Research On Production Planning And Scheduling Problem With Consideration Of The Deployment Of Production Line

In today's evolving industrial background,the new mode of manufacturing continually emerging.It has already caused attention widely in business and academic circles on production planning and scheduling problem.In the manufacturing mode of multi-varieties,multi-tasks and small batch,it is worth that one simple production line can't meet the processing requirement of multi-varieties.We should arrange the production line again when different kinds of products need processing.So we make a research on production planning and scheduling with the considering of impact of production line deployment.In this paper,we adopt systems engineering theory and methods and make a research on production planning and scheduling optimization with the considering of the rule of product flexibility and output flexibility in the manufacturing mode of multi-varieties and small batch.The specific studies include some parts below.(1)We have reviewed the related researching including the basic theory of production scheduling and production deployment.On the other hand,we introduce simple introduction on genetic algorithm.(2)According to the analysis of the research characteristics,we build the mathematical model of production planning and scheduling problem with the considering of the deployment of production line.The objective function is the total production cost including tardiness/lead time penalty fees and production line deployment.(3)After analyzing of the model characteristics,we introduce the algorithm frame of separate steps and integrated method.Based on optimization step by step,we separate the model into two sub problems which include production problem and production line deployment problem.We use genetic algorithm to solve the production problem and use heuristic algorithm to solve the other problem.In addition,we use integrated method to optimize the model and put the heuristic algorithm into the frame of genetic algorithm so that we introduce a hybrid genetic algorithm.At last,we test the effectiveness of the proposed algorithm by simulation test.