Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

The automated guided vehicles dispatching is one of the important operations in containers terminal because it affects the loading/unloading process. This operation has become faster and more complex until the automation advent. Although this evolution, the environment has become dynamic and uncertain. This paper aims to propose an improved particle swarm approach for solving the bi-objective problem of automated guided vehicles dispatching and routing in a dynamic environment of containers terminal. The objectives are to minimize the total travel distance of all automated guided vehicles and maximize the workload balance between them. The application of particle swarm algorithm in its basic form, shows a premature convergence. To ameliorate this convergence, the authors proposed the application of a method to escape the worst particles from the local optimum. The new Hybrid Guided Particle Swarm approach consists of hybridization between Dijkstra algorithms and a Guided Particle Swarm Algorithm. The routing problem is solved with Dijkstra algorithm and the dispatching problem with guided particle swarm approach. As a first step, this approach has been applied in a static environment where the dispatching parameters and the routing parameters are fixed in advance. The second step consists of applying this approach in a dynamic environment where the number of containers associated with each automated guided vehicles can change, the shortest path and the container locations can also change during the algorithm execution. The numeric results in a static environment show a good Hybrid Guided Particle Swarm performance with a faster and more stable convergence, which surpasses previous approaches such as Hybrid Genetic Approach and the efficiency of its extension approach Dynamic Hybrid Guided Particle Swarm in a dynamic environment.