Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones
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Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones Junjun Li1 • Bowei Xu2 • Yongsheng Yang2 • Huafeng Wu1
Springer Nature B.V. 2018
Abstract 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. Keywords Quantum ant colony optimization Automated guided vehicles (AGVs) path planning Bloch coordinates of pheromones Repulsion factor
1 Introduction Automated guided vehicles (AGVs) in automated logistic systems, such as intelligent warehouses, automated container terminals and distribution centers, perform transportation tasks to support the handling operations (He´ctor et al. 2014; Fazlollahtabar and Saidi-Mehrabad 2015; Vivaldinni et al. 2016; Leng et al. 2017). In general, AGVs moving simultaneously on a road-type network are vulnerable to possible AGV conflicts, such as collision, congestion, or deadlocks, which in turn increase the complexity of AGVs path planning, affect the automated logistic system throughput, and incur high costs (Evers and Koppers 1996; Park et al. 2009; Gigliotta et al. 2014; SaidiMehrabad et al. 2015; Roy et al. 2016). In such conditions, & Bowei Xu [email protected] 1
Merchant Marine College, Shanghai Maritime University, Shanghai, China
2
Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, China
applying an appropriate AGVs conflict-free path planning approach will control the conflicts among them and keep the automated logistic system working at a superior performance level (Nishi et al. 2011; Xin et al. 2015; Miyamoto and Inoue 2016). Therefore, AGVs path planning becomes a challenge for logistics managers (Kim et al. 2006; Nacera et al. 2016; Yang et al. 2017; Fanti et al. 2018). Smolic-Rocak et al. (2010) presented a dynamic routing method with time windows in vector form to solve the shortest path problem of AGVs dynamically. Shao et al. (2014) proposed a traffic control model based on semaphores to res
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