Research On Energy-efficient Scheduling Of Multi-load AGV In Flexible Manufacturing Workshop

Flexible manufacturing is an important field of intelligent manufacturing and the direction of sustainable development of the manufacturing industry in the future.In the flexible manufacturing workshop represented by mechanical and electronic manufacturing,Automated Guided Vehicle(AGV)has been widely used with the advantages of high automation,flexible change of transport path and good safety.It is one of the key pieces of equipment to realize intelligent logistics in the workshop.In the flexible manufacturing workshop,multi-load AGV is gradually widely used because of its higher single-vehicle transportation capacity and stronger distribution flexibility.With the proposal of China’s "carbon peak and carbon neutrality " strategy,how to respond to the national call for energy conservation and emission reduction and reduce energy consumption in flexible manufacturing workshops is a problem that the manufacturing industry should pay attention to.At the same time,the energy-efficient of AGV logistics transportation in the flexible manufacturing workshop is very important to the energy-efficient of the whole workshop.Therefore,this paper studies the energy-efficient scheduling problem of multi-load AGV in flexible manufacturing workshops.The main contents are as follows:(1)Research on energy-efficient scheduling of single multi-load AGV.Aiming at the energy-efficient scheduling problem of a single multi-load AGV in the flexible manufacturing workshop,the material handling environment of the flexible manufacturing workshop is modeled by using the topological graph method,and the energy-efficient scheduling model of multi-load AGV is established with energy consumption and handling distance as the optimization objectives.An energy-efficient scheduling method for multi-load AGV based on the integration of modified Dijkstra algorithm and NSGA-II(IMDAN)is proposed,which achieved task loading and unloading node sorting optimization and AGV path optimization,thus obtaining a multi-objective optimization AGV scheduling plan for total energy consumption and transportation distance.The actual case of a flexible manufacturing workshop is analyzed to verify the energy-efficient effect of the proposed scheduling model and the effectiveness of the scheduling method.(2)Research on energy-efficient scheduling of multi-load AGVs.Aiming at the energy-efficient scheduling problem of multi-load AGV in flexible manufacturing workshop,the energy-efficient scheduling problem of multi-load AGV in flexible manufacturing workshop is described,and the conflict types between workshop environment information and AGV are analyzed.A three-stage intelligent optimization method is proposed to solve the energy-efficient scheduling problem of multi-load AGV.In the first stage,KMeans clustering and Nearest Distance(KMND)are used to assign tasks in the workshop.In the second stage,the IMDAN method is used to sort and optimize the task loading and unloading nodes assigned by each AGV to obtain the corresponding handling path of each AGV.In the third stage,the Deep Q-learning Network(DQN)algorithm is used to adjust the initial handling path of each AGV,to solve the conflict between AGVs.The actual case of a flexible manufacturing workshop is analyzed to verify the effectiveness of the method.(3)Multi-load AGV energy-efficient scheduling prototype system development.Based on the results of the previous two chapters,a multi-load AGV energy-efficient scheduling prototype system is developed by using Python language and MATLAB language under the Py Qt5 platform,which provides technical support for multi-load AGV energy-efficient scheduling in the flexible manufacturing workshop.The above research results can effectively improve the handling efficiency of AGV in the flexible manufacturing workshop and have important guiding significance for the improvement of workshop operation efficiency.