Container Management System Using Bipartite Graph Solver
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Solution Overview
Problem
Container shipping companies face challenges in managing the balance between supply and demand of containers, leading to difficulties in identifying feasible pickup locations, excess returned containers, and high transportation costs due to inefficient repositioning of empty containers, especially when customer locations are not tracked.
Innovation Solution
A container management system utilizing a solver algorithm that builds bipartite graphs to assign customer locations to depot locations based on balancing and cost minimization criteria, optimizing pick-up, return, and street-turn operations by generating optimal schedules and routes that minimize costs and ensure sufficient container stock levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If container shipping companies manage container balance without tracking customer locations, then operational simplicity is maintained, but container pickup feasibility and return management become challenging
Solution Approach 1:
The patent introduces an intermediary optimization system that acts as a mediator between container supply and demand. This system processes location data, depot capacity, and container flow information to generate optimized pickup and return assignments, resolving the contradiction by adding intelligence without requiring direct tracking complexity at operational levels
Solution Approach 2:
The system performs preliminary optimization calculations before container pickup and return operations. By pre-processing location data and depot capacity information, the system determines feasible pickup locations and optimal return assignments in advance, ensuring reliability while maintaining operational simplicity during actual container movements
2Reliability
If feasible pickup locations are not identified for customers, then customer service quality deteriorates, but container repositioning costs may increase
Solution Approach 1:
The patent implements dynamic optimization that adapts to changing container supply and demand conditions. The system continuously evaluates depot capacity, container locations, and customer requirements to dynamically assign pickup locations and return destinations, ensuring service fulfillment while minimizing repositioning costs through real-time adjustments
Solution Approach 2:
The system changes key parameters including pickup location assignments, return destination selections, and depot capacity allocations based on current container flow conditions. By optimizing these parameters dynamically, the system ensures customer service requirements are met while minimizing unnecessary container repositioning and associated costs
3Quantity of substance
If returned containers exceed depot capacity, then storage efficiency is reduced, but container availability for future pickups decreases
Solution Approach 1:
The system performs preliminary assignment of return containers to specific depots based on predicted future pickup demand and current depot capacity. By pre-determining return destinations before containers actually arrive, the system prevents capacity exceedance and ensures optimal container distribution, maintaining both adequate stock levels and high availability for future pickups
Solution Approach 2:
The patent implements feedback mechanisms that monitor depot capacity utilization and container flow patterns. This feedback information is used to adjust return assignments and pickup allocations dynamically, ensuring that depot capacities are respected while maintaining sufficient container stock levels and availability for customer operations
Data Source
AI summary
In optimized container management system, a booking request with customer locations, booking time, container type, movement mode, depot locations and depot locations stock information are received as input in a solver in a container management system received in a container management system. Based on the movement mode of booking request received by customer, solver algorithm is invoked to build a bipartite graph. A bipartite graph with nodes representing customer locations and depot locations are generated, based on the movement mode. Edges between the customer locations and the depot locations are generated based on a container balancing criteria of a solver algorithm. For remaining booking request, edges between the customer locations and the depot locations are generated based on a cost minimization criteria of the solver algorithm. The depot locations are assigned to the corresponding customer locations based on satisfying the container balancing criteria and the cost minimization criteria.