Research On Multi-objective Flexible Job Shop Scheduling Problem Considering Energy Consumption

With the rapid development of advanced manufacturing technology,information technology and network technology,and under the guidance of national innovation management,quality and efficiency improvement,the traditional manufacturing mode is developing towards intelligent manufacturing.Because flexible job shop scheduling can rationally allocate the existing resources in the workshop to achieve the purpose of improving equipment utilization,reducing energy consumption and cost,it has become a hot spot of concern for enterprises.Flexible Job Shop Scheduling Problem(FJSP),as an extension of the traditional Job Shop Scheduling Problem(JSP),increases the selection of jobs to machines,so it greatly improves the flexibility of scheduling and the complexity of calculation,which is in line with the production mode of modern manufacturing.This paper takes the energy consumption problem as the research core,conducts in-depth analysis and research in the complex workshop environment,establishes the corresponding mathematical model and uses relevant algorithms to solve the multiobjective problem considering energy consumption,so as to achieve the purpose of energy-saving production.Firstly,for the flexible job shop scheduling problem considering energy consumption,a mathematical model with energy consumption and maximum completion time as optimization objectives is established,and a NSGA-II-SA algorithm suitable for solving the model is proposed.The algorithm takes energy-saving production as the starting point,introduces the idle time priority method in the decoding operation of the algorithm,and designs two rules according to the energy-saving requirements to save energy without reducing efficiency.Then,the simulated annealing method is performed on the individuals in the Pareto frontier to improve the quality of the individuals.Finally,the effectiveness of the NSGA-II-SA algorithm proposed in this paper is proved by experimental simulation.Secondly,aiming at the energy consumption problem of flexible job shop scheduling considering machine adjustment,an energy-saving optimization model with maximum completion time,total setup time and total energy consumption as optimization objectives is established,and a multi-objective hybrid algorithm(MOGATS)is designed to solve the model.The algorithm designs a variety of initialization rules to ensure the diversity of the initial population,and performs simulated annealing operation in the mutation operator to improve the local search ability of the algorithm,and then finds the optimal individual of each target in the Pareto front as the initial solution for tabu search,so as to improve the quality of the solution.Finally,through experimental simulation with other algorithms,it is proved that the MOGATS algorithm can effectively solve the energy consumption problem of flexible job shop scheduling considering machine adjustment.Finally,aiming at the energy consumption problem of flexible job shop scheduling considering machine and worker double constraints,a mathematical model considering processing time,setup time,worker cost and energy consumption cost is established to optimize the maximum completion time,total setup time and total cost,and a multipopulation co-evolutionary algorithm(MPCEA)is proposed to solve the model.In the algorithm,the elite individuals of multiple populations use interaction to improve the quality of the population,and use the clustering sorting method instead of the classical crowding sorting method in the selection operator to improve the search ability of the algorithm.Finally,the simulation experiment is carried out by using the actual case,which proves that the MPCEA algorithm can effectively solve the energy consumption problem of flexible job shop scheduling considering the dual constraints of machine and worker.