The Experts below are selected from a list of 17337 Experts worldwide ranked by ideXlab platform

Philip Kaminsky - One of the best experts on this subject based on the ideXlab platform.

Huseyin Topaloglu - One of the best experts on this subject based on the ideXlab platform.

  • assortment optimization under the Multinomial Logit Model with sequential offerings
    Informs Journal on Computing, 2020
    Co-Authors: Nan Liu, Huseyin Topaloglu
    Abstract:

    We consider assortment optimization problems, where the choice process of a customer takes place in multiple stages. There is a finite number of stages. In each stage, we offer an assortment of pro...

  • assortment optimization under the Multinomial Logit Model with product synergies
    Operations Research Letters, 2019
    Co-Authors: Huseyin Topaloglu
    Abstract:

    Abstract In synergistic assortment optimization, a product’s attractiveness changes as a function of which other products are offered. We represent synergy structure graphically. Vertices denote products. An edge denotes synergy between two products, which increases their attractiveness when both are offered. Finding an assortment to maximize retailer’s expected profit is NP-hard in general. We present efficient algorithms when the graph is a path, a tree, or has low treewidth. We give a linear program to recover the optimal assortment for paths.

  • technical note capacitated assortment optimization under the Multinomial Logit Model with nested consideration sets
    Operations Research, 2017
    Co-Authors: Jacob Feldman, Huseyin Topaloglu
    Abstract:

    We study capacitated assortment problems when customers choose under the Multinomial Logit Model with nested consideration sets. In this choice Model, there are multiple customer types, and a customer of a particular type is interested in purchasing only a particular subset of products. We use the term consideration set to refer to the subset of products that a customer of a particular type is interested in purchasing. The consideration sets of customers of different types are nested in the sense that the consideration set of one customer type is included in the consideration set of another. The choice process for customers of different types is governed by the same Multinomial Logit Model except for the fact that customers of different types have different consideration sets. Each product, if offered to customers, occupies a certain amount of space. The sale of each product generates a certain amount of revenue. Given that customers choose from among the offered products according to the Multinomial logi...

  • assortment optimization under the Multinomial Logit Model with random choice parameters
    Production and Operations Management, 2014
    Co-Authors: Paat Rusmevichientong, David B Shmoys, Chaoxu Tong, Huseyin Topaloglu
    Abstract:

    We consider assortment optimization problems under the Multinomial Logit Model, where the parameters of the choice Model are random. The randomness in the choice Model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This choice Model is also called the mixture-of-Logits Model. The goal of the firm is to choose an assortment of products to offer that maximizes the expected revenue per customer, across all customer segments. We establish that the problem is NP complete even when there are just two customer segments. Motivated by this complexity result, we focus on assortments consisting of products with the highest revenues, which we refer to as revenue-ordered assortments. We identify specially structured cases of the problem where revenue-ordered assortments are optimal. When the randomness in the choice Model parameters does not follow a special structure, we derive tight approximation guarantees for revenue-ordered assortments. We extend our Model to the multi-period capacity allocation problem, and prove that, when restricted to the revenue-ordered assortments, the mixture-of-Logits Model possesses the nesting-by-fare-order property. This result implies that revenue-ordered assortments can be incorporated into existing revenue management systems through nested protection levels. Numerical experiments show that revenue-ordered assortments perform remarkably well, generally yielding profits that are within a fraction of a percent of the optimal.

  • joint stocking and product offer decisions under the Multinomial Logit Model
    Production and Operations Management, 2013
    Co-Authors: Huseyin Topaloglu
    Abstract:

    This article studies a joint stocking and product offer problem. We have access to a number of products to satisfy the demand over a finite selling horizon. Given that customers choose among the set of offered products according to the Multinomial Logit Model, we need to decide which sets of products to offer over the selling horizon and how many units of each product to stock so as to maximize the expected profit. We formulate the problem as a nonlinear program, where the decision variables correspond to the stocking quantity for each product and the duration of time that each set of products is offered. This nonlinear program is intractable due to its large number of decision variables and its nonseparable and nonconcave objective function. We use the structure of the Multinomial Logit Model to formulate an equivalent nonlinear program, where the number of decision variables is manageable and the objective function is separable. Exploiting separability, we solve the equivalent nonlinear program through a dynamic program with a two dimensional and continuous state variable. As the solution of the dynamic program requires discretizing the state variable, we study other approximate solution methods. Our equivalent nonlinear program and approximate solution methods yield insights for good offer sets.

