Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Caroline Krejci
Abstract
Crowd logistics is a system in which an online platform connects a group of non-professional couriers (crowd/carriers), who use their under-utilized resources to offer delivery service to other individuals or businesses (senders) for a fee. While crowd logistics platforms have the potential to offer more flexible and responsive delivery services for much lower rates than traditional logistics providers, it is difficult for platforms to be successful as it is challenging to meet carriers’ and senders’ expectations. Crowd logistics has been applied in the context of food and grocery delivery, parcel pickup and drop-off services and last-mile delivery, however, it has not been leveraged to help farmers deliver their produce to geographically co-located customers in regional food supply chains. This research aims to develop methods to analyze, design and operate a successful collaborative online transportation platform for sustainable food distribution. To this end, a small-scale prototype of a crowd logistics platform for delivery of agricultural products has been designed. Given the expensive and time-consuming process to build a production-grade deployable transportation platform, preliminary efforts focused on creating an agent-based simulation model of a crowd logistics network and the efficient analysis of the effect of model input parameters on output metrics. Furthermore, due to the lack of existing methods that allow farmers take-on the role of an agent in the simulation model which enables gathering data on their behavior to validate the agent-based model, a novel networked participatory agent-based modeling framework was developed in Python.
Keywords
Crowd logistics, Regional food supply chains, Agent-based models, Participatory simulation, Machine learning
Disciplines
Industrial Engineering | Systems Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kulkarni, Preetam, "ANALYSIS OF CROWD LOGISTICS NETWORKS USING AGENT-BASED MODELS" (2025). Industrial, Manufacturing, and Systems Engineering Dissertations. 244.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/244