Research On Integrated Optimization Of Production Planning And Scheduling Based On Particle-Improved Collaborative Optimization Algorithm

There are many production factors in the process industry,which determine the final production effect.These production factors are generally managed by production decision-making activities.Planning activity and scheduling activity are important parts of production decision-making activities.Production planning is to distribute the resource of production macroscopically,while production scheduling is to plan specific activities.Although the tasks of them are different,there is a certain correlation between them,which could better describe the integration of production planning and scheduling integration if handled well.In order to solve the integration problem more efficiently,collaborative optimization algorithm can be introduced to solve the problem.Collaborative optimization algorithm could reduce the difficulty of solving the problem by decomposing the problem,but the initial point has effects on it and it could fall into the local optimal region more easily.These problems are more obvious in complex problems.Therefore,in order to deal with complex problems such as integrated optimization of production planning and scheduling,this paper will improve the collaborative optimization algorithm according to the shortcomings of it,and carry out simulation analysis.Finally,based on the improved algorithm,an improved integrated model of production planning and scheduling is established and solved.The main research contents of this article are summarized as follows:Aiming at the problems of collaborative optimization algorithm that it is easy to be affected by the initial point and fall into the local optimal region,this paper proposes a particle-improved collaborative optimization algorithm.This algorithm relaxes the system-level consistency constraints by introducing an improved relaxation factor for the collaborative optimization system level,and uses the improved particle update rule to expand the optimization range of the system-level variables in the early stage.For the discipline level of collaborative optimization,the system-level consistency constraints are coordinated by introducing the dynamic coefficients,and the prior knowledge is used to improve the solution speed of the discipline level.Finally,through multiple sets of simulation experiment data,the particle-improved collaborative optimization algorithm has a better effect in dealing with complex problems.Aiming at the problems of high solution complexity and redundant information in the integrated production planning and scheduling model,this paper proposes to use particle improved collaborative optimization algorithm to improve the integrated production planning and scheduling model.The improved integrated model decomposes the production planning model into several sub-planning models according to the quantity of products,and the sub-planning models are coupled with the improved production planning for solution.The production scheduling model is decomposed into several sub-scheduling models according to the kinds of cost,and the sub-scheduling models are coupled with the improved production scheduling for solution.Improving coordination conditions can tolerate a certain difference between planning and scheduling and reasonably deal with the problem of excessive difference.The improved integrated model is applied to the production example,and the final multiple sets of data show that the improved model could handle different demand scenarios well.