Research On Complex Job Shop Scheduling Based On Intelligent Optimization Algorith

Green job shop scheduling is an effective means of production management and operation for manufacturing enterprises to maintain their competitiveness under the current situation of energy shortage,environmental deterioration and market pressure.Through the reasonable allocation of workshop resources,the cooperative optimization of economic indicators and environmental indicators can be realized.However,in the actual green job shop operation and production process,it is full of random disturbance events of various types,so it is difficult to ensure that the original scheduling decision can still better adapt to the current highly complex and changeable working conditions.Therefore,to explore a set of green job shop scheduling method under disturbance environment is an urgent problem to be solved.This paper mainly does the following work:(1)Aiming at the complex dynamic scheduling problem in manufacturing enterprises,the brainstorming optimization algorithm was improved and applied to solve the dynamic flexible job shop scheduling problem.Firstly,a rescheduling model based on machine faults is established,and the objective function is to minimize the maximum completion time.Then,according to the characteristics of discrete problems,the idea of Genetic Algorithm and the steps of brainstorming Optimization Algorithm are integrated,and the Brain Storm Optimization-Genetic Algorithm(BSO-GA)is proposed.In the Algorithm iterative operation,The Times of discussion between groups and discussions within groups are dynamically adjusted so that the algorithm has excellent effects in both global and local search.Moreover,precedence operation crossover,reverse mutation operator and elite retention strategy are introduced.The improved algorithm can solve dynamic flexible job shop scheduling problem efficiently.Finally,the standard FJSP and dynamic Scheduling examples are simulated,and the solution of the improved algorithm is compared with other algorithms.The simulation results show that the BSO-GA algorithm has better stability and convergence ability.(2)In the flexible job-shop scheduling problem based on the research of dynamic,considering the machine in the processing and the situation of energy consumption,idle machine automatic start-stop method is put forward,build green dynamic high-dimensional multi-objective flexible job shop scheduling model,in order to minimize the total energy consumption,the maximum completion time,the machine total load and the stability of product quality as the goal,The Grey Wolf Optimization(GWO)algorithm is improved to solve the high dimensional multi-objective green dynamic flexible job-shop scheduling problem.First of all,to improve the quality of initial population,the use of reverse learning initialization population strategy,and then,according to multi-objective problem of pareto solutions is not the only and the characteristics of the gray Wolf optimization algorithm proposed multi-stage official leadership mechanism,and the introduction of POX crossover operator and reverse mutation operator,to improve the elite reserved strategy,to adapt to the multi-objective optimization algorithm.Finally,two groups of simulation experiments are designed to compare the three intelligent optimization algorithms to prove the effectiveness of the algorithm.The simulation results show that the improved algorithm has better distribution and convergence in solving the high-dimensional multi-objective green dynamic flexible job-shop scheduling problem.