Automatic Design of Dynamic Collaboration Strategies for Machines and Automated Guided Vehicles via Multiobjective Genetic Programming

In the flexible manufacturing system (FMS), the automated guided vehicles (AGVs) have been widely applied to the material logistics. The transporting phases of AGVs and the processing phases of machines are alternately executed and form the production flow. The two kinds of phases will both influence the completing time and cause energy consumption and are difficult to decouple. Therefore, in this paper, we focus on the dynamic collaboration problem between processing machines and AGVs (DCPMA) and establish a mul-tiobjective optimization model to minimize the makespan and the energy consumption of FMS. In order to solve DCPMA, we propose a novel genetic programming (GP) to evolve collaboration strategies. In GP, 10 status statistics related to the handling time and energy consumption are selected into GP terminal set to express the GP tree. During dynamic simulation, each collaboration strategy evaluated by GP will dynamically select the job-machine-AGV scheme combination with the highest priority calculated from the GP tree. In addition, a series of generation operators and selection operators are customized for DCPMA. Finally, the training and testing results show that the proposed GP is superior to 28 combinations of basic collaboration strategies, and has better adaptability and scalability for various scenarios.