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Instance space analysis of the capacitated vehicle routing problem

Created by
  • Haebom

Author

Alessandra MMM Gouvea , Nuno Paulos, Eduardo Uchoa, Mari a CV Nascimento

Outline

This paper presents an Instance Space Analysis (ISA) methodology to address the problem of understanding the complex relationship between instance features and metaheuristic (MH) performance in the vehicle routing problem (CVRP). Using the dataset of DIMACS 12th Implementation Challenge, we identify 23 relevant instance features and analyze the impact of instance structure on the behavior of MH by projecting the instance space into two dimensions through PRELIM, SIFTED, and PILOT steps utilizing dimensionality reduction and machine learning techniques. The key contribution is to provide a new instance analysis method in the CVRP field by providing a projection matrix that can easily integrate new instances into the analysis.

Takeaways, Limitations

Takeaways:
CVRP provides a new perspective to understand the relationship between instance features and metaheuristic performance.
We present an efficient methodology and two-dimensional projection matrix for instance space analysis (ISA).
Provides an easy way to integrate new instances into your analytics.
We present a novel instance analysis method for CVRP research.
Limitations:
The dataset used in the analysis is limited to the DIMACS 12th Implementation Challenge, requiring further research on generalizability.
Some information loss may occur due to 2D projection.
Results may vary depending on the type and performance of the metaheuristic algorithm used. Additional analysis of various metaheuristic algorithms is required.
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