Yongsheng Yang

Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones

In this work, a novel quantum ant colony optimization algorithm for automated guided vehicles (AGVs) path planning based on Bloch coordinates of pheromones is proposed. In consideration of the difficulty in solving the AGVs path planning problem because of NP-hard computational complexity, this approach combines the advantages of quantum theory and ant colony algorithm to obtain feasible, conflict-free, and optimal paths. To expand the search space, the pheromones on paths are coded according to Bloch coordinates. To make full use of the pheromones of three-dimensional Bloch coordinates, they are chosen with certain probabilities in accordance with the paths they obtained. Repulsions among AGVs are supposed to exist to avoid conflicts. A repulsion factor is employed in the state transition rule to increase the space–time distance among AGVs as much as possible. We compare the performance of the proposed algorithm with those of the other three methods in simulation of AGVs path planning at an automated container terminal. Simulation results illustrate the superiority of the proposed algorithm.

Impact Analysis of Travel Time Uncertainty on AGV Catch-Up Conflict and the Associated Dynamic Adjustment

In automated logistics systems, travel time uncertainty can severely affect automated guided vehicle (AGV) conflict and path planning. Insight is required into how travel time uncertainty affects catch-up conflict, the main AGV conflict in one-way road networks. Under normal circumstances, the probability formula for catch-up conflict is deduced based on an analysis of AGV catch-up conflict. The vertex, monotonicity, and symmetry of catch-up conflict probabilities are developed, for symmetrical AGV travel time distribution densities. A dynamic adjustment method based on conflict probability for AGVs is designed. The probability features of catch-up conflicts and the performance of the associated dynamic adjustment are simulated and validated for AGVs at an automated container terminal. The simulation results show that the impact analysis of travel time uncertainty on AGV catch-up conflict is correct, and the dynamic adjustment is effective.

Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

An Integrated Scheduling Method for AGV Routing in Automated Container Terminals

Abstract The simultaneous scheduling of quay cranes (QCs), automated guided vehicles (AGVs), and yard cranes (YCs) in automated container terminals (ACTs) has been a critical problem. This paper proposes an integrated scheduling for handling equipment coordination and AGV routing. With the goal of minimising makespan, we set up a bi-level programming model. To solve the model, we investigate and compare the rolling horizon procedure (RHP) and Congestion Prevention Rule-based Bi-level Genetic Algorithm (CPR-BGA). It is shown that the CPR-BGA algorithm is highly effective for the integrated scheduling in ACTs. We conclude that the CPR-BGA is effective.

Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference and coupling coefficient will increase the risks of cascading failures. Cascading threshold values and exist, where cascading failures will spread to the entire network when or . When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree can improve the network invulnerability.