scholarly journals Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

2021 ◽  
Vol 9 (10) ◽  
pp. 1080
Author(s):  
Junjun Li ◽  
Jingyu Yang ◽  
Bowei Xu ◽  
Yongsheng Yang ◽  
Furong Wen ◽  
...  

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

2018 ◽  
Vol 17 (03) ◽  
pp. 375-390 ◽  
Author(s):  
Fuqiang Zhang ◽  
Jingjing Li

To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.


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