Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

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

Authors

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

Journal

The Journal is awaiting validation by the Administrative Services.

Title: EURO JOURNAL ON TRANSPORTATION AND LOGISTICS

Vol. 12

ISSN: 2192-4376

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13