# Miguel LejeuneGeorge Washington University | GW

Miguel Lejeune

PhD

## About

100

Publications

21,446

Reads

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2,018

Citations

Introduction

Miguel Lejeune is a Tenured Full Professor at George Washington University.

Additional affiliations

September 2008 - present

## Publications

Publications (100)

We develop stochastic programming models for project portfolio selection under the plan-driven waterfall approach and the more flexible agile approach. The models account for the requirement to earn return fast and to generate a certain return with high probability. The models take the form of static (waterfall) and dynamic (agile) disjunctive inte...

We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasser...

We study the distributionally robust linearized stable tail adjusted return ratio (DRLSTARR) portfolio optimization problem, in which the objective is to maximize the worst-case linearized stable tail adjusted return ratio (LSTARR) performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties,...

The price-based unit commitment (PBUC) problem aims to optimise the power generating units' schedules to meet the system demand with the objective to maximise the generation companies' (GENCOs') profit. State-of-the-art PBUC models have taken into account exogenous uncertainties in renewable generation, demand, and price signals. This study propose...

We study the distributionally robust linearized stable tail adjusted return ratio (DRLSTARR) portfolio optimization problem, in which the objective is to maximize the worst-case linearized stable tail adjusted return ratio (LSTARR) performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties,...

This paper examines optimization models that use liquidity constraints to track an index. Liquidity is relevant from a risk management perspective but has hardly been explored in the portfolio optimization literature. The liquidity aspect is especially critical in emerging markets. We present two modeling approaches to instill liquidity in index tr...

We present a spatiotemporal data set of all out-of-hospital sudden cardiac arrests (OHCA) dispatches for the City of Virginia Beach. We also develop a modular toolkit that can be used to process the data and generate problem instances based on user-defined input. The data were collected from multiple sources, and our analysis process was validated...

We study distributionally robust chance-constrained programming (DRCCP) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to all types of distributionally robust chance-constrained optimization problems subjected to individual as well as joint chance constraints, with ran...

Inspired by the opportunities provided by the Industry 4.0 technologies for smarter, risk‐informed, safer, and resilient operation, control, and management of the lifeline critical networks, this paper investigates mobility‐as‐a‐service for resilience delivery during natural disasters. Focusing on effective service restoration in power distribution...

Widespread outbreaks of infectious disease, i.e., the so-called pandemics that may travel quickly and silently beyond boundaries, can significantly upsurge the morbidity and mortality over large-scale geographical areas. They commonly result in enormous economic losses, political disruptions, social unrest, and quickly evolve to a national security...

We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the...

We propose a novel partial sample average approximation (PSAA) framework to solve the two main types of chance-constrained linear matrix inequality (CCLMI) problems: CCLMI with random technology matrix and CCLMI with random right-hand side. We propose a series of computationally tractable PSAA-based approximations for CCLMI problems, analyze their...

We define a regularized variant of the dual dynamic programming algorithm called DDP-REG to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regularization of the stochastic dual dynamic p...

When engaging in the development of new products, the primary objective of start-up companies is to generate a specified return level quickly and with high confidence. Achieving this goal is complicated because of uncertainties in projects’ returns and durations. In the study titled, “Waterfall and Agile Product Development Approaches: Disjunctive...

The legacy Optimal Power Flow (OPF) dispatch in electric power grids with high proliferation of renewables can be at risk due to the lack of awareness on major uncertainties and sudden changes in renewable outputs. This may, in turn, result in conditions where transmission line power flows are significantly exceeded and subsequent automatic protect...

Area under ROC curve (AUC) is a performance measure for classification models. We propose new distributionally robust AUC models (DR-AUC) that rely on the Kantorovich metric and approximate AUC with the hinge loss function, and derive convex reformulations using duality. The DR-AUC models outperform deterministic AUC and support vector machine mode...

