Multi-Tier Adaptive Memory Programming and Cluster- and Job-based Relocation for Distributed On-demand Crowdshipping

TL;DR

This paper introduces a multi-tier adaptive memory programming approach for efficient on-demand crowdshipping, optimizing request assignments and crowdsourcee relocations to improve delivery efficiency in urban environments.

Contribution

It proposes a novel multi-tier adaptive memory programming method combined with a cluster- and job-based relocation strategy for crowdshipping optimization.

Findings

M-TAMP outperforms existing assignment methods.

Relocation strategies significantly enhance efficiency.

Numerical experiments validate the approach's effectiveness.

Abstract

With rapid e-commerce growth, on-demand urban delivery is having a high time especially for food, grocery, and retail, often requiring delivery in a very short amount of time after an order is placed. This imposes significant financial and operational challenges for traditional vehicle-based delivery methods. Crowdshipping, which employs ordinary people with a low pay rate and limited time availability, has emerged as an attractive alternative. This paper proposes a multi-tier adaptive memory programming (M-TAMP) to tackle on-demand assignment of requests to crowdsourcees with spatially distributed request origins and destination and crowdsourcee starting points. M-TAMP starts with multiple initial solutions constructed based on different plausible contemplations in assigning requests to crowdsourcees, and organizes solution search through waves, phases, and steps, imitating both ocean waves and human memory functioning while seeking the best solution. The assignment is further enforced by proactively relocating idle crowdsourcees, for which a computationally efficient cluster- and job-based strategy is devised. Numerical experiments demonstrate the superiority of MTAMP over a number of existing methods, and that relocation can greatly improve the efficiency of crowdsourcee-request assignment.