Title:
Marketplace Design for Crowdsourced Same-Day Delivery
Marketplace Design for Crowdsourced Same-Day Delivery
Author(s)
Behrendt, Adam Bernard
Advisor(s)
Wang, He
Savelsbergh, Martin
Savelsbergh, Martin
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Abstract
Crowdsourced delivery platforms operate as an intermediary between consumers who place orders and couriers who make deliveries; both of which are uncertain. The main challenge of a crowdsourced delivery platform is to meet a service level for their customers (e.g., 95% on-time delivery) by serving dynamically arriving orders with time windows. The two critical courier management decisions for a platform are how to schedule couriers and how to assign orders to couriers. These two decisions can be centralized (i.e., decided by the platform) or decentralized (i.e., decided by the couriers). Centralizing these decisions produces a more reliable workforce while decentralizing them may come with cost savings to the platform and allows for more freedom to couriers in deciding when and where to work. Crowdsourced delivery platforms have begun to utilize multiple courier types (i.e., a hybrid system) with the hope of reaping the advantages of each. In this dissertation, we address the challenge of managing two types of couriers at the strategic, tactical, and operational decision levels. We first present fluid models for fleet sizing and order pricing that establish the superiority of a hybrid system over each system individually. Next, we present the scheduling problem for centralized couriers under uncertainty and provide a prescriptive machine learning method for fast online schedule creation. Finally, we study order allocation in a hybrid system in depth and propose order pooling and splitting policies. In our online experiments we find that a look ahead splitting policy outperforms pooling and batching policies while being more robust to uncertainty.
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Date Issued
2023-04-25
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Resource Type
Text
Resource Subtype
Dissertation