Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms

Abstract

Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving cargo handling efficiency. In this paper, as one of the most effective artificial intelligence technologies, the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters, is firstly introduced to simultaneously manage the autonomous ETVs and assign the corresponding entrances and exits. Moreover, as ABC has the disadvantage of slow convergence rate, a novel full-dimensional search strategy with parallelization (PfdABC) and a random multidimensional search strategy (RmdABC) are incorporated in the framework of ABC to increase the convergence speed. After being evaluated on benchmark functions, it is applied to solve the combinatorial optimization problem with multiple tasks and multiple entrances and exits in the terminal. The simulations show that the proposed algorithms can achieve a much more desired performance than the traditional artificial bee colony algorithm in terms of balancing the exploitation and exploration abilities, especially when dealing with the cooperative control and scheduling problems.