Automated Route Design for Distribution Center Logistics
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Solution Overview
Problem
Current systems for designing load and route optimization in delivery operations are inefficient, as they do not effectively automate the process of stacking pallets within trailers and determining optimal delivery routes, leading to suboptimal use of trailer space and increased transportation costs.
Innovation Solution
A computer-based system that uses simulated annealing and mixed integer programming to automatically design load configurations and routes, optimizing pallet stacking and trailer utilization while considering weight, temperature control, and rest constraints for drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for designing load and route optimization, then flexibility and adaptability are maintained, but efficiency and productivity are reduced
Solution Approach 1:
The patent replaces manual mechanical design processes with computer-based automated systems that use simulated annealing and mixed integer programming algorithms. The system automatically generates load designs and routes by substituting human manual planning with computational optimization, thereby dramatically improving productivity while managing complexity through software automation.
2Volume of moving object
If traditional load design methods are used, then simplicity is maintained, but trailer space utilization is suboptimal
Solution Approach 1:
The system replaces traditional manual load design methods with computer-based automated optimization using simulated annealing and mixed integer programming. This substitution enables the system to explore complex loading configurations and optimize trailer space utilization far beyond what manual methods can achieve, while the complexity is managed through algorithmic automation.
Solution Approach 2:
The patent employs simulated annealing which involves changing parameters (temperature, energy states) to explore the solution space. The algorithm transitions from high-energy random configurations to low-energy optimized configurations by systematically changing thermal parameters, enabling optimal trailer space utilization through controlled parameter evolution.
3Productivity
If automated optimization systems are implemented, then productivity and space utilization improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex optimization problem into distinct components: load design generation, route optimization, and constraint satisfaction. By dividing the overall problem into separate manageable segments that can be processed independently and then integrated, the system achieves high productivity while controlling computational complexity through modular problem decomposition.
Solution Approach 2:
The system performs preliminary actions by pre-generating feasible load designs and routes before final optimization. The simulated annealing algorithm prepares multiple candidate solutions in advance, and mixed integer programming refines them subsequently. This preliminary action approach allows the system to explore the solution space efficiently before committing to final optimized designs, managing computational complexity through staged processing.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform certain acts. The acts can include obtaining orders for fulfillment to physical stores from a distribution center. There can be one or more respective stack groups associated with each of the orders. The acts also can include generating a set of feasible route templates for delivering the orders to the physical stores. The acts additionally can include formulating a mixed integer programming formulation for an assignment of the respective stack groups associated with the orders to the set of route templates. The acts further can include using an optimization solver for the mixed integer programming formation to determine the assignment that minimizes an overall cost of delivering the orders to the physical stores from the distribution center. The acts additionally can include outputting the assignment. Other embodiments are described.