Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

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

Autores

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

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: EURO JOURNAL ON TRANSPORTATION AND LOGISTICS

Vol. 12

ISSN: 2192-4376

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

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

Outras Informações

ID Authenticus: P-00X-SWJ

DOI: 10.1016/j.ejtl.2023.100105

Abstract (EN): We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample -average approximation (SAA) and distributionally robust optimization (DRO). The results show the effective-ness of the DRL approach by examining out-of-sample performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13