We consider a lender (bank) that determines the optimal loan price (interest rate) to offer to prospective borrowers under uncertain borrower response and default risk. A borrower may or may not accept the loan at the price offered, and both the principal loaned and the interest income become uncertain because of the risk of default. We present a r...

Area under ROC curve (AUC) is a widely used performance measure for classification models. We propose a new distributionally robust AUC maximization model (DR-AUC) that relies on the Kantorovich metric and approximates the AUC with the hinge loss function. We use duality theory to reformulate the DR-AUC model as a tractable convex quadratic optimiz...

Non-profit organizations play a central role in responding to the devastating consequences of epidemic outbreaks in developing economies. We propose an epidemic response model in resource-limited countries that determines the number, size, and location of treatment facilities, deploys critical medical staff, locates ambulances to triage points, and...

Disasters in developing countries tremendously affect the economy and long-term development. Recent years have seen an increase in epidemic outbreaks in countries like Haiti and in West Africa. However, there seems to be a lack of decision support to address epidemic outbreak challenges in developing countries compared to their developed counterpar...

A recent article by Zaghian et al. (2018) proposed reformulations, proposed reformulations of stochastic chance-constrained programming models for radiation therapy treatment planning. This note questions the validity of the proposed reformulations and shows that they are not equivalent to the original formulations. Two numerical examples illustrat...

We study a multi-objective portfolio optimization model that employs two conflicting objectives—maximizing mean return, and minimizing risk as measured by the Gini Mean Difference (GMD). We assume that an investor’s implicit utility is a function of these two objectives and help the investor identify the optimal (i.e., most preferred) portfolio amo...

We propose a new fractional stochastic integer programming model for forestry revenue management. The model takes into account the main sources of uncertainties—wood prices and tree growth—and maximizes a reliability-to-stability revenue ratio that reflects two major goals pursued by forest owners. The model includes a joint chance constraint with...

We consider probabilistically constrained stochastic programming problems, in which the random variables are in the right-hand sides of the stochastic inequalities defining the joint chance constraints. Problems of that kind arise in a variety of contexts, and are particularly difficult to solve for random variables with continuous joint distributi...

Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techn...

Multi-portfolio optimization problems and the incorporation of marginal risk contribution constraints have recently received a sustained interest from academia and financial practitioners. We propose a class of new stochastic risk budgeting multi-portfolio optimization models that impose portfolio as well as marginal risk constraints. The models pe...

We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely evacuation and medical treatment to injured soldiers. The model enforces the “Golden Hour” evacuation doctrine, attempts to maximize the expected number of severely injured soldiers evacuated within one hour...

Purpose
To propose a novel evidence-based Haddon matrix that identifies intervention options for organizations and governments responding to an epidemic in a developing economy.
Design/methodology/approach
A literature review of articles published within a year of the cholera outbreak in Haiti. Two separate types of literature sources are used –...

The timing of forest stands harvesting is an important operational decision in forestry. Major goals of private nonindustrial forest owners are to achieve a steady flow of profits while reaching an overall satisfactory and reliable profit level. These goals are pursued under uncertainties in the growth of trees in di erent regions and in the prices...

This paper proposes a multistage stochastic programming approach for the asset-liability management of Brazilian pension funds. We generate asset price scenarios with stochastic differential equations—Geometric Brownian Motion model for stocks and Cox–Ingersoll–Ross model for fixed income securities. Intertemporal solvency regulatory rules for Braz...

We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regulariza...

We study an extended set of Mean-Gini portfolio optimization models that encompasses a general version of the mean-risk formulation, the Minimal Gini model (MinG) that minimizes Gini’s Mean Differences, and the new risk-adjusted Mean-Gini Ratio (MGR) model. We analyze the properties of the various models, prove that a performance measure based on a...

We consider a class of multi-objective probabilistically constrained programs (MOPCP) with a joint probabilistic constraint and a variable risk level. We consider two cases with only a random right-hand side vector or a multi-row random technology matrix, and propose a Boolean modeling framework to derive new mixed-integer linear programs (MILP) th...

