Research On Flexible Job Shop Scheduling Problem Uncertain Conditions

With the gradual extension of intelligent technology to all aspects of social production and life,the manufacturing industry has also accelerated the pace of industrial intelligence,and developed countries have taken manufacturing intelligence as the main goal of the next industrial upgrade.China has also comprehensively deployed and implemented the "Manufacturing Power Strategy" with intelligent manufacturing as the main direction.As an important part of smart factory,the core of intelligent scheduling is the optimization problem of workshop scheduling.The Flexible Job Shop Scheduling Problem(FJSP)under uncertain conditions is more flexible than the traditional FJSP considering the uncertain processing time,machine damage,emergency order insertion,etc.that may occur in the actual production process.It is close to the actual production situation.Therefore,the study of FJSP under uncertain conditions is of great significance in improving the intelligence of manufacturing enterprises.In this paper,the improved discrete particle swarm optimization algorithm is used to solve the FJSP under uncertain conditions,and the optimization goal is to minimize the maximum interval grey completion time to carry out research and discussion.First,according to the characteristics of FJSP with interval gray processing time,a mathematical model is established,and an improved discrete particle swarm algorithm is proposed.Secondly,the parameters in the algorithm are optimized through orthogonal experiments,and three sets of comparative experiments are designed to verify the effectiveness of the improved method proposed in this paper.The improved discrete particle swarm optimization algorithm with the optimal parameter configuration is used to solve the benchmark case,and the minimum value,average value and algorithm running time of the maximum interval gray completion time are selected to compare with the algorithms proposed in other literatures.The results show that the algorithm proposed in this paper is better than the algorithms proposed in other literatures in many cases,indicating that the improved discrete particle swarm optimization algorithm has higher solution efficiency and solution quality in the solution of the benchmark case.Finally,considering the maintenance situation of the machine in the workshop,the reliability of the machine is evaluated,and the corresponding maintenance plan is adopted according to the evaluation result,and the corresponding grey Gantt chart with the machine maintenance strategy is generated according to the optimal scheduling plan.Based on the above research results,a scheduling prototype system for dispatchers is designed and developed.The innovations of this paper are mainly divided into three aspects.1.Mathematical model level: use interval grey processing time to replace the traditional fixed processing time to establish a mathematical model,consider the machine maintenance situation,and improve the multithreshold maintenance model based on machine reliability.2.The optimization algorithm level: based on the characteristics of the mathematical model,the particle swarm algorithm is discretized,and the algorithm data structure is improved according to the characteristics of the interval grey processing time.The global random selection initialization,particle update step size adaptation and load balancing strategy are adopted to improve the solution performance of the algorithm.3.Guiding the production level: On the basis of the grey Gantt chart,combined with the machine maintenance strategy based on machine reliability,a scheduling plan with machine maintenance intervals is generated.