Hybrid Solver for Multi-Stop Routing Optimization
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Solution Overview
Problem
Logistics route optimization, particularly in solving the Traveling Salesman Problem (TSP), is computationally challenging due to its NP-hard complexity, leading to inefficiencies in determining optimal routes that visit multiple nodes while minimizing costs and constraints.
Innovation Solution
A hybrid solver approach is employed, converting relational routing data tables into graphs with nodes and vertices representing origins, destinations, and route parameters. This method uses a classical optimization methodology initially and applies parametric distillation to ensure a valid solution. If the solution domain is too complex, a quantum annealer is invoked to find the optimal route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a classical computing process is used to determine the optimal route, then the solution can be found using conventional algorithms, but the computational time and complexity increase significantly for multi-stop routing problems
Solution Approach 1:
The patent combines classical computing processes with quantum annealing to create a hybrid solver. The classical component handles route optimization while the quantum annealer addresses complex computational problems, merging the strengths of both approaches to achieve faster and more accurate solutions for multi-stop routing.
Solution Approach 2:
The patent introduces a quantum annealer as an intermediary computational tool between the classical routing problem and the optimal solution. This quantum component acts as a mediator that processes complex optimization problems more efficiently than classical computers alone, reducing computational time while maintaining solution accuracy.
2Productivity
If a quantum annealer is invoked to solve complex routing problems, then computational requirements are reduced and faster solutions are achieved, but the system complexity increases
Solution Approach 1:
The patent segments the routing optimization process into distinct components: a classical computing process for route optimization and a quantum annealing process for solving complex computational problems. This segmentation allows each component to handle specific tasks optimally while working together in a coordinated hybrid system.
Solution Approach 2:
The hybrid solver is designed with multi-functionality, capable of handling both classical routing optimization problems and quantum computational problems. The system can invoke either a classical computing process or a quantum annealer depending on the problem complexity, providing universal problem-solving capability across different computational domains.
3Ease of operation
If relational routing data tables are converted into graphs with nodes and vertices, then the routing problem can be visualized and processed more effectively, but the data processing complexity increases
Solution Approach 1:
The patent transforms routing data from a tabular representation to a graph-theoretic representation, moving from a two-dimensional table structure to a multi-dimensional graph structure with nodes, edges, and attributes. This dimensional transformation enables more effective visualization and processing of routing problems by capturing spatial and relational relationships more naturally.
Data Source
AI summary
Relational routing data tables are converted into graphs comprising nodes and vertices. The nodes can include origins and destinations associated with routes, while the vertices represent route parameters. Route segments can then be mapped from the graphs. For an origin-destination input, a set of shortest parameterized paths among the route segments is identified. These shortest parameterized paths includes route segments weighted over a range of route parameters. Within a solution domain, an optimal solution can be generated by filtering the shortest parameterized path based on a set of one or more selected parameters to generate an optimal solution. A classical threshold determines whether the optimal solution is generated using a classical computing process or a quantum computing process.