Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. (September 2016)

• Record Type: Journal Article
• Authors: Wang, Yuan; Zhang, Dongxiang; Liu, Qing; Shen, Fumin; Lee, Loo Hay
• Is Part Of: Transportation research. Volume 93(2016)
• Page Start: 279
• Page End: 293
• Publication Date: 2016-09
• Subjects: Crowdsourcing -- Last-mile delivery -- Minimum cost flow; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2016.06.002
• Languages: English
• ISSNs: 1366-5545

Abstract

Abstract: In urban logistics, the last-mile delivery from the warehouse to the consumer's home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.