A Decision Support Framework for Integrated Lane Identification and Long-Term Backhaul Collaboration Using Spatial Analytics and Optimization

The movement of empty trucks (dead-heading) incurs significant costs and creates greenhouse gas emissions and road congestion. A strategy to tackle the dead-heading problem is to identify long-term backhaul collaboration opportunities, in which shippers and carriers generate frequent movement patterns (lanes) from historical truck movements in stage one, and then use the identified lanes as inputs in stage two for an optimization problem to create load-sharing contracts that eliminate empty backhauls. Where existing research has treated these as separate stages, we present an end-to-end integrated decision-making framework to connect these two stages and show that an integrated system would improve performance across diverse measures through improved lane generation and optimization. Our research offers a new design framework for backhaul collaboration that integrates spatial analytics methods into an optimization model, with improvements in financial, environmental, and social benefits. We use fine-grained GPS telematics data collected from two large logistics companies and evaluate potential lanes using spatial analytics techniques. Our framework delivers up to 75% potential improvements compared to a standard approach commonly used in practice.