A Two-Stage Allocation–Transportation Framework with Improved Holistic Swarm Optimization for Port Cargo Transportation Planning

Abstract

Due to the high density of warehouse distribution, port cargo is highly susceptible to fire, humidity, and various natural disasters. When these disasters occur, they can result in severe cargo losses. Meanwhile, transportation cost control has long been a prominent research focus in port logistics due to the enormous throughput. To address these problems, this paper proposes a two-stage allocation–transportation framework based on improved holistic swarm optimization. In the allocation stage, this paper designs a warehouse zoning strategy. Based on the distance criterion, a clustering method is employed to group spatially proximate warehouses into the same zone, and distribute the same cargo across different zones. In this way, the same cargo could be prevented from being completely destroyed in a disaster. In the transportation stage, this paper designs an improved holistic swarm optimization algorithm to plan the routes of cargo transportation. In this method, this paper integrates the greedy search algorithm, ant colony optimization, and adaptive swap/reversal operations with the holistic swarm optimization algorithm, which could enhance global search capability and significantly reduce the length of transportation routes. To validate the effectiveness of the proposed algorithm, experiments are conducted on both instances of varying scales and CVRPLIB benchmark instances, and the results are compared with several baseline methods. Specifically, compared with the best-performing baseline ACO, IHSO reduces the optimal route length by up to 2.08% on the self-generated instances and by 5.24% on the CVRPLIB benchmark instances. Moreover, all improvements are statistically significant under the Wilcoxon signed-rank test (p < 0.05), confirming that the advantage of IHSO is systematic rather than incidental. The proposed algorithm demonstrates high adaptability in solving port cargo path planning problems, delivering high-quality solutions.

The authors' abstract, as published at the source. Biomimetics, 2026 · DOI ↗