Graph-Based Location Optimization for Supply Chain Trajectories
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Solution Overview
Problem
Optimizing facility locations to efficiently manage the movement and flow of objects such as people, goods, and vehicles is a computationally complex problem, similar to the traveling salesman problem, which lacks effective solutions for applications like supply chain optimization and emergency response, especially in critical situations like pandemics.
Innovation Solution
A method and system that capture location and movement data to build graphs, integrate constraints, and use reinforcement learning systems to determine desired target locations and optimal paths for objects, considering factors like social distancing, risk, and storage capacity, while dynamically adjusting constraints if optimization criteria are not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional optimization methods are used for facility location problems, then the problem can be solved with simple algorithms, but the solution quality is poor and cannot handle complex real-world scenarios like supply chain optimization and emergency response
Solution Approach 1:
The patent transforms the facility location problem into a graph optimization problem by changing the representation parameters - using graphs with nodes representing locations and edges representing trajectories, and incorporating multiple constraint parameters (social distancing, risk factors, storage capacity) to achieve high-quality solutions for complex scenarios
Solution Approach 2:
The patent introduces graph theory as an intermediary framework between the facility location problem and the optimization algorithms. By representing the problem as a graph with vertices and edges, it enables the use of advanced optimization techniques (first and second optimization systems) that can handle complex constraints and multiple objects simultaneously
2Adaptability or versatility
If multiple constraints are integrated into the optimization system to handle real-world scenarios, then the solution becomes more accurate and applicable, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the optimization process into two distinct systems: a first optimization system that determines desired target locations considering constraints like storage capacity and risk factors, and a second optimization system that selects optimal paths considering social distancing and trajectory conflicts. This segmentation allows each system to handle specific constraints independently, reducing overall complexity
Solution Approach 2:
The patent implements dynamic constraint integration where the graph structure and constraints are updated based on object movements and changing conditions. The system dynamically adjusts trajectories and target locations as objects move, allowing adaptability to real-time changes without requiring complete re-optimization
3Productivity
If the system optimizes trajectories for multiple objects simultaneously considering their movements, then the overall system efficiency improves, but the computational burden increases
Solution Approach 1:
The patent performs preliminary determination of desired target locations using the first optimization system before selecting optimal paths. By pre-determining target locations based on constraints like storage capacity and risk factors, the system reduces the search space for the second optimization system, thereby reducing computational time while maintaining overall efficiency
Solution Approach 2:
The patent merges the optimization of multiple objects into a unified graph-based framework where all objects are considered simultaneously. The system combines trajectory optimization with target location assignment in an integrated approach, allowing efficient coordination of multiple objects without requiring separate optimization for each object
Data Source
AI summary
A method and a related system for allocating target locations to optimize trajectories between several objects and the target locations may be provided. The method comprises capturing location data of the target locations as well as location and movement data of the objects, building a graph using the target locations as well as the location and movement data and integrating constraints into the graph. Furthermore, the method comprises determining for each of the several objects a desired target location using a first optimization system, thereby determining endpoints of a trajectory between each of the objects and its respective desired target location and selecting for each of the several objects an optimal path as the trajectory between the object and the desired target location, using a second optimization system, and taking into account movements of other objects along their trajectories.