiased density ...

We study quasi-Monte Carlo (QMC) integration of smooth functions defined over the multi-dimensional unit cube. Inspired by a recent work of Pan and Owen, we ...

We explore the use of Array-RQMC, a randomized quasi-Monte Carlo method designed for the simulation of Markov chains, to reduce the variance when simulating...

We give an overview of the state of the art on the design and implementation of random number generators for simulation and general Monte Carlo sampling in p...

Based on data from real call centers, we develop, test, and compare forecasting methods to predict the waiting time of a call upon its arrival to the center,...

We study a solution approach for a staffing problem in multi-skill call centers. The objective is to find a minimal-cost staffing solution while meeting a ta...

We present LatNet Builder, a software tool to find good parameters for lattice rules, polynomial lattice rules, and digital nets in base 2, for quasi-Monte...

Estimating the unknown density from which a given independent sample originates is more difficult than estimating the mean, in the sense that for the best po...

Array-RQMC has been proposed as a way to effectively apply randomized quasi-Monte Carlo (RQMC) when simulating a Markov chain over a large number of steps to...

We extend a quasi-Monte Carlo scheme designed for coagulation to the simulation of the coagulation-fragmentation equation. A number \(N\) of particles is ...

We consider a two-stage stochastic discrete program in which some of the second stage constraints involve expectations that cannot be computed easily and a...

In emergency call centers (for police, firemen, ambulances, rescue teams) a single event can sometimes trigger many incoming calls to the center in a short p...

We study the behavior of a generalized splitting method for sampling from a given distribution conditional on the occurrence of a rare event. The method retu...

Random number generators were invented before there were symbols for writing numbers, and long before mechanical and electronic computers. All major civiliza...

We consider a network whose links have random capacities and in which a certain target amount of flow must be carried from some source nodes to some destina...

We study the behavior of a generalized splitting method for sampling from a given distribution conditional on the occurrence of a rare event. The method retu...

We survey basic ideas and results on randomized quasi-Monte Carlo (RQMC) methods, discuss their practical aspects, and give numerical illustrations. RQM...

We study the lattice structure of random number generators of the MIXMAX family, a class of matrix linear congruential generators that produce a vector of...

The search neutrality debate is about whether search engines should or should not be allowed to uprank certain results among the organic content matching a...

We study and compare various methods to generate a random variate from the normal distribution truncated to some finite or semi-infinite interval, with spec...

We review the Array-RQMC method, its variants, sorting strategies, and convergence results. We are interested in the convergence rate of measures of discrepa...

We consider a stochastic staffing problem with uncertain arrival rates. The objective is to minimize the total cost of agents under some chance constraints, ...

We are interested in predicting the wait time of customers upon their arrival in some service system such as a call center or emergency service. We propose t...

We consider a staffing problem with probabilistic constraints in an emergency call center. The aim is to minimize the total cost of agents while satisfying...

We develop customer delay predictors for multi-skill call centers that take as inputs the queueing state upon arrival and the waiting time of the last custom...

The effective management of call centers is a challenging task mainly because managers are consistently facing considerable uncertainty. Among important sour...

When a keyword-based search query is received by a search engine (SE), a classified ads website, or an online retailer site, the platform has exponentially...

Traditionally, both researchers and practitioners rely on standard Erlang queueing models to analyze call center operations. In those models, service times a...

We examine the requirements and the available methods and software to provide (or imitate) uniform random numbers in parallel computing environments. In this...

We introduce a new software tool and library named Lattice Builder, written in C++, that implements a variety of construction algorithms for good rank-1 latt...

In a static network reliability model one typically assumes that the failures of the components of the network are independent. This simplifying assumption m...

We propose an exponential tilting method for exact simulation from the truncated multivariate student-t distribution in high dimensions as an alternative t...

We study call routing policies for call centers with multiple call types and multiple agent groups. We introduce new weight-based routing policies where each...

We consider different statistical models for the call arrival process in telephone call centers. We evaluate the forecasting accuracy of those models by desc...

