Research On Dynamic Scheduling Of Flexible Job Shop Considering Energy Consumption And Fuzzy Delivery Time

As the pillar of the national economy,manufacturing industry is a strategic industry.Under the strategic background of green development and energy conservation,green manufacturing considering energy conservation and consumption reduction has become a new trend of manufacturing industry.In the past,people mostly focused on the problem of deterministic job shop scheduling,but in reality,due date is often ambiguous due to a variety of random factors.In addition,manufacturing enterprises will encounter machine failures in the actual production process,emergency workpiece arrival,order cancellation,shortage of raw materials and other dynamic interference events.Therefore,this paper considers energy consumption and fuzzy due date of flexible job shop dynamic scheduling optimization problem,which has good theoretical significance and practical value for job shop scheduling problem.Based on the theory of flexible job shop scheduling,dynamic scheduling and fuzzy scheduling,a rolling window rescheduling strategy driven by event and cycle is adopted to dynamically schedule flexible job shop scheduling under machine fault disturbance.Aiming at the situation that the workpiece has a fuzzy due date,ladder delivery is adopted according to the theory of fuzzy mathematics.Periodic window representation and lexicographic multi-objective programming are used to establish a multi-objective fuzzy flexible job shop dynamic scheduling model with the objective of minimum completion time,minimum energy consumption and maximum customer satisfaction.Finally,an improved adaptive immune genetic algorithm is designed to initialize the population.When initializing the population,the initialization machine,the initialization process and the random initialization are combined.At the same time,adaptive genetic parameters are introduced to adjust the probability of crossover operator and mutation operator in the genetic process adaptively.And the model is solved.The simulation results of the example are compared with the genetic algorithm to verify the effectiveness and feasibility of the algorithm.