We consider a lender (bank) who determines the optimal loan price (interest rates) to offer to prospective borrowers under uncertain risk and borrower response. A borrower may or may not accept the loan at the price offered, and in the presence of default risk, both the principal loaned and the interest income become uncertain. We present a risk-ba...

We study the online display advertising problem in which advertisers’ demands for ad exposures (impressions) of various types compete for slices of shared resources. In general, advertisers prefer to receive impressions that are evenly-spread across the audience segments they target, as well as evenly-spread across time. In order to accomplish this...

From a practical perspective, the paper demonstrates that the appropriate use of dispersion, population, and equity criteria can lead to fairly good solutions with respect to the p-median objective. The only stipulation is that the decision maker verifies (through simple constraint checks) that the chosen locations meet the dispersion, population,...

We introduce a risk-averse stochastic modeling approach for a pre-disaster relief network design problem under uncertain demand and transportation capacities. We determine the sizes and locations of the response facilities and the inventory levels of relief supplies at each facility while guaranteeing a certain level of network reliability. We intr...

We consider a class of multi-objective probabilistically constrained problems MOPCP with a joint chance constraint, a multi-row random technology matrix, and a risk parameter (i.e., the reliability level) defined as a decision variable. We propose a Boolean modeling framework and derive a series of new equivalent mixed-integer programming formulati...

We consider a probabilistic portfolio optimization model including fixed and proportional transaction costs. We derive a deterministic equivalent of the probabilistic model for fat-tailed portfolio returns. We develop a method which finds provably near-optimal solutions in minimal amount of time for industry-sized (up to 2000 assets) problems. To s...

Foreign investment decisions by multinational enterprises are usually arrived by considering not just the firm-specific factors, but location-specific factors are also of paramount importance. Using three supply factors specific to a country – supply environment, supply infrastructure, absorptive capacity, we construct a rating system representing...

We propose models to investigate effectiveness–equity tradeoffs in tree network facility location problems. We use the commonly used median objective as a measure of effectiveness, and the Gini index as a measure of (in)equity, and formulate bicriteria problems involving these objectives. We develop procedures to identify an efficient set of soluti...

This study investigates multiperiod service level (MSL) policies in supply chains facing a stochastic customer demand. The objective of the supply chains is to construct integrated replenishment plans that satisfy strict stockout-oriented performance measures which apply across a multiperiod planning horizon. We formulate the stochastic service lev...

We develop a new modeling and solution method for stochastic programming problems that include a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean function (p...

We study a probabilistic portfolio optimization model in which trading restrictions modeled with combinatorial constraints are accounted for. We provide several deterministic reformulations equivalent to this stochastic programming problem and discuss their computational efficiency. The reformulated problem takes the form of a mixed-integer nonline...

We develop a new modeling and exact solution method for stochastic programming problems that include a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean funct...

We study a probabilistic portfolio optimization model in which trading restrictions modeled with combinatorial constraints are accounted for. We provide several deterministic reformulations equivalent to this stochastic programming problem and discuss their computational efficiency. The reformulated problem takes the form of a mixed-integer nonline...

We develop a new modeling and exact solution method for stochastic programming problems that include a joint probabilistic constraint in which the multi-row random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean func...

This study revisits the celebrated p-efficiency concept introduced by Prékopa (Z.Oper. Res. 34:441–461, 1990) and defines a p-efficient point (pLEP) as a combinatorial pattern. The new definition uses elements from the combinatorial pattern recognition
field and is based on the combinatorial pattern framework for stochastic programming problems pro...

Os modelos de média-variância de otimização de carteira apresentam questionamentos em relação ao seu efetivo desempenho devido ao chamado erro de estimação. Em conseqüência, a otimização estocástica vêm aumentando sua importância devido à possibilidade da inclusão da incerteza na estimativa dos parâmetros. Neste estudo foi avaliado o desempenho do...

