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Hybrid genetic algorithm for solving the inventory routing problem with small capacity-to-demand ratio demand points

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
optimization-theory-and-computation
neural-networks

Abstract

This study addresses the inventory routing problem under a single-period multi-delivery scenario for demand points with a low capacity-to-demand ratio. A combinatorial optimization model is constructed to allow multiple deliveries within the period. To cope with the increased model complexity, an Improved Hybrid Genetic Algorithm incorporating multiple strategies is designed. Numerical experiments, including ablation study, algorithm comparisons, and parameter sensitivity analysis, verify the effectiveness of the proposed model and algorithm. The results indicate that the intra-period multi-delivery mechanism can effectively accommodate demand characterized by low capacity-to-demand ratio and high timeliness requirements, while significantly increasing the problem’s computational difficulty. The proposed hybrid genetic algorithm performs excellently in terms of solution quality, convergence speed, and stability, demonstrating strong adaptability and competitive advantage across instances of varying scales. This work provides a general methodological foundation and algorithmic support for the optimization of related distribution systems.
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