The Experts below are selected from a list of 63051 Experts worldwide ranked by ideXlab platform
Mogens Fosgerau - One of the best experts on this subject based on the ideXlab platform.
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The Inverse Product Differentiation Logit Model
2019Co-Authors: Mogens Fosgerau, Julien Monardo, André De PalmaAbstract:This paper proposes an empirical Model of inverse demand for differentiated products: the Inverse Product Differentiation Logit (IPDL) Model. The IPDL Model generalizes the commonly used nested Logit Model to allow richer substitution patterns, including complementarity. Nevertheless, the IDPL Model can be estimated by two-stage least squares using aggregate data. We apply the IDPL Model to data on ready-to-eat cereals in Chicago in 1991-1992, and find that complementarity is pervasive in this market. We then show that the IPDL Model belongs to a wider class of inverse demand Models in which products can be complements, and which is sufficiently large to encompass a large class of discrete choice demand Models. We establish invertibility for this wider class, thus extending previous results on demand inversion.
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a nested recursive Logit Model for route choice analysis
Transportation Research Part B-methodological, 2015Co-Authors: Mogens Fosgerau, Emma FrejingerAbstract:We propose a route choice Model that relaxes the independence from irrelevant alternatives property of the Logit Model by allowing scale parameters to be link specific. Similar to the recursive Logit (RL) Model proposed by Fosgerau et al. (2013), the choice of path is Modeled as a sequence of link choices and the Model does not require any sampling of choice sets. Furthermore, the Model can be consistently estimated and efficiently used for prediction.
Shlomo Bekhor - One of the best experts on this subject based on the ideXlab platform.
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stochastic user equilibrium formulation for generalized nested Logit Model
Transportation Research Record, 2001Co-Authors: Shlomo Bekhor, Joseph N PrashkerAbstract:The route choice problem is complicated in typical transportation networks because of the size of the choice set and because of the overlapping problem since many routes share links. Well-known Models like the probit and the Logit were further developed in an attempt to overcome these problems. The Logit Model has the appeal of being relatively close to the probit Model while keeping a convenient analytical closed form. However, the simple multinomial Logit Model cannot correctly represent route choice, especially with respect to the overlapping problem. Other hierarchical Logit Models can potentially overcome the overlapping problem. The recently developed generalized nested Logit (GNL) Model is found to be very suitable for route choice, as is the cross-nested Logit (CNL) Model. The inclusion of the congestion effect in the route choice problem is accounted for in stochastic user equilibrium (SUE) problems. The development of a SUE formulation for the GNL Model is presented. In addition, how to adapt the GNL Model to route choice in a way similar to that of the CNL Model is shown. An equivalent SUE formulation for the GNL Model is developed. In this way, a unified framework is presented to relate GNL-type Models, which are derived from discrete choice theory, with aggregate entropy formulations. A preliminary algorithm is developed to illustrate the potential application of the GNL formulation for real networks.
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link nested Logit Model of route choice overcoming route overlapping problem
Transportation Research Record, 1998Co-Authors: Peter Vovsha, Shlomo BekhorAbstract:A new link-nested Logit Model of route choice is presented. The Model is derived as a particular case of the generalized-extreme-value class of discrete choice Models. The Model has a flexible correlation structure that allows for overcoming the route overlapping problem. The corresponding stochastic user equilibrium is formulated in two equivalent mathematical programming forms: as a particular case of the general Sheffi formulation and as a generalization of the Logit-based Fisk formulation. A stochastic network loading procedure is proposed that obviates route enumeration. The proposed Model is then compared with alternative assignment Models by using numerical examples.
Peter Vovsha - One of the best experts on this subject based on the ideXlab platform.
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link nested Logit Model of route choice overcoming route overlapping problem
Transportation Research Record, 1998Co-Authors: Peter Vovsha, Shlomo BekhorAbstract:A new link-nested Logit Model of route choice is presented. The Model is derived as a particular case of the generalized-extreme-value class of discrete choice Models. The Model has a flexible correlation structure that allows for overcoming the route overlapping problem. The corresponding stochastic user equilibrium is formulated in two equivalent mathematical programming forms: as a particular case of the general Sheffi formulation and as a generalization of the Logit-based Fisk formulation. A stochastic network loading procedure is proposed that obviates route enumeration. The proposed Model is then compared with alternative assignment Models by using numerical examples.
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application of cross nested Logit Model to mode choice in tel aviv israel metropolitan area
Transportation Research Record, 1997Co-Authors: Peter VovshaAbstract:Currently, modal split Modeling is done mainly by means of disaggregated mode choice Models. The almost absolute dominance of multinomial and nested Logit Models over other mode choice Models among applied transportation Modelers is attributable to their theoretical soundness, to their simple and understandable analytical structure, and to the calibration procedures that have been developed. Typical urban transport systems, however, are characterized by a variety of modes including private (automobile), public transit (bus, suburban rail, light rail, and subway), and various combinations of these. Analysis reveals that the nested Logit Model based on the assumption of groupwise similarities among modes is not a suitable Modeling tool in such situations. A cross-nested Model that is derived from the generalized extreme value class and that can be thought of as a generalization of the nested Logit Model is proposed. The Model takes into account the cross similarities between different pure and combined mode...
