Preprint
Dynamic Pickup-and-Delivery for Collaborative Platforms with Time-Dependent Travel and Crowdshipping
ArXiv.org
Cornell University
08/14/2024
DOI: 10.48550/arxiv.2408.07450
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 guided by a
parameterized cost function approximation that accounts for the remaining
temporal capacity of delivery vehicles. Through a set of computational
experiments, we demonstrate the improvement of our approach over a myopic
approach in several key performance metrics. In addition, we conduct
computational experiments demonstrate the impact of inserting wait time in the
route scheduling and the benefit of explicitly modeling time-dependent travel
times. Through our computational testing, we also investigate the effect of
demand management mechanisms that facilitate many-to-one request bundles or
one-to-many request bundles on reducing the cost to service requests.
Details
- Title: Subtitle
- Dynamic Pickup-and-Delivery for Collaborative Platforms with Time-Dependent Travel and Crowdshipping
- Creators
- Sara StoiaDemetrio LaganàJeffrey W Ohlmann
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2408.07450
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 08/14/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984697042502771
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