We propose a new modeling and solution method for probabilistically constrained optimization problems. The methodology is based on the integration of the stochastic programming and combinatorial pattern recognition fields. It permits the fast solution of stochastic optimization problems in which the random variables are represented by an extremely...

We evaluate the creditworthiness of banks using statistical, as well as combinatorics-, optimization-, and logic-based methodologies. We reverse-engineer the Fitch risk ratings of banks using ordered logistic regression, support vector machine, and Logical Analysis of Data (LAD). The LAD ratings are shown to be the most accurate and most successful...

Enhanced indexation is a structured investment approach that combines passive and active financial management techniques. We propose an enhanced indexation model whose goal is to maximize the excess return that can be attained with high reliability, while ensuring that the relative market risk does not exceed a specified limit. We measure the relat...

We propose a probabilistic version of the Markowitz portfolio problem with proportional transaction costs. We derive equivalent convex reformulations, and analyze their computational efficiency for solving large (up to 2000 securities) portfolio problems. There is a great disparity in the solution times. The time differential between formulations c...

The goal of this paper is to address the problem of evaluating the performance of a system running under unknown values for
its stochastic parameters. A new approach called LAD for Simulation, based on simulation and classification software, is presented. It uses a number of simulations with very few replications
and records the mean value of direc...

The central objective of this paper is to develop a transparent, consistent, self-contained, and stable country risk rating
model, closely approximating the country risk ratings provided by Standard and Poor’s (S&P). The model should be non-recursive,
i.e., it should not rely on the previous years’ S&P ratings. The set of variables selected here in...

Probabilistically constrained problems, in which the random variables are finitely distributed, are non-convex in general and hard to solve. The p-efficiency concept has been widely used to develop efficient methods to solve such problems. Those methods require the generation of p-efficient points (pLEPs) and use an enumeration scheme to identify p...

We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is forced to be below a low-risk threshold with a large probability, thereby limi...

We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is forced to be below a low-risk threshold with a largeprobability, thereby limit...

We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is forced to be below a low-risk threshold with a large probability, thereby limi...

We propose a new modeling and solution method for probabilistically constrained optimization problems. The methodology is based on the integration of the stochastic programming and combinatorial pattern recognition fields. It permits the very fast solution of stochastic optimization problems in which the random variables are represented by an extre...

This study uses a novel combinatorial method, the Logical Analysis of Data (LAD), to reverse-engineer and construct credit
risk ratings, which represent the creditworthiness of financial institutions and countries. The proposed approaches are shown
to generate transparent, consistent, self-contained, and predictive credit risk rating models, closel...

In this paper we address the following probabilistic version (PSC) of the set cover- ing problem: $ min{cx | P(Ax ≥ ξ) ≥ p, x_j \in {0, 1}N }$ where A is a 0-1 matrix, ξ is a random 0-1 vector and $p \in (0, 1]$ is the threshold probability level. We formulate (PSC) as a mixed integer non-linear program (MINLP) and linearize the resulting (MINLP) t...

In this paper, we study extensions of the classical Markowitz mean-variance portfolio optimization model. First, we consider that the expected asset returns are stochastic by introducing a probabilistic constraint, which imposes that the expected return of the constructed portfolio must exceed a prescribed return threshold with a high confidence le...

This research was motivated by our work with the private investment group of an international bank. The ob-jective is to construct fund-of-funds (FoF) that follow an absolute return strategy and meet the requirements imposed by the Value-at-Risk (VaR) market risk measure. We propose the VaR-Black Litterman model which accounts for the VaR and tradi...

We construct a discrete-time, multi-period replenishment plan that integrates the inventory, production and distribution functions and that satisfies the conditions of a very demanding cycle service level. The corresponding optimization problem takes the form of a very complex mixed-integer stochastic program. We develop a new enumerative algorithm...

