An Improved Ant Colony Optimization Algorithm for Automated Ready-Mixed Concrete Distribution

Abstract

The dynamic scheduling of ready-mixed concrete constitutes a critical bottleneck in construction automation. Following the design science paradigm and informed by a systematic literature review, this study develops the multistrategy ant colony optimization (MSACO) algorithm, which integrates three mechanisms: adaptive pheromone evaporation, elite ant guidance, and genetic mutation. Empirical validation based on the road network of a major Chinese city (involving four batching plants, six customer sites, and a fleet of seven fuel vehicles and five electric vehicles) demonstrates that MSACO significantly outperforms algorithms including the genetic algorithm, ant colony optimization, particle swarm optimization, and multistrategy adaptive ant colony optimization in terms of solution accuracy, convergence speed, and stability. The proposed algorithm achieves an average reduction in distribution costs of 7.06%, with advantages reaching 10.7% under highly constrained conditions ( p < 10 − 7 ). The main contributions are threefold: it proposes a triple adaptive mechanism tailored for dynamic scheduling scenarios; formulates a mathematical model incorporating plant capacity, load limits, and electric vehicle range; and provides a quantifiable basis for the digital transformation of construction logistics.