Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

TL;DR

This paper presents a calibrated agent-based model for supplier selection in freight transportation, effectively capturing regional freight flows and trade patterns to aid policy and infrastructure planning.

Contribution

It introduces a large-scale, behaviorally informed modeling framework that integrates trade, costs, and supplier ratings, calibrated to observed freight data across major U.S. regions.

Findings

High fidelity in replicating freight patterns in four U.S. metros

Consistent alignment with national shipping distance trends

Reveals spatial variations in trade and demand

Abstract

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and supplier rating model adapted from existing literature. The model's core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas, Atlanta, Chicago, Dallas-Fort Worth, and Los Angeles, the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision-making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.