Multimodal Load Selection with Parallel Facility Capacity Optimization
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Solution Overview
Problem
Conventional freight planning systems struggle to efficiently manage load selection in multi-modal transportation networks with facility capacity constraints, limiting scalability and requiring extensive user involvement to adapt to changing scenarios.
Innovation Solution
A modularized algorithm scheme using a central coordinating engine, load generation, and mixed integer programming to split, consolidate, and optimize loads across multiple transportation modes while adhering to facility capacity limits, enabling parallel computing and distributed cloud deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional freight planning systems are used for load selection in multi-modal transportation networks, then facility capacity constraints can be managed, but scalability is limited and extensive user involvement is required
Solution Approach 1:
The system segments the load selection problem into discrete candidate loads with specific attributes (pickup windows, delivery windows, facility requirements). Each candidate load is evaluated independently against facility capacity constraints, allowing the system to handle complex multi-modal transportation networks through modular, scalable processing rather than requiring comprehensive manual planning.
Solution Approach 2:
The system dynamically adjusts parameters such as pickup windows, delivery windows, and facility capacity allocations based on changing scenarios. By treating these as variable parameters rather than fixed constraints, the system adapts to different transportation modes, time windows, and facility capabilities automatically, reducing the need for extensive user involvement while maintaining versatility.
2Productivity
If load selection is performed with facility capacity constraints, then transportation costs can be minimized, but processing time increases due to sequential evaluation
Solution Approach 1:
The system performs preliminary actions by pre-defining candidate loads with their attributes (pickup windows, delivery windows, facility requirements) before the actual load selection process. This pre-processing allows the optimization algorithm to work with structured data, significantly improving processing efficiency while maintaining the ability to evaluate facility capacity constraints for each candidate load systematically.
3Loss of energy
If multiple candidate loads are evaluated to optimize facility utilization, then transportation costs decrease, but the complexity of managing load configurations increases
Solution Approach 1:
The system manages load configuration complexity by treating pickup windows, delivery windows, and facility capacities as adjustable parameters. The optimization process automatically adjusts these parameters to find configurations that minimize transportation costs while satisfying facility constraints, eliminating the need for manual management of complex load configurations.
Data Source
AI summary
A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: splitting a set of candidate loads into subsets of candidate loads, wherein time windows of the subsets of candidate loads align with time ranges of a facility; iteratively reducing, without integer constraints, the subsets of candidate loads into a number of candidate loads; determining multiple candidate loads from the number of candidate loads that optimize an objective function with the integer constraints; and selecting at least one candidate load from the multiple candidate loads corresponding to a mode of transportation. Other embodiments are described.