The Fred Astaire East Side Dance Studio in New York City holds ballroom dancing showcases at least twice a year to provide its students with an environment for socializing, practice, and improvement. The most important part of organizing a showcase is constructing the dance-presentations timetable. The number of participants is increasing each year...

We construct an integrated multi-period inventory–production–distribution replenishment plan for three-stage supply chains. The supply chain maintains close relationships with a small group of suppliers, and the nature of the products (bulk, chemical, etc.) makes it more economical to rely upon a direct shipment, full-truck load distribution policy...

In this paper, we study extensions of the classical Markowitz’ mean-variance portfolio optimization model. First, we consider that the expected asset returns are stochastic by introducing a probabilistic constraint imposing that the expected return of the constructed portfolio must exceed a prescribed return level with a high conﬁdence level. We st...

In this paper we address the following probabilistic version (PSC) of the set cover- ing problem: $ min{cx | P(Ax ≥ ξ) ≥ p, x_j \in {0, 1}N }$ where A is a 0-1 matrix, ξ is a random 0-1 vector and $p \in (0, 1]$ is the threshold probability level. We formulate (PSC) as a mixed integer non-linear program (MINLP) and linearize the resulting (MINLP) t...

In this paper, we study extensions of the classical Markowitz’ mean-variance portfolio optimization model. First, we consider that the expected asset returns are stochastic by introducing aprobabilistic constraint imposing that the expected return of the constructed portfolio must exceeda prescribed return level with a high conﬁdence level. We stud...

We consider a supply chain operating in an uncertain environment: The customers' demand is characterized by a dis- crete probability distribution. A probabilistic programming approach is adopted for constructing an inventory-production- distribution plan over a multiperiod planning horizon. The plan does not allow the backlogging of the unsatisfied...

Few models have been developed for the integrated planning and scheduling of the inventory, production and distribution functions. In this paper, we consider a three-stage supply chain, for which a sustainable inventory–production–distribution plan over a multi-period horizon is constructed. The associated program takes the form of a general mixed-...

In order to evaluate the creditworthiness of various countries, a learning model is induced from the 1998 Standard and Poor’s country risk ratings, using the 1998 values of nine economic and three political indicators. This learning model allows the construction of a partially ordered set describing the relative superiority of countries on the basi...

Supply chain management is a field at the confluence of many other disciplines; it has been studied under a number of perspectives, which has played a role for the crossbreeding of the discipline. In this paper, we propose a typology of supply chain configurations that contributes to tie together terms that have been used disjointedly for describin...

The planning and scheduling of the inventory, production and distribution functions remains an open research area, since few studies have developed models allowing their integrated management. In this paper, we consider a three-stage supply chain, for which a sustainable inventory-production-distribution plan over a multi-period horizon is construc...

We propose to integrate different algorithms for constructing D-optimum designs for linear models. Our emphasis is on efficiency gain and on applicability to larger models than those currently considered in the literature. We implement a one-exchange algorithm and use a generalized simulated annealing. This method does not require to construct or t...

We develop an exchange algorithm designed for linear models. We name It coordinate-columnwise exchange algorithm because It works columnwise, that Is factor by factor, and it modifies a limited number of coordinates of the considered column at each iteration. Among its advantages, we can cite the speed of the updating process and its ability to acc...

Denial of Service (DoS) attacks consist of overwhelming a server, a network or a Web site in order to paralyze its normal activity. The additional parameter in Distributed Denial of Service (DdoS) attacks is the distributing strategy. It means that DDoS attacks do not come from a single computer but stem from all accessible channels and servers. Co...

Churn management is a fundamental concern for businesses and the emergence of the digital economy has made the problem even more acute. Companies’ initiatives to handle churn and customers’ profitability issues have been directed to more customer-oriented strategies. In this paper, we present a customer relationship management framework based on th...