In this paper, we propose new mathematical programming approaches for computing time-dependent bid prices in network revenue management problems. In contrast...

We study call routing policies commonly used in call centers with multiple call types and multiple agent groups. We propose a new weight-based routing polic...

Monte Carlo method for estimating multidimensional integrals, with applications to rare-event probability estimation. The method fuses two distinct and po...

Randomized quasi-Monte Carlo (RQMC) can be seen as a variance reduction method that provides an unbiased estimator of the integral of a function <i>f</i> ov...

Advanced discrete choice models, such as parametric/non-parametric mixed logit and hybrid choice models, are heavily used in travel behavior research. The...

We study the problem of constructing shifted rank-1 lattice rules for the approximation of high-dimensional integrals with a low weighted star discrepancy, f...

We study the convergence behavior of a randomized quasi-Monte Carlo (RQMC) method for the simulation of discrete-time Markov chains, known as array-RQMC. The...

We consider a class of Markov chain models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multicomponent...

We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variance-reduction techniques, the integration erro...

Staffing and scheduling optimization in large multiskill call centers is time-consuming, mainly because it requires lengthy simulations to evaluate performan...

<p>Monte Carlo simulation is an incredibly versatile tool for studying complex stochastic systems. By replicating the simulation several times independent...

For every stochastic simulation model, there is in theory a way of changing the probability laws that drive the system so that the resulting IS estimator h...

The asymptotic robustness of estimators as a function of a rarity parameter, in the context of rare-event simulation, is often qualified by properties such a...

The coupling-from-the-past (CFTP) algorithm of Propp and Wilson, also called perfect sampling, permits one to sample exactly from the stationary distributio...

We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing...

Random number generators based on linear recurrences modulo 2 are among the fastest long-period generators currently available. The uniformity and independe...

We propose and analyze a quasi-Monte Carlo (QMC) method for simulating a discrete-time Markov chain on a discrete state space of dimension <img src="/cgi-...

Starting from coding-theoretic constructions, we build digital nets with good figures of merit, where the figure of merit takes into account the equidistrib...

Variance reduction techniques (VRTs) are often essential to make simulation quick and accurate enough to be useful. A case in point is simulation-based opti...

A popular approach for modeling dependence in a finite-dimensional random vector <b>X</b> with given univariate marginals is via a normal copula that fits t...

We introduce <i>TestU01</i>, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical te...

We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in th...

Importance sampling (IS) is the primary technique for constructing reliable estimators in the context of rare-event simulation. The asymptotic robustness of...

The fastest long-period random number generators currently available are based on linear recurrences modulo 2. So far, software that provides multiple disjo...

Besides speed and period length, the quality of uniform random number generators is usually assessed by measuring the uniformity of their point sets, formed...

G. Marsaglia introduced recently a class of very fast <i>xorshift</i> random number generators, whose implementation uses three "xorshift" operations. They ...

We study an iterative cutting-plane algorithm on an integer program, for minimizing the staffing costs of a multiskill call center subject to service-level ...

Fast uniform random number generators with extremely long periods have been defined and implemented based on linear recurrences modulo 2. The twisted GFSR ...

We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variance-reduction techniques, and the main classe...

We propose a Dynamic Programming (DP) approach combined with approximation for pricing options embedded in bonds, the focus being on call and put options wit...

Lattice rules are quasi-Monte Carlo methods for estimating large-dimensional integrals over the unit hypercube. In this paper, after briefly reviewing key i...

This paper explores new ways of constructing and implementing random number generators based on linear recurrences in a finite field with 2<sup><i>w</i></sup...

Several methods for reducing the variance in the context of Monte Carlo simulation are based on correlation induction. This includes antithetic variates, L...

We study the structure and point out weaknesses of recently-proposed random number generators based on special types of linear recurrences with small coeffic...

We develop stochastic models of time-dependent arrivals, with focus on the application to call centers. Our models reproduce essential features of call cen...

