Capacitated Arc Routing Optimization via Route Decomposition
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Solution Overview
Problem
Existing methods for solving large-scale capacitated arc routing problems face challenges such as high computation complexity, high computation cost, low planning efficiency, and poor stability, particularly due to the capacity constraint which increases the difficulty in finding optimal routes.
Innovation Solution
A route planning method that involves acquiring and preprocessing route information, performing global optimization using a low-cost route cutting off operator, temporarily ignoring capacity constraints to merge routes, and using a greedy strategy with randomness to insert tasks into vehicle routes while controlling vehicle returns to optimize route sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If capacity constraint is added to CARP to make it more practical, then practical value is improved, but difficulty in solving is worsened
Solution Approach 1:
The patent applies segmentation by dividing the large-scale capacitated arc routing problem into smaller sub-problems through route decomposition. The complex problem is broken down into manageable segments that can be solved independently and then integrated, reducing the overall difficulty while maintaining the capacity constraint requirement for practical applications.
2Measurement precision
If exact algorithms are used to solve CARP, then solution accuracy is improved, but scalability to large-scale problems is worsened
Solution Approach 1:
The patent segments the large-scale problem into smaller sub-problems that can be solved with exact algorithms, maintaining solution accuracy for each segment while enabling scalability to large overall problems through the divide-and-conquer approach.
Solution Approach 2:
The patent changes the scale parameter by decomposing large problems into smaller sub-problems with reduced size, allowing exact algorithms to be applied effectively to each segment while the overall system maintains scalability to handle large-scale instances.
3Speed
If heuristic algorithms are used to solve CARP, then computation speed is improved, but ability to handle large-scale problems is worsened
Solution Approach 1:
The patent segments large-scale problems into smaller sub-problems that can be efficiently handled by heuristic algorithms, maintaining both computation speed and the ability to handle large overall problems through progressive decomposition and integration.
4Measurement precision
If meta-heuristic algorithms are used to solve LSCARP, then solution quality is improved, but computation resources required are worsened
Solution Approach 1:
The patent segments the large-scale capacitated arc routing problem into smaller sub-problems, allowing meta-heuristic algorithms to be applied to each segment with reduced computation resources, while maintaining solution quality through the integration of segmented solutions.
Solution Approach 2:
The patent changes the problem size parameter by decomposing large problems into smaller sub-problems, reducing the computation resources required for meta-heuristic algorithms while preserving solution quality through systematic integration of results.
5Adaptability or versatility
If problem size increases in CARP, then practical applicability is improved, but time complexity is worsened
Solution Approach 1:
The patent segments large-scale problems into smaller sub-problems, enabling the system to handle larger practical problems while reducing time complexity by solving smaller segments independently and integrating results, avoiding the exponential time complexity of solving the entire problem at once.
Data Source
AI summary
A route planning method for large-scale capacitated arc routing problem is disclosed, belonging to the field of combinatorial optimization. The present disclosure firstly performs global optimization and proposes a low-cost decomposition optimization solution based on CARP, which pertinently preserves more excellent decompositions during iterations. And, the present disclosure is also applied to a local search stage, and proposes an improved route construction rule. In a process of route insertion, a problem of excessive useless cost caused by a vehicle with almost full load returning to a depot is considered. After improvement, local search can be carried out more effectively, thus further improving the solution quality. Compared with an existing route planning method, the present disclosure considers details and characteristics of CARP optimization problems in a more detailed manner, thus achieving solutions with a lower cost and improving the stability by about two to three times.