Dynamic Pickup-and-Delivery for Collaborative Platforms with Time-Dependent Travel and Crowdshipping
Sara Stoia, Demetrio Lagan\`a, Jeffrey W. Ohlmann

TL;DR
This paper develops a dynamic routing model for pickup-and-delivery in collaborative platforms, incorporating time-dependent travel times and uncertain crowdshipper availability, and demonstrates its effectiveness through computational experiments.
Contribution
It introduces a Markov decision process-based heuristic for dynamic scheduling that accounts for time-dependent travel and crowdshipper uncertainty, improving routing efficiency.
Findings
Heuristic outperforms myopic approaches in key metrics.
Modeling time-dependent travel times improves scheduling accuracy.
Demand management strategies reduce service costs.
Abstract
We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time dependent travel times. While dedicated vehicles are available throughout the day, the availability of crowdshippers is unknown a priori and they appear randomly for only portions of the day. With an objective of minimizing the sum of routing costs, piece-rate crowdshipper payments, and lateness charges, we model the uncertainty in request arrivals and crowdshipper appearances as a Markov decision process. To determine an action at each decision epoch, we employ a heuristic that partially destroys the existing routes and repairs them…
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Taxonomy
TopicsTransportation and Mobility Innovations · Sharing Economy and Platforms · Mobile Crowdsensing and Crowdsourcing
