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David Simchi-Levi. Owen, Zachary Davis Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2018-11-28T15:25:53Z 2018-11-28T15:25:53Z 2018 2018 http://hdl.handle.net/1721.1/119283 1065541545 Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 183-186). Many problems in revenue management and operations management more generally can be framed as problems of resource allocation. This thesis focuses on developing policies and guarantees for resource allocation problems with reusable resources and on learning models for personalized resource allocation. First, we address the problem of pricing and assortment optimization for reusable resources under time-homogeneous demand. We demonstrate that a simple randomized policy achieves at least one half of the optimal revenue in both the pricing and assortment settings. Further, when prices are fixed a priori, we develop a method to compute the optimal randomized state-independent assortment policy. The performance of our policies is evaluated in numerical experiments based on arrival rate and parking time data from a municipal parking system. Though our algorithms perform well, our computational results suggest that dynamic pricing strategies are of limited value in the face of a consistent demand stream. Motivated in part by the computational results of the previous section, in the second section, we consider the problem of pricing and assortment optimization for reusable resource under time-varying demand. We develop a time-discretization strategy that yields a constant-factor performance guarantee relative to the optimal policy continuous-time policy. Additionally, we develop heuristic methods that implement a bid-price strategy between available resources based on pre-computed statistics that is computable in real-time. These methods effectively account for the future value of resources that in turn depend on the future patterns of demand. We validate our methods on arrival patterns derived from real arrival rate patterns in a parking context. In the third part, we consider the problem of learning contextual pricing policies more generally. We propose a framework for making personalized pricing decisions based on a multinomial logit model with features based on both customer attributes, item attributes, and their interactions. We demonstrate that our modeling procedure is coherent and in the well specified setting we demonstrate finite sample bounds on the performance of our strategy based on the size of the training data. by Zachary Davis Owen. Ph.D. 186 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Revenue management and learning in systems of reusable resources Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a60fe8b5-b4b0-44c1-80e9-3d49beb1d870"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Revenue management and learning in systems of reusable resources</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Owen, Zachary Davis</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>Many problems in revenue management and operations management more generally can be framed as problems of resource allocation. This thesis focuses on developing policies and guarantees for resource allocation problems with reusable resources and on learning models for personalized resource allocation. First, we address the problem of pricing and assortment optimization for reusable resources under time-homogeneous demand. We demonstrate that a simple randomized policy achieves at least one half of the optimal revenue in both the pricing and assortment settings. Further, when prices are fixed a priori, we develop a method to compute the optimal randomized state-independent assortment policy. The performance of our policies is evaluated in numerical experiments based on arrival rate and parking time data from a municipal parking system. Though our algorithms perform well, our computational results suggest that dynamic pricing strategies are of limited value in the face of a consistent demand stream. Motivated in part by the computational results of the previous section, in the second section, we consider the problem of pricing and assortment optimization for reusable resource under time-varying demand. We develop a time-discretization strategy that yields a constant-factor performance guarantee relative to the optimal policy continuous-time policy. Additionally, we develop heuristic methods that implement a bid-price strategy between available resources based on pre-computed statistics that is computable in real-time. These methods effectively account for the future value of resources that in turn depend on the future patterns of demand. We validate our methods on arrival patterns derived from real arrival rate patterns in a parking context. In the third part, we consider the problem of learning contextual pricing policies more generally. We propose a framework for making personalized pricing decisions based on a multinomial logit model with features based on both customer attributes, item attributes, and their interactions. We demonstrate that our modeling procedure is coherent and in the well specified setting we demonstrate finite sample bounds on the performance of our strategy based on the size of the training data.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>