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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Silva, M (Autor) Outra; Joao Pedro Pedroso (Autor) FCUP; Viana, A (Autor) Outra

Revista

Título: European Journal of Operational Research

Vol. 307

Páginas: 249-265

ISSN: 0377-2217

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências Físicas > Matemática > Matemática aplicada > Investigação operacional

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17