Fabian Bastin

Fabian Bastin
Université de Montréal | UdeM · Department of Computer Science and Operations Research

PhD

About

54
Publications
13,083
Reads
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580
Citations
Additional affiliations
June 2019 - present
Université de Montréal
Position
  • Professor (Full)
June 2013 - May 2019
Université de Montréal
Position
  • Professor (Associate)
January 2007 - May 2013
Université de Montréal
Position
  • Professor (Assistant)

Publications

Publications (54)
Article
Extended aircraft arrival management under uncertainty has been previously studied in the literature using two-stage stochastic optimization in the case of a single initial approach fix (IAF) and a single runway. In this paper, we propose an extension taking into account: (i) multiple IAFs feeding the landing runway, (ii) aircraft having different...
Article
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 target level for the quality of service to customers. We consider a common situation in which the arrival rates are unobserved random variables for which preliminary forecasts are available in a...
Article
The extended aircraft arrival management problem, as an extension of the classic aircraft landing problem, seeks to preschedule aircraft on a destination airport a few hours before their planned landing times. A two-stage stochastic mixed-integer programming model enriched by chance constraints is proposed in this paper. The first-stage optimizatio...
Article
We consider a multistage stochastic discrete program in which constraints on any stage might involve expectations that cannot be computed easily and are approximated by simulation. We study a sample average approximation (SAA) approach that uses nested sampling, in which at each stage, a number of scenarios are examined and a number of simulation r...
Article
The arrival manager operational horizon, in Europe, is foreseen to be extended up to 500 n miles around destination airports. In this context, arrivals need to be sequenced and scheduled a few hours before landing, when uncertainty is still significant. A computational study, based on a two-stage stochastic program, is presented and discussed to ad...
Article
Full-text available
Copulas are fast gaining in popularity in fields requiring modelling of multivariate data. Population synthesis is one such domain where copulas hold much promise. Characteristics of a population are inherently a multivariate distribution, for example, age, education and employment have a dependent relationship with each other. However, characteris...
Preprint
Full-text available
An important step in solving a stochastic optimization problem is the search for an efficient method for discretizing the probability distribution of the random parameters. This step becomes even more critical when one works with a multistage problem, where the discretization of the underlying stochastic process may lead to a scenario tree of large...
Preprint
Full-text available
The generation of scenario trees is an important topic in all fields dealing with multistage planning under uncertainty. However, scenario trees tend to grow exponentially as the number of stages increases, which, as of today, limits their application to problems with few stages. Recently, a new framework was developed in Keutchayan et al. [46,47]...
Article
Full-text available
This paper considers a sequential discrete choice problem in a time domain, formulated and solved as a route choice problem in a space domain. Starting from a dynamic specification of time-series discrete choices, we show how it is transferrable to link-based route choices that can be formulated by a finite path choice multinomial logit model. This...
Article
The increasing use of internet as a major ticket distribution channel has resulted in passengers becoming more strategic to fare policy. This potentially induces passengers to book the ticket well in advance in order to obtain a lower fare ticket, and later adjust their ticket when they are sure about trip scheduling. This is especially true in fle...
Article
This paper focuses on the comparison of the random regret minimization (RRM) and mother logit models for analyzing the choice between alternatives having deterministic attributes. The mother logit model allows utilities of a given alternative to depend on attributes of other alternatives. It was designed to relax the independence from irrelevant al...
Article
We propose a way to estimate a family of static Multivariate Extreme Value (MEV) models with large choice sets in short computational time. The resulting model is also straightforward and fast to use for prediction. Following Daly and Bierlaire (2006), the correlation structure is defined by a rooted, directed graph where each node without successo...
Article
Full-text available
Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We des...
Technical Report
Full-text available
The progressive hedging algorithm is to date one of the most popular scenario decomposition methods in multi-stage stochastic programming. While it achieves full decomposition over scenarios, its efficiency remains sensitive to some implementation choices. In particular, the algorithm performance is highly sensitive to the penalty parameter value,...
Article
In this paper, we propose a new nonmonotone adaptive retrospective Trust Region (TR) method for solving unconstrained optimization problems. Inspired by the retrospective ratio proposed in Bastin et al. (Math Program Ser A 123:395–418, 2010), a new nonmonotone TR ratio is introduced based on a convex combination of the nonmonotone classical and ret...
Article
Discrete choice models are commonly used in transportation planning and modeling, but their theoretical basis and applications have been mainly developed in a static context. In this paper, we propose an estimation technique for analyzing the impact of technological changes on the dynamic of consumer demand. The proposed research presents a dynamic...
Conference Paper
Full-text available
This paper focuses on the comparison of estimation and prediction results between the random utility maximization (RUM) and random regret minimization (RRM) frameworks for the route choice recursive logit (RL) model (Fosgerau et al., 2013). The RL model is originally based on the RUM principle. We propose different versions of the RL model based on...
Article
Traditional stochastic programming methods are widely used for solving hydroelectric reservoirs management problems under uncertainty. With these methods, random parameters are described using a scenario tree possessing an unstructured topology. Therefore, traditional methods can potentially handle high-order time-correlation effects, but their com...
Article
The main aim of this paper is to measure the social welfare loss for a continuous moral hazard model when a set of minimal assumptions are fulfilled. By using a new approach, we are able to reproduce the results of Balmaceda, Balseiro, Correa, and Stier-Moses (2010) pertaining to the social welfare loss for discrete and continuous models respective...
Technical Report
Full-text available
Maximum likelihood estimation (MLE) is one of the most popular technique in econometric and other statistical applications due to its strong theoretical appeal, but can lead to numerical issues when the underlying optimization problem is solved. We examine in this paper a range of trust region and line search algorithms and focus on the impact that...
Technical Report
Full-text available
Maximum likelihood estimation (MLE) is one of the most popular technique in econometric and other statistical applications due to its strong theoretical appeal, but can lead to numerical issues when the underlying optimization problem is solved. We examine in this paper a range of trust region and line search algorithms and focus on the impact that...
