Optimizing the last-mile parcel delivery problem with crowd-shipping under demand and supply uncertainties : a two-stage stochastic approach

Abstract

Crowd-shipping has emerged as a potentially cost-effective solution to address the burgeoning demand for urban last-mile delivery services. This paper investigates the integration of partial crowd-shipping into traditional parcel delivery operations from the perspective of a delivery company. Recognizing the inherent uncertainties in such systems, we explicitly consider both crowd-shipping supply and parcel demand variability within a novel parcel delivery problem with crowd-shipping in stochastic programming (PDPCS-SP). We formulate a mixed-integer linear program (MILP) to determine the optimal delivery sched-ule that minimizes the expected total operational cost. Given the computational complexity of the problem, we develop a two-stage heuristic algorithm based on an adaptive large neighborhood search (ALNS) to efficiently solve large-scale instances. Extensive numerical experiments demonstrate the effectiveness and efficiency of the proposed PDPCS-SP model and solution approach, highlighting their potential to enhance the sustainability and economic viability of last-mile delivery operations.

Date of Award	2024
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Sisi JIAN (Supervisor)