ITSC 2024 Paper Abstract

Close

Paper WeBT11.4

Hou, Shixuan (Western University), Gao, Jie (TU Delft), Tang, Yili (University of Western Ontario (Western University)), Ghaddar, Bissan (Ivey Business School)

Dynamic Predictive Matching Framework for Crowd-Sourced Delivery Service

Scheduled for presentation during the Regular Session "Intelligent Logistics" (WeBT11), Wednesday, September 25, 2024, 15:30−15:50, Salon 19/20

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Keywords Intelligent Logistics, Simulation and Modeling, Travel Behavior Under ITS

Abstract

This paper studies a same-day crowd-sourced delivery setting where in-store customers deliver online orders on their way home. This environment is dynamic and uncertain, characterized by fluctuating numbers of in-store customers and online orders throughout the day, and unpredictable customer decisions to accept or reject delivery tasks. To address these challenges, we develop a two-stage event-driven dynamic matching framework. The first stage leverages short-term predictions about future arrivals of in-store customers and online orders, allowing us to postpone matching decisions for certain drivers and orders, thus optimizing immediate outcomes to maximize order satisfaction over a future time interval. In response to these initial outcomes, the second stage computes the probability of in-store customers accepting matched orders and introduces two compensation models. These models are designed to tailor compensation for each customer, aiming to minimize expected delivery costs at the current decision-making point. Experimental results demonstrate that our framework reduces delivery costs by approximately 15% compared to baseline methods, highlighting its potential to improve the efficiency of crowd-sourced delivery systems in a constantly changing market.

 

 

All Content © PaperCept, Inc.


This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2024 PaperCept, Inc.
Page generated 2024-10-14  00:54:42 PST  Terms of use