Machine-Learning Backhaul Attachment for Multi-Constraint Route Optimization
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Solution Overview
Problem
The challenge in truck logistics involves determining which inbound loads (backhauls) to attach to outbound delivery routes efficiently, considering factors like trailer type, stop sequence, locations, time windows, and regulatory constraints, while future delivery information is unknown.
Innovation Solution
A system utilizing machine-learning models and mixed integer programming to recommend and optimize backhaul attachments by classifying and matching inbound loads with outbound routes, taking into account historical data and real-time constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine backhaul attachments, then flexibility in handling complex constraints is maintained, but productivity and optimization efficiency deteriorate
Solution Approach 1:
The system segments the backhaul attachment determination process into distinct functional modules: a machine learning model for classifying future releases, a mixed integer programming model for optimizing backhaul assignments, and a processing system that coordinates between them. This segmentation allows each module to specialize in specific tasks while maintaining overall system manageability despite the complexity of handling multiple constraints simultaneously.
2Loss of energy
If advanced optimization models are used to reduce empty miles, then transportation cost optimization improves, but computational complexity and processing time worsen
Solution Approach 1:
The machine learning model performs preliminary classification of future releases before the mixed integer programming optimization occurs. By pre-processing and categorizing which future deliveries are likely to occur, the system reduces the search space for the optimization algorithm, enabling it to find optimal backhaul assignments faster while still achieving significant reduction in empty miles.
3Measurement precision
If multiple constraints are considered in backhaul matching, then solution accuracy improves, but computational difficulty worsens
Solution Approach 1:
The system transforms the complex multi-constraint optimization problem into a structured mathematical formulation with clearly defined parameters and objective functions. The mixed integer programming model converts qualitative constraints (trailer type compatibility, stop sequence requirements, time windows) into quantitative parameters that can be systematically processed, improving matching accuracy while making the computational problem more tractable.
Data Source
AI summary
A method including training a machine-learning model based on pairs of historical backhauls and historical releases to recommend current releases or future releases. The method also can include determining, using the machine-learning model, recommended backhaul loads to be assigned to current releases from among available backhaul loads. The method additionally can include automatically generating matches for outbound routes with the recommended backhaul loads. Other embodiments are described.