Lattice rules are among the best methods to estimate integrals in a large number of dimensions. They are part of the <i>quasi-Monte Carlo</i> set of tools. ...

Call and put options embedded in bonds are of American-style, and cannot be priced in a closed-form. In this paper, we formulate the problem of pricing thes...

This is a review article on lattice methods for multiple integration over the unit hypercube, with a variance-reduction viewpoint. It also contains some new ...

Pricing Asian options based on the arithmetic average, under the Black and Scholes model, involves estimating an integral (a mathematical expectation) for w...

A hedger of a contingent claim may decide to partially replicate on some states of nature and not on the others: A partial hedge initially costs less than a...

The cell-loss ratio at a given node in an ATM switch, defined as the steady-state fraction of packets of information that are lost at that node due to buffe...

Different versions of the serial test for testing the uniformity and independence of vectors of successive values produced by a (pseudo)random number genera...

Central limit theorems are obtained for the ``perturbation analysis Robbins-Monro single run'' (PARMSR) optimization algorithm, with updates either after ev...

This study concerns a generic model-free stochastic optimization problem requiring the minimization of a risk function defined on a given bounded domain in ...

We consider an ATM switch model, in which each of <i>N</i> sources is a Markov modulated rate process. We look at some approximations that have been propos...

Combining parallel multiple recursive sequences provides an efficient way of implementing random number generators with long periods and good structural pro...

Central limit theorems are obtained for the PARMSR (perturbation analysis Robbins-Monro single run) algorithm with averaging, updated either after every reg...

Convergence rate results are derived for a stochastic optimization problem where a performance measure is minimized with respect to a vector parameter <img ...

Uniformity tests based on a discrete form of entropy are introduced and studied in the context of empirical testing of uniform random number generators. Nu...

We give an overview of the main techniques for generating uniform random numbers of computers. Theoretical and practical issues are discussed.

We consider a class of stochastic models for which the performance measure is defined as a mathematical expectation that depends on a parameter <img src="th...

In this work, we examine how to combine the score function method with the standard crude Monte Carlo and experimental design approaches, in order to evalua...

We analyze the random number generators obtained by combining two or more multiple recursive generators. We study the lattice structure of such combined gen...

In this paper, we develop mathematical machinery for verifying that a broad class of general state space Markov chains reacts smoothly to certain types of p...

Approaches like infinitesimal perturbation analysis and the likelihood ratio method have drawn a great deal of attention recently, as ways of estimating the...

The lattice structure of conventional linear congruential random number generators (LCGs), over integers, is well known. In this paper, we study LCGs in the...

Infinitesimal Perturbation Analysis (IPA) is perhaps the most efficient derivative estimation method for many practical discrete-event stochastic systems, w...

We show that in most interesting cases where infinitesimal perturbation analysis (IPA) applies for derivative estimation, a finite-difference scheme with co...

We analyze a class of combined random number generators proposed by L'Ecuyer (1988), which combines a set of linear congruential generators (LCGs) with dist...

We describe different derivative estimators for the case of steady-state performances measures and obtain the order of their convergence rates. These estima...

In this paper, we propose three combined Tausworthe random number generators with period length about 10¹⁸, whose <i>k</i>-distribution propertie...

In this paper, we suggest an approximation procedure for the solution of two-player zero-sum stochastic games with continuous state and action spaces simila...

The production scheduling problem considered in this paper is related to the planning of operations of a flexible manufacturing cell composed of a punch pres...

This paper deals with an infinite-horizon discrete-event dynamic programming model with discounting, and with Borel state and action spaces. Instead of the ...

This paper is concerned with a <i>repair vs replacement</i> problem for a system observed in continuous time and subject to random failure. When the system ...

This paper presents a general dynamic programming algorithm for the solution of optimal stochastic control problems concerning a class of discrete event syst...

Thèse de doctorat

A preventive replacement problem is formulated in discrete time for a multicomponent system having non identical elements. The components are assumed to be ...