Research On Scheduling Problem For Flexible Job Shop With Multiple Constraints

The problem of job shop scheduling is an important problem to improve the productivity of the manufacturing industry.The problem of flexible job shop scheduling is an important branch,and also the main problem under the mode of multi-variety and small batch production.Scheduling optimization problems in practical production often face multiple optimization objectives,such as completion time,machine equipment utilization rate and equipment equilibrium index,etc.In recent years,under the environment of energy conservation and emission reduction,production energy consumption has also become one of the optimization objectives of shop scheduling.Different workshops also face different constraints,which are a major reason for production planning and production restriction,such as production preparation time constraints,production batch constraints and processing equipment constraints.In order to improve the production environment and improve production efficiency,more extensive and in-depth research on practical shop floor scheduling problem is urgently needed in manufacturing industry.In this context,combined with the practical problems of the workshop,this thesis studies the multi-objective scheduling problem of the multiconstraint flexible job shop.The main research contents are as follows:In this thesis,the multi-objective flexible job shop scheduling problem is firstly studied.The NSGA-Ⅱ algorithm is mixed with the improved spatial neighborhood movement algorithm,and the hybrid algorithm is proposed to solve the multi-objective flexible job shop problem.In the process of combining the neighborhood movement algorithm,aiming at the characteristics of flexible Job shop scheduling problem,the gradual random movement mode of neighborhood search is changed to continuous precise movement mode with TOC theory as the support,and the objective function value is continuously and stably optimized.The earliest start time of process,process floating time,initial machining period and idle period after adjustment of machine process are described mathematically in the improved spatial neighborhood movement algorithm,and the unloading position of key process is established mathematically.Three code adjustment strategies are proposed to solve the problem of illegal solution after key process unloading.The hybrid NSGA-Ⅱ algorithm and the improved precise spatial neighborhood movement algorithm were used to solve the classical example and compared with other algorithms to verify the effectiveness of the algorithm.According to the characteristics of multi-processing constraints and the goal of energy-saving production,the energy-saving scheduling problem of flexible job shop with multi-constraints was studied.Firstly,the preparation time,batch constraint and furnace resource constraint are studied and described mathematically.The scheduling model of a flexible job shop with multiple constraints was established,and the scheduling strategies were designed for two kinds of production preparation time problems,order and process batch constraints and furnace resource constraints respectively.Based on the above hybrid algorithm,the double-layer encoding strategy was changed to four-layer encoding strategy,and the machine index layer and workpiece processing category layer were added.A heuristic energy saving scheduling method was designed in the unloading position selection process.The effectiveness of the algorithm is verified by testing a random example and comparing with other algorithms.Finally,on the basis of the above algorithm research,the job shop scheduling system is developed and designed,and the system functions are displayed.The system for the workshop,the types of orders and process set up shop scheduling plan,will complete the scheduling plan,users are more likely to understand through the gantt chart to intuitive,such as order gantt chart,gantt chart and resources can be done in the system orders,calendar,resource and process maintenance,greatly enhance the work efficiency of the planners.