자료유형  

 학위논문

Control Number  

0016931626

International Standard Book Number  

9798379704988

Dewey Decimal Classification Number  

519.4

Main Entry-Personal Name  

Akturk, Tahsin Deniz.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of Chicago., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(158 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Candogan, Ozan;Gupta, Varun.

Dissertation Note  

Thesis (Ph.D.)--The University of Chicago, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약The dissertation mainly focuses on the spatial imbalance of resources observed in the sharing economy. It consists of two main chapters, with the first chapter focusing on designing policies to solve this imbalance and the second chapter focusing on evaluating the performance of easy-to-implement policies on solving this imbalance of resources. In the third chapter, we discuss possible extensions and other applications for the methodology we provided. In the first chapter, we consider the problem of managing resources in shared micro-mobility systems (bike-sharing and scooter-sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model: each period begins with an initial inventory at each node in the network, and then customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov Decision Process and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean-field-based policy to state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data.The second chapter considers the joint optimization of rebalancing/sourcing inventory on a graph. We focus on the lost-sales setting with customer-induced relocations. Through a coupling analysis, we provide worst-case performance bounds, with tight instances, for policies commonly used in practice. We provide further insights into the performance of these policies and discuss cost regimes where they are effective.

Index Term-Uncontrolled  

Dynamic programming

Index Term-Uncontrolled  

Inventory management

Index Term-Uncontrolled  

Micromobility systems

Index Term-Uncontrolled  

Reusable resources

Index Term-Uncontrolled  

Sharing economy

Index Term-Uncontrolled  

Stochastic processes

Added Entry-Corporate Name  

The University of Chicago Business

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642825