Static Pricing Problems under Mixed Multinomial Logit Demand

Price differentiation is a common strategy for many transport operators. In this paper, we study a static multiproduct price optimization problem with demand given by a continuous mixed multinomial logit model. To solve this new problem, we design an efficient iterative optimization algorithm that asymptotically converges to the optimal solution. To this end, a linear optimization (LO) problem is formulated, based on the trust-region approach, to find a "good" feasible solution and approximate the problem from below. Another LO problem is designed using piecewise linear relaxations to approximate the optimization problem from above. Then, we develop a new branching method to tighten the optimality gap. Numerical experiments show the effectiveness of our method on a published, non-trivial, parking choice model

Enhancing Discrete Choice Models with Representation Learning

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.Comment: 35 pages, 12 tables, 6 figures, +11 p. Appendi

Stochastic Optimization with Adaptive Batch Size: Discrete Choice Models as a Case Study

The 2.5 quintillion bytes of data created each day brings new opportunities, but also new stimulating challenges for the discrete choice community. Opportunities because more and more new and larger data sets will undoubtedly become available in the future. Challenging because insights can only be discovered if models can be estimated, which is not simple on these large datasets. In this paper, inspired by the good practices and the intensive use of stochastic gradient methods in the ML field, we introduce the algorithm called Window Moving Average - Adaptive Batch Size (WMA-ABS) which is used to improve the efficiency of stochastic second-order methods. We present preliminary results that indicate that our algorithms outperform the standard secondorder methods, especially for large datasets. It constitutes a first step to show that stochastic algorithms can finally find their place in the optimization of Discrete Choice Models

Introduction to disaggregate demand models

Demand information is an input for a great deal of operations research models. Assumed as given in many problem instances addressed in the literature, demand data are difficult to generate. In this tutorial, we provide an introduction to disaggregate demand models that are designed to capture in detail the underlying behavioral mechanisms at the foundation of the demand

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets in the context of Discrete Choice Models. We achieve this by proposing new efficient stochastic optimization algorithms and extensively testing them alongside existing approaches. We develop these algorithms based on three main contributions: the use of a stochastic Hessian, the modification of the batch size, and a change of optimization algorithm depending on the batch size. A comprehensive experimental comparison of fifteen optimization algorithms is conducted across ten benchmark Discrete Choice Model cases. The results indicate that the HAMABS algorithm, a hybrid adaptive batch size stochastic method, is the best performing algorithm across the optimization benchmarks. This algorithm speeds up the optimization time by a factor of 23 on the largest model compared to existing algorithms used in practice. The integration of the new algorithms in Discrete Choice Models estimation software will significantly reduce the time required for model estimation and therefore enable researchers and practitioners to explore new approaches for the specification of choice models.Comment: 43 page

Enhancing discrete choice models with representation learning

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science

Posttranscriptional and posttranslational regulation of virulence in erwinia amylovora

Erwinia amylovora is the causal agent of fire blight, the most destructive bacterial disease of the Rosaceae family plants. Two virulence factors, the type III secretion system (T3SS) and the exopolysaccharide (EPS) amylovoran, are strictly required for its pathogenicity. Our previous studies have determined the role of several transcription factors in the regulation of E. amylovora virulence; however, molecular mechanisms of virulence regulation at the posttranscriptional and posttranslational levels have still remained elusive. In this dissertation, our goal was to understand new regulatory mechanisms in E. amylovora virulence. First, we characterized the molecular mechanism of Lon protease-mediated virulence regulation. Mutation of the lon gene caused the amylovoran overproduction, the increased T3SS expression and the non-motile phenotype. In the absence of Lon, abundance and stability of the HrpS/HrpA and RcsA proteins were significantly increased, and the resulting accumulation of the RcsA/RcsB proteins influenced the expression of flhD, hrpS and csrB. In addition, lon expression is under the control of the RNA-binding protein CsrA, possibly at both the transcriptional and post-transcriptional levels, suggesting a possible interplay between Lon and the Csr system. Second, we examined the role of ClpXP protease in virulence regulation and its potential interaction with Lon. Mutation in clpXP diminished the T3SS expression, amylovoran production and motility, resulting in delayed disease progress. Highly accumulated RpoS proteins were detected in the clpXP mutant, and mutation of rpoS in the clpXP mutant background restored virulence to the wild-type level. These suggest that ClpXP-dependent RpoS degradation positively affects virulence traits. In addition, lack of both ClpXP and Lon resulted in significantly reduced virulence independently of RpoS level, suggesting that ClpXP and Lon are indispensable for full virulence. Third, transcriptional regulation mechanism of the hrpS gene, encoding the essential T3SS activator, was examined. We found that the hrpS gene contains two promoters driven by HrpX/HrpY and the Rcs system, respectively. IHF also positively regulates hrpS expression through directly binding to the hrpX promoter and positively regulating hrpX/hrpY expression. Moreover, hrpX expression was down-regulated in the ppGpp-deficient mutant and the dksA mutant, but up-regulated when the wild-type strain was treated with serine hydroxamate, suggesting that ppGpp might induce hrpX/hrpY and hrpS expression. Furthermore, CsrA positively regulates hrpS expression mainly through the Rcs system. These results suggest that E. amylovora recruits multiple stimuli-sensing systems to regulate hrpS and T3SS gene expression. Fourth, we examined the global effect of CsrA and determined potential molecular mechanisms of CsrA-dependent virulence regulation in E. amylovora. Using REMSA, direct interaction between CsrA protein and csrB sRNA was confirmed, while CsrA did not bind to the transcripts of T3SS activators, hrpL and hrpS. Transcriptomic analyses under the T3SS-inducing condition revealed that mutation in csrA led to differential expression in more than 20% genes in the genome. Of these, T3SS genes and those required for cell growth and viability were significantly down-regulated, explaining the pleiotropic defects in the csrA mutant. On the other hand, the csrB mutant exhibited significant up-regulation of the major virulence genes, further suggesting antagonistic effects of csrB on CsrA. Through REMSA combined with site-directed mutagenesis and LacZ reporter gene assay, three CsrA targets (flhD, rcsB and relA) were identified that positively regulate E. amylovora virulence. Overall, this dissertation demonstrates that E. amylovora employs multiple layers of gene regulatory networks to effectively control the expression of virulence factors

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order