Methods Applied To Dynamic Flexible Job Shop Scheduling Problem With Machine Breakdown

In the actual production workshop,dynamic events such as machine breakdown,random arrival of jobs,delay in delivery,and emergency order occur frequently,making the dynamic flexible job shop scheduling problem one of the research hotspots of manufacturing systems.Among them,machine breakdown is one of the most common dynamic events in the actual machining process,which has a serious impact on the efficiency and stability of dynamic flexible job shop scheduling.Therefore,aiming at dynamic flexible job shop scheduling problem with machine breakdown,we construct a multi-objective optimization model based on scheduling efficiency index and scheduling stability index,propose an adaptive dynamic rescheduling strategy,and adopt different multi-objective optimization algorithms to solve this model in this paper.The following are the main research contents:(1)The classification and characteristics of the Dynamic Flexible Job Shop Scheduling Problem(DFJSP)are analyzed in-depth.On this basis,the research status of dynamic flexible job shop with machine faults at home and abroad is summarized,and the difficulties in the current esearch on DFJSP are analyzed.In addition,combined with the multi-objective dynamic scheduling optimization method,the dynamic rescheduling strategy for machine breakdown and dynamic scheduling performance indicators are discussed in depth,and the calculation methods of each index are given.(2)In order to solve the dynamic flexible job shop scheduling problem with random disturbance of machine breakdown,firstly,a multi-objective dynamic scheduling model based on average flow time and processing energy consumption is constructed in this paper,then,according to the randomness of machine breakdown,the event-driven and cycle-driven hybrid drive rescheduling strategy is used to generate the rescheduling scheme.Secondly,a genetic algorithm(GA)and a simulated annealing algorithm(SA)are combined to design a Genetic and Simulated Annealing Algorithm(GASA)for the above model.The hybrid genetic algorithm generates a new set of individuals through the selection of the cross-variation operation of the genetic algorithm,and then performs a simulated annealing process on each individual in the population to avoid falling into local optimum.Finally,the effectiveness of the algorithm is proved by simulation of experimental examples.(3)In the actual manufacturing system,the balance for the efficiency and stability of scheduling is the key to solving the dynamic flexible job shop problem.In this paper,the delay degree is calculated by the maximum completion time and the total load of the machine,and the deviation degree of the rescheduling scheme from the initial scheduling scheme is calculated by the process deviation degree,the machine deviation degree and the load deviation degree.Thus,the delay degree as the scheduling efficiency index and the deviation degree as the stability index of the scheduling,a multi-objective optimization dynamic model based them is constructed.On this basis,the constructed multi-objective dynamic scheduling model is solved by Non-dominated Sorting Genetic Algorithm(NSGA-II).Simultaneously,aiming at machine breakdown,two dynamic rescheduling strategies,namely,transfer rescheduling strategu and full rescheduling strategy,are proposed.Experiments with different job sizes are used to compare at different breakdown time to test two rescheduling strategies.The performance is designed to derive the optimal adaptation conditions for the two rescheduling strategies.Finally,the experimental results verify the effectiveness of the algorithm and rescheduling strategy.