Simulated Annealing Load Design for Trailer Space Utilization
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Solution Overview
Problem
Current methods for designing load and route optimization in delivery systems are inefficient, as they do not automatically generate optimal stack configurations and delivery routes, leading to suboptimal use of trailer space and increased transit times.
Innovation Solution
A computer-based system employing simulated annealing to determine stack building plans and route optimization, which minimizes the number of stacks, optimizes trailer loading, and considers constraints like stack height, temperature ranges, and driver rest times, to automatically generate load and route designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or heuristic methods are used for load and route design, then implementation simplicity is maintained, but trailer space utilization is suboptimal and transit times are increased
Solution Approach 1:
The patent replaces manual load design and route planning methods with an automated computer-based system that uses simulated annealing algorithms. This substitution transforms the mechanical process of manual optimization into an automated computational system, achieving superior trailer space utilization and route optimization while reducing reliance on human expertise and time-intensive manual processes
Solution Approach 2:
The system dynamically adjusts multiple parameters including stack configurations, trailer loading arrangements, route sequences, and delivery schedules through simulated annealing optimization. By continuously modifying these parameters to find optimal solutions, the system achieves improved space utilization and reduced transit times compared to static manual planning methods
2Loss of time
If automated simulated annealing is used to generate optimal stack configurations and routes, then trailer space utilization is improved and transit times are reduced, but computational complexity increases
Solution Approach 1:
The system performs comprehensive route and load optimization calculations in advance before delivery operations begin. By pre-computing optimal routes and stack configurations using simulated annealing, the system minimizes real-time decision-making complexity and ensures that transit time reductions are achieved through预先 optimized plans rather than real-time adjustments
Solution Approach 2:
The automated system independently performs load design and route optimization without requiring continuous human intervention. The simulated annealing algorithm autonomously explores solution spaces and converges on optimal configurations, reducing the need for complex human-computational interaction while achieving superior optimization results
3Reliability
If multiple constraints are considered in optimization, then compliance with regulatory and operational requirements is ensured, but optimization complexity increases
Solution Approach 1:
The optimization system is designed to handle multiple constraint types simultaneously including driver rest time requirements, temperature range specifications for different cargo, trailer capacity limits, and delivery window constraints. By integrating these diverse constraints into a unified optimization framework, the system ensures comprehensive compliance without requiring separate optimization processes for each constraint type
Solution Approach 2:
The simulated annealing optimization process continuously evaluates constraint compliance during the search for optimal solutions. The system provides feedback on constraint violations and adjusts candidate solutions accordingly, ensuring that final optimized routes and load configurations satisfy all regulatory and operational requirements while maintaining optimization effectiveness
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 receiving orders from physical stores for fulfillment from a distribution center, each of the orders comprising a set of items and a requested delivery date; generating a stack building plan for each of the orders using simulated annealing; obtaining routes for delivering the orders in trailers from the distribution center to the physical stores based at least in part on the stack building plan; and generating a load design for each of the routes to deliver in a trailer of the trailers a load for one or more of the orders, such that floor spot assignments for stacks for each of the one or more of the orders in the load carried by the trailer satisfy sequence-of-delivery constraints and center-of-gravity constraints. Other embodiments are disclosed.