Technical Report
The multinomial logit (MNL) model is in general used for analyzing route choices in real networks in spite of the fact that path utilities are believed to be correlated. For this reason different attributes, such as path size, have been proposed to deterministically correct the utilities for correlation and they are often used in practice. Yet, sta...
Article
[1] Among the numerous methods proposed over the past decades for solving reservoir management problems, only a few are applicable on high-dimensional reservoir systems (HDRSs). The progressive hedging algorithm (PHA) was rarely used for managing reservoir systems, but this method is a promising alternative to conventionally used methods for managi...
Article
Econometric models based on simulations are used extensively in transportation. Simulation methods provide only an approximation of the objective function and produce estimators that suffer from bias and loss in efficiency. Two types of bias are known to exist in simulation-based estimators: simulation bias, as a result of the nonlinear transformat...
Article
We examine the effectiveness of randomized quasi-Monte Carlo (RQMC) techniques to estimate the integrals that express the discrete choice probabilities in a mixed logit model, for which no closed form formula is available. These models are used extensively in travel behavior research. We consider popular RQMC constructions such as randomized Sobol’...
Article
Advanced discrete choice models-in particular, mixed logit models-are used extensively in transportation. Although much progress in estimation techniques has made them numerically appealing, their properties have not been fully explored. This lack of exploration sometimes leads to confusing quality measurements and misinterpretation of the estimate...
Article
Maximum simulated likelihood (MSL) procedure is generally adopted in discrete choice analysis to solve complex models without closed mathematical formulation. This procedure differs from the maximum likelihood simply because simulated probabilities are inserted into the Log-Likelihood (LL) function. The LL function to be maximized is the sum of the...
Article
Full-text available
Abstract The estimation of random,parameters by means,of mixed logit models is be- coming current practice amongst discrete choice analysts, one of the most straight- forward applications being the derivation of willingness to pay distribution over a heterogeneous population. In numerous practical cases, parametric distributions are a priori specie...
Article
Full-text available
We introduce a new trust-region method for unconstrained optimization where the radius update is computed using the model information at the current iterate rather than at the preceding one. The update is then performed according to how well the current model retrospectively predicts the value of the objective function at last iterate. Global conve...
Conference Paper
Full-text available
The overall energy efficiency of a pulp and paper mill is directly influenced by the design of both the water system and the heat transfer network. In this industry, energy and water systems are linked through the water tank network, and consequently, the production of warm and hot water should be carefully evaluated to identify design improvements...
Article
Full-text available
We consider a class of stochastic programming models where the uncertainty is classically represented using parametric distribution families, but with unknown parameters that will be estimated together with the optimal value of the problem. However, misspecification of the underlying random variables often leads to unrealistic results when little i...
Article
Full-text available
The estimation of random parameters by means of mixed logit models is becoming current practice amongst discrete choice analysts, one of the most straightforward applications being the derivation of willingness to pay distribution over a heterogeneous population. In numerous practical cases, parametric distributions are a priori specified and the p...
Article
This paper develops a model of activity and trip scheduling that combines three elements that have to date mostly been investigated in isolation: the duration of activities, the time-of-day preference for activity participation and the effect of schedule delays on the valuation of activities. The model is an error component discrete choice model, d...
Article
Monte Carlo methods have extensively been used and studied in the area of stochastic programming. Their convergence properties typically consider global minimizers or first-order critical points of the sample average approximation (SAA) problems and minimizers of the true problem, and show that the former converge to the latter for increasing sampl...
Article
This paper presents the application of a new algorithm for maximizing the simulated likelihood functions appearing in the estimation of mixed multinomial logit (MMNL) models. The method uses Monte Carlo sampling to produce the approximate likelihood function and dynamically adapts the number of draws on the basis of statistical estimators of the si...
Article
Full-text available
Researchers and analysts are increasingly using mixed logit models for estimating responses to forecast demand and to determine the factors that affect individual choices. However the numerical cost associated to their evaluation can be prohibitive, the inherent probability choices being represented by multidimensional integrals. This cost remains...
Article
Full-text available
The recent growth of interest in activity-based methods has focused particular attention on travellers’ decision making regarding the timing and duration of their participation in activities. However, to date these two dimensions of activity participation have been largely treated separately. It is clear, however, that in general, the benefit that...
Article
Full-text available
The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi-Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm line-search approach and trust region m...
Article
Full-text available
We consider stochastic nonlinear programs, restricting ourself to differentiable, but possibly non-convex, problems. The non-convexity leads us to consider non-linear approaches, designed to find second-order critical solutions. We focus here on the use of trust-region approaches when solving a sample average approximation, and adapt the technique...
Article
The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi–Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm line-search approach and trust region m...
Article
Full-text available
Researchers and analysts are increasingly using mixed logit models for estimating responses to forecast demand and to determine the factors that affect individual choices. These models are interesting in that they allow for taste variations between individuals and they do not exhibit the independent of irrelevant alternatives property. However the...
Article
Full-text available
The estimation of random parameters by means of mixed logit models is becoming current practice amongst discrete choice analysts, one of the most straightforward applications being the derivation of willingness to pay distribution over a heterogeneous population. In many practical cases, parametric distributions are a priori specified and the param...
Article
Full-text available
Mixed Multinomial Logit Models (MMNL) are now a popular and efficient framework in discrete choice theory. However, it is well known that the numerical cost associated to the evaluation of multidimensional integrals in MMNL models remains high even if Monte Carlo (MC) or quasi-Monte Carlo (QMC) techniques are used instead of classical quadrature me...

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