CS Company Machine Shop Production Scheduling Optimization Research

With the advancement of economic globalization and intelligent manufacturing,the production level of manufacturing is constantly improving.The development of hydropower in my country has gradually shifted from low flow to high flow,in which hydraulic turbine is the main carrier that converts water energy into electric energy.Hydraulic turbine manufacturing belongs to multi-variety,small-batch customized production,which needs to continuously meet the individual needs of customers,and at the same time puts forward higher requirements for the flexibility of the workshop.CS company is a large-scale enterprise specializing in the research and development and production of hydraulic turbines.Its machining workshop is mainly responsible for the manufacture of hydraulic turbine parts.It has the production characteristics of multiple product types,small production batches,and good equipment flexibility.The production scheduling of this workshop is a typical FJSP problem(Flexible Job Shop Scheduling Problem).Its essence is to rationally arrange the processing tasks and scheduling order for multiple products on multiple alternative machine tools under the conditions of multiple constraints and multiple objectives.At present,the machine shop mainly relies on manual experience for production scheduling,and there is still much room for improvement in terms of delivery time,production costs and equipment utilization.For this reason,this article studies the FJSP scheduling problem of CS company’s machine shop under the multi-variety,small batch customized production mode,which is of great significance to enhance the machine shop’s optimized scheduling ability and improve the efficiency and benefit of the enterprise.This paper first analyzes the current situation of production scheduling in CS company’s machine shop,and establishes a multi-objective scheduling optimization model with the minimum five objective functions of maximum completion time,maximum delay time,maximum load of machine operation,total load of machine operation and total cost of production and processing.Secondly,on the basis of the NSGA-II algorithm(non-dominant sorting genetic algorithm),the algorithm’s coding,crowding degree,selection,crossover,mutation and other genetic operations are improved.The classic flexible job shop production scheduling example Kacem8 × 8 is used to verify the effectiveness of the improved NSGA-II algorithm.Then take CS company machine shop production scheduling as an example,use NSGA-II improved algorithm to optimize the solution,and use Matlab to simulate the production scheduling model to obtain the Pareto optimal approximate solution set.Then use the ideal solution method(TOPSIS)to evaluate the Pareto optimal approximate solution set,determine the optimal degree of compatibility plan and draw its Gantt chart.Finally,the optimal coordination degree plan is compared with the current scheduling plan.The optimal coordination degree plan is superior to the current scheduling plan in terms of completion time,delay time,equipment utilization,and total production and processing costs.It proves that the improved NSGA-II algorithm can improve the production scheduling ability of CS company’s machine shop.