Method And Application Of Energy-Saving Flexible Job Shop Considering Production And Transportation Coordination

The research of FJSP is not limited to the processing process itself.Since transportation is an important part of the workshop operation,enterprises need to complete the production of the workpiece at a minimum cost under the constraints of limited resources.The two parts of resources,production and transportation,must be coordinated scheduling.In the scheduling process,not only the processing order of the workpiece should be considered,but also the vehicle by which the workpiece is transported and the corresponding transportation path should be considered.In addition,energy saving scheduling and other energy-related studies have attracted more and more attention.This paper studies the energy-saving scheduling of flexible job shop under the coordination of transportation and production resources.The main research contents are as follows:(1)Considering the influence of transportation time in production,the comprehensive energy consumption model of flexible job shop was studied to further meet the constraints of processing and transportation resources.The mathematical model of flexible job shop energy saving scheduling problem under the mixed transportation mode was established with the goal of maximum completion time and total energy consumption.Design a hybrid discrete particle swarm optimization algorithm(HDPSO)to solve the problem,in view of the problem characteristics,design effective way of chromosome decoding,introducing the competitive learning mechanism,every certain algebra started using competitive learning mechanism,keep the population diversity and avoid premature algorithm,combined with three kinds of neighborhood search operator,improve the stability of the algorithm.(2)In view of the complex manufacturing environment of intelligent production workshop and considering the wide application of AGV,an energy-saving scheduling model of flexible job shop considering the coordination of AGV transportation and machine was built,and a novel multi-stage teaching and learning optimization algorithm(MSTLBO)was proposed to solve the optimization with the objectives of maximum completion time,total energy consumption and total cost.In view of the characteristics of AGV collaborative scheduling,a three-layer coding and a new decoding method are designed to avoid the use conflict of AGV,and a variety of teaching/self-learning factors and self-learning/teacher promotion strategies are designed to effectively solve discrete problems,improve the convergence speed,and balance the deep local search ability of the algorithm.(3)Taking SQ manufacturing enterprise as an example,the two scheduling models are solved by using HDPSO algorithm and MSTLBO algorithm respectively,and the job assignment of machining lathe,the job assignment of transportation equipment,and the moving order of transportation equipment are determined.Compared with other algorithms,the results show that the proposed improved algorithm is effective for flexible job shop.Finally,considering the energy-saving scheduling problem of the flexible job shop with production and transportation cooperation,the cooperative scheduling problem of production resources and transportation resources in the flexible job shop is solved.It is proved that the cooperative scheduling of transportation equipment can not only reduce the completion time,but also achieve the purpose of energy saving and emission reduction.The proposed algorithm can also effectively solve the problem.