Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Silva, M (Author) Other; Joao Pedro Pedroso (Author) FCUP; Viana, A (Author) Other

Journal

Title: European Journal of Operational Research

Vol. 307

Pages: 249-265

ISSN: 0377-2217

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Scientific classification

CORDIS: Physical sciences > Mathematics > Applied mathematics > Operations research

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00X-EQT

DOI: 10.1016/j.ejor.2022.10.039

Abstract (EN): In this work, we study last-mile delivery with the option of crowd shipping. A company uses occasional drivers to complement its fleet in the activity of delivering products to its customers. We model it as a variant of the stochastic capacitated vehicle routing problem. Our approach is data-driven, where not only customer orders but also the availability of occasional drivers are uncertain. It is assumed that marginal distributions of the uncertainty vector are known, but the joint distribution is difficult to estimate. We optimize considering a worst-case joint distribution and model with a strategic planning perspective, where we calculate an optimal a priori solution before the uncertainty is revealed. A limit on the infea-sibility of the routes due to the capacity is imposed using probabilistic constraints. We propose an extended formulation for the problem using column-dependent rows and implement a branch-price-and-cut algorithm to solve it. We also develop a heuristic approximation to cope with larger instances of the problem. Through computational experiments, we analyze the solution and performance of the implemented algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17