Approximate dynamic programming for pickup and delivery problem with crowd-shipping

We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that im...

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Veröffentlicht in:Transportation research. Part B: methodological Jg. 187; S. 103027
Hauptverfasser: Mousavi, Kianoush, Bodur, Merve, Cevik, Mucahit, Roorda, Matthew J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2024
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ISSN:0191-2615
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Abstract We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an Approximate Dynamic Programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity. •A pickup and delivery crowd-shipping operation for same-day delivery is introduced.•Uncertainty in the arrival of crowd-shippers and online orders is incorporated.•An approximate dynamic programming policy for real-time matching is introduced.•Several enhancements for off-line learning of value functions are incorporated.•Numerical experiments provide managerial insights on various performance measures.
AbstractList We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an Approximate Dynamic Programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity. •A pickup and delivery crowd-shipping operation for same-day delivery is introduced.•Uncertainty in the arrival of crowd-shippers and online orders is incorporated.•An approximate dynamic programming policy for real-time matching is introduced.•Several enhancements for off-line learning of value functions are incorporated.•Numerical experiments provide managerial insights on various performance measures.
ArticleNumber 103027
Author Roorda, Matthew J.
Bodur, Merve
Cevik, Mucahit
Mousavi, Kianoush
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  givenname: Merve
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  givenname: Matthew J.
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  surname: Roorda
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  organization: Department of Civil and Mineral Engineering, University of Toronto, Canada
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crossref_primary_10_1016_j_multra_2025_100240
crossref_primary_10_1016_j_retrec_2025_101607
crossref_primary_10_1287_trsc_2024_0827
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Keywords Markov decision process
Approximate dynamic programming
Last-mile delivery
Value function approximation
Crowd-shipping
Language English
License This is an open access article under the CC BY-NC license.
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Snippet We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This...
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StartPage 103027
SubjectTerms Approximate dynamic programming
Crowd-shipping
Last-mile delivery
Markov decision process
Value function approximation
Title Approximate dynamic programming for pickup and delivery problem with crowd-shipping
URI https://dx.doi.org/10.1016/j.trb.2024.103027
Volume 187
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