| With the in-depth implementation of Made in China 2025 strategy,the new generation of information technology is deeply integrated with the manufacturing industry.Intelligent manufacturing has become the key to promote the transformation and upgrading of China’s manufacturing industry and implement high-quality development.As an important topic in the field of intelligent scheduling,the core task of smart manufacturing,job shop scheduling problem determines the production scheduling scheme of discrete manufacturing enterprises,which is of great significance to the improvement of production efficiency and competitiveness of enterprises.Under the flexible manufacturing mode,the uncertainty and complexity in the production process increase dramatically,and dynamic events often appear in the production process.Modern manufacturing industries urgently need dynamic scheduling methods to effectively reduce the adverse effects caused by dynamic events.Traditional scheduling methods lack intelligence and real-time monitoring of dynamic events,and the physical space and the information space of intelligent manufacturing lack interoperability.Digital twin,as a new technology of virtual-real interaction and real-time mapping,provides a new idea to solve the challenges of job shop scheduling development.In this paper,the following research is conducted on the job shop scheduling problem of discrete manufacturing enterprises.(1)In order to form the initial scheduling scheme for production,a solving method based on the improved lion swarm optimization algorithm is designed for the static job shop scheduling problem.To address the problems of slow convergence and weak local search capability of the lion swarm optimization algorithm in solving the job shop scheduling problem,a hybrid lion swarm optimization algorithm incorporating Solis&Wets local search is proposed.In this algorithm,the lion swarm optimization algorithm is combined with the particle swarm optimization algorithm to improve the convergence speed of the algorithm,and the search results are further localized using the Solis&Wets local search algorithm to increase the search accuracy.The effectiveness and stability of the proposed improved algorithm in solving the job shop scheduling problem is verified through simulation analysis on the benchmark datasets as well as the machine shop example.(2)For the dynamic job shop scheduling problem,a dynamic scheduling integration model based on SPT scheduling rules is proposed.In this model,a rescheduling method based on SPT rules is given for three different dynamic scheduling events:urgent order insertion,workpiece priority change,and equipment failure.When a dynamic event arrives,the dynamic event type is first identified,and a new scheduling scheme is generated by selecting different rescheduling methods according to the dynamic event type.Finally,the feasibility of the proposed model is verified on a dynamic scheduling example.(3)A digital twin model of dynamic job shop based on scheduling rules is designed for the feasibility verification of static scheduling schemes and real-time rescheduling of dynamic events.To address the problems of lack of intelligence and real-time of traditional job shop scheduling,a digital twin-based dynamic job shop scheduling model is constructed by combining digital twin technology.Through the example,it is proved that the model can effectively verify the equipment utilization rate and possible machining problems of the initial scheduling plan when generating the initial scheduling plan,and give modifications on the initial scheduling plan in time.Dynamic events can be detected in real time through real-time interaction between the physical shop-floor and the virtual shop-floor.After a rescheduling solution is given by the shop-floor service system,the scheduling solution is passed back for validation and production.The example validates the effectiveness of the proposed model for dynamic job shop scheduling problems. |
PDF Full Text Request