Improved Multi-Dimensional Bee Colony Algorithm for Airport Freight Station Scheduling

TL;DR

This paper introduces an enhanced multi-dimensional artificial bee colony algorithm tailored for optimizing airport freight station scheduling, improving cargo handling efficiency through advanced search strategies and parallelization.

Contribution

The paper presents a novel multi-dimensional ABC algorithm with full-dimensional search and parallelization, specifically designed for complex airport freight scheduling problems.

Findings

The proposed algorithms outperform traditional ABC in convergence speed.

Enhanced algorithms achieve better balance between exploration and exploitation.

Successful application to real-world airport cargo scheduling scenarios.

Abstract

Due to the rapid increase of air cargo and postal transport volume, an efficient automated multi-dimensional warehouse with elevating transfer vehicles (ETVs) should be established and an effective scheduling strategy should be designed for improving the cargo handling efficiency. In this paper, artificial bee colony algorithm, which possesses strong global optimization ability and fewer parameters, is firstly introduced to simultaneously optimize the route of ETV and the assignment of entrances and exits. Moreover, for further improve the optimization performance of ABC, novel full-dimensional search strategy with parallelization, and random multi-dimensional search strategy are incorporated in the framework of ABC to improve the diversity of the population and the convergence speed respectively. Our proposed algorithms are evaluated on several benchmark functions, and then applied to solve the combinatorial optimization problem with multitask, multiple entrances and exits in air cargo terminal. The simulations show that the proposed algorithms can achieve much more desired performance than the traditional artificial bee colony algorithm at balancing the exploitation and exploration abilities.