Emma Frejinger - One of the best experts on this subject based on the ideXlab platform.
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a nested recursive Logit Model for route choice analysis
Transportation Research Part B-methodological, 2015Co-Authors: Mogens Fosgerau, Emma FrejingerAbstract:We propose a route choice Model that relaxes the independence from irrelevant alternatives property of the Logit Model by allowing scale parameters to be link specific. Similar to the recursive Logit (RL) Model proposed by Fosgerau et al. (2013), the choice of path is Modeled as a sequence of link choices and the Model does not require any sampling of choice sets. Furthermore, the Model can be consistently estimated and efficiently used for prediction.
Huseyin Topaloglu - One of the best experts on this subject based on the ideXlab platform.
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Approximation Methods for Pricing Problems Under the Nested Logit Model with Price Bounds
Informs Journal on Computing, 2015Co-Authors: W. Zachary Rayfield, Paat Rusmevichientong, Huseyin TopalogluAbstract:We consider two variants of a pricing problem under the nested Logit Model. In the first variant, the set of products offered to customers is fixed, and we want to determine the prices of the products. In the second variant, we jointly determine the set of offered products and their corresponding prices. In both variants, the price of each product has to be chosen within given upper and lower bounds specific to the product, each customer chooses among the offered products according to the nested Logit Model, and the objective is to maximize the expected revenue from each customer. We give approximation methods for both variants. For any I > 0, our approximation methods obtain a solution with an expected revenue deviating from the optimal expected revenue by no more than a factor of 1 + I. To obtain such a solution, our approximation methods solve a linear program whose size grows at rate 1/I. In addition to our approximation methods, we develop a linear program that we can use to obtain an upper bound on the optimal expected revenue. In our computational experiments, we compare the expected revenues from the solutions obtained by our approximation methods with the upper bounds on the optimal expected revenues and show that we can obtain high-quality solutions quite fast.
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the d level nested Logit Model assortment and price optimization problems
Operations Research, 2015Co-Authors: Paat Rusmevichientong, Huseyin TopalogluAbstract:We consider assortment and price optimization problems under the d -level nested Logit Model. In the assortment optimization problem, the goal is to find the revenue-maximizing assortment of products to offer, when the prices of the products are fixed. Using a novel formulation of the d -level nested Logit Model as a tree of depth d , we provide an efficient algorithm to find the optimal assortment. For a d -level nested Logit Model with n products, the algorithm runs in O ( d n log n ) time. In the price optimization problem, the goal is to find the revenue-maximizing prices for the products, when the assortment of offered products is fixed. Although the expected revenue is not concave in the product prices, we develop an iterative algorithm that generates a sequence of prices converging to a stationary point. Numerical experiments show that our method converges faster than gradient-based methods, by many orders of magnitude. In addition to providing solutions for the assortment and price optimization problems, we give support for the d -level nested Logit Model by demonstrating that it is consistent with the random utility maximization principle and equivalent to the elimination by aspects Model.
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constrained assortment optimization for the nested Logit Model
Management Science, 2014Co-Authors: Guillermo Gallego, Huseyin TopalogluAbstract:We study assortment optimization problems where customer choices are governed by the nested Logit Model and there are constraints on the set of products offered in each nest. Under the nested Logit Model, the products are organized in nests. Each product in each nest has a fixed revenue associated with it. The goal is to find a feasible set of products, i.e., a feasible assortment, to maximize the expected revenue per customer. We consider cardinality and space constraints on the offered assortment, which limit the number of products and the total space consumption of the products offered in each nest, respectively. We show that the optimal assortment under cardinality constraints can be obtained efficiently by solving a linear program. The assortment optimization problem under space constraints is NP-hard. We show how to obtain an assortment with a performance guarantee of 2 under space constraints. This assortment also provides a performance guarantee of 1/(1- (epsilon) ) when the space requirement of each product is at most a fraction (epsilon) of the space availability in each nest. Building on our results for constrained assortment optimization, we show that we can efficiently solve joint assortment optimization and pricing problems under the nested Logit Model, where we choose the assortment of products to offer to customers, as well as the prices of the offered products.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1931 . This paper was accepted by Dimitris Bertsimas, optimization.
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Assortment Optimization Under Variants of the Nested Logit Model
Operations Research, 2014Co-Authors: James M. Davis, Guillermo Gallego, Huseyin TopalogluAbstract:We study a class of assortment optimization problems where customers choose among the offered products according to the nested Logit Model. There is a fixed revenue associated with each product. The objective is to find an assortment of products to offer so as to maximize the expected revenue per customer. We show that the problem is polynomially solvable when the nest dissimilarity parameters of the choice Model are less than one and the customers always make a purchase within the selected nest. Relaxing either of these assumptions renders the problem NP-hard. To deal with the NP-hard cases, we develop parsimonious collections of candidate assortments with worst-case performance guarantees. We also formulate a convex program whose optimal objective value is an upper bound on the optimal expected revenue. Thus, we can compare the expected revenue provided by an assortment with the upper bound on the optimal expected revenue to get a feel for the optimality gap of the assortment. By using this approach, our computational experiments test the performance of the parsimonious collections of candidate assortments that we develop.
