Stochastic Last-Mile Delivery with Crowd-Shipping and Mobile Depots

Kianoush Mousavi (), Merve Bodur () and Matthew J. Roorda ()
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Kianoush Mousavi: Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada
Merve Bodur: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Matthew J. Roorda: Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada

Transportation Science, 2022, vol. 56, issue 3, 612-630

Abstract: This paper proposes a two-tier last-mile delivery model that optimally selects mobile depot locations in advance of full information about the availability of crowd-shippers and then transfers packages to crowd-shippers for the final shipment to the customers. Uncertainty in crowd-shipper availability is incorporated by modeling the problem as a two-stage stochastic integer program. Enhanced decomposition solution algorithms including branch-and-cut and cut-and-project frameworks are developed. A risk-averse approach is compared against a risk-neutral approach by assessing conditional-value-at-risk. A detailed computational study based on the City of Toronto is conducted. The deterministic version of the model outperforms a capacitated vehicle routing problem on average by 20%. For the stochastic model, decomposition algorithms usually discover near-optimal solutions within two hours for instances up to a size of 30 mobile depot locations, 40 customers, and 120 crowd-shippers. The cut-and-project approach outperforms the branch-and-cut approach by up to 85% in the risk-averse setting in certain instances. The stochastic model provides solutions that are 3.35%–6.08% better than the deterministic model, and the improvements are magnified with increased uncertainty in crowd-shipper availability. A risk-averse approach leads the operator to send more mobile depots or postpone customer deliveries to reduce the risk of high penalties for nondelivery.

Keywords: last-mile delivery; crowd-shipping; mobile depot; two-stage stochastic integer programming; decomposition; value of stochastic solution; conditional-value-at-risk (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:56:y:2022:i:3:p:612-630

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