Alper şen - One of the best experts on this subject based on the ideXlab platform.

Fred L Mannering - One of the best experts on this subject based on the ideXlab platform.

  • markov switching Multinomial Logit Model an application to accident injury severities
    Accident Analysis & Prevention, 2009
    Co-Authors: Nataliya V Malyshkina, Fred L Mannering
    Abstract:

    In this study, two-state Markov switching Multinomial Logit Models are proposed for statistical Modeling of accident-injury severities. These Models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate Multinomial Logit processes. To demonstrate the applicability of the approach, two-state Markov switching Multinomial Logit Models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for Model estimation. The estimated Markov switching Models result in a superior statistical fit relative to the standard (single-state) Multinomial Logit Models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

  • markov switching Multinomial Logit Model an application to accident injury severities
    arXiv: Applications, 2008
    Co-Authors: Nataliya V Malyshkina, Fred L Mannering
    Abstract:

    In this study, two-state Markov switching Multinomial Logit Models are proposed for statistical Modeling of accident injury severities. These Models assume Markov switching in time between two unobserved states of roadway safety. The states are distinct, in the sense that in different states accident severity outcomes are generated by separate Multinomial Logit processes. To demonstrate the applicability of the approach presented herein, two-state Markov switching Multinomial Logit Models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time interval. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for Model estimation. The estimated Markov switching Models result in a superior statistical fit relative to the standard (single-state) Multinomial Logit Models. It is found that the more frequent state of roadway safety is correlated with better weather conditions. The less frequent state is found to be correlated with adverse weather conditions.

David A Hensher - One of the best experts on this subject based on the ideXlab platform.

  • revealing additional dimensions of preference heterogeneity in a latent class mixed Multinomial Logit Model
    Applied Economics, 2013
    Co-Authors: William H Greene, David A Hensher
    Abstract:

    Latent class Models offer an alternative perspective to the popular mixed Logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed, although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class Model is a random parameter latent class Model which allows for another layer of preference heterogeneity within each class. This article sets out the random parameter latent class Model and illustrates its applications using a stated choice data set on alternative freight distribution attribute packages pivoted around a recent trip in Australia.

  • the generalised Multinomial Logit Model misinterpreting scale and preference heterogeneity in discrete choice Models or untangling the un untanglable
    Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013
    Co-Authors: John M Rose, William H Greene, Stephane Hess, David A Hensher
    Abstract:

    Recently published papers dealing with issues of scale and preference heterogeneity are having a significant impact on the choice Modeling community. In particular, the generalized Multinomial Logit Model (GMNL) is now being widely promoted as the Model of choice in many discipline areas given its purported ability to separately identify scale and preference heterogeneity. The purpose of this paper is to firstly discuss a number of issues related to the estimation of the GMNL Model. The second objective of the paper is to argue that the GMNL Model does not in fact untangle scale and preference heterogeneity as has been reported and that the outputs derived from the Model have been misinterpreted. The authors further argue that the Model is not a generalized version of the mixed Multinomial Logit Model, but in fact is a mixed Multinomial Logit with more flexible, but still restrictive, mixtures of distributions that do not necessarily equate to an ability to capture scale heterogeneity. They finally discuss the only theoretical circumstance under which random scale and preference can be separately identified within the Logit family of Models.

  • revealing additional dimensions of preference heterogeneity in a latent class mixed Multinomial Logit Model
    Social Science Research Network, 2010
    Co-Authors: William H Greene, David A Hensher
    Abstract:

    Latent class Models offer an alternative perspective to the popular mixed Logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed (i.e., fixed parameters), although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class Model is a random parameter latent class Model which allows for another layer of preference heterogeneity within each class. This paper sets out the random parameter latent class Model, building on the fixed parameter latent class Model, and illustrates its applications using a stated choice data set on alternative freight distribution attribute packages pivoted around a recent trip in Australia.

  • revealing additional dimensions of preference heterogeneity in a latent class mixed Multinomial Logit Model
    Research Papers in Economics, 2010
    Co-Authors: William H Greene, David A Hensher
    Abstract:

    Latent class Models offer an alternative perspective to the popular mixed Logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed, although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class Model is a random parameter latent class Model which allows for another layer of preference heterogeneity within each class. This article sets out the random parameter latent class Model and illustrates its applications using a stated choice data set on alternative freight distribution attribute packages pivoted around a recent trip in Australia. (This abstract was borrowed from another version of this item.)