Collaborative Scheduling Optimization Of Cloud And Local Production Orders Under Shared Manufacturing Mode

As a new intelligent manufacturing mode,shared manufacturing has been preliminarily applied in China.Shared manufacturing integrates the supply and demand sides of manufacturing resources and production capacity through industrial Internet and other technologies,which effectively improves the utilization rate of social idle manufacturing resources.However,in the shared manufacturing mode,manufacturing capacity supply enterprises need to coordinate the production tasks of cloud orders from the shared platform and local orders of long-term offline cooperative customers,and their production operation management is facing new challenges.Cloud orders are random and uncertain,which requires manufacturing enterprises to respond quickly to market demand and timely production and delivery.Local orders usually have certain stability and regularity,and need to complete production according to the agreed time and quantity.Therefore,how to coordinate the production scheduling of two types of orders,optimize the delivery time of orders in the cloud,and ensure the stability of local order production is an urgent production operation problem for current capacity supply enterprises.The purpose of this thesis is to study how manufacturing enterprises,as the supplier of shared capacity,coordinate and optimize the production scheduling plan of random cloud orders and local orders in production in the internal intelligent manufacturing production system under the shared manufacturing cloud platform mode,so as to simultaneously minimize the production completion time of cloud orders and minimize the disturbance of cloud orders to local order production,and provide a set of nondominated mixed flow production scheduling schemes for manufacturing enterprises participating in the shared manufacturing mode.The main research contents of this thesis include: Firstly,the production scheduling characteristics and mixed flow production mode of local orders and cloud orders of capacity supply enterprises under the shared manufacturing mode are analyzed,and the dynamic production scheduling process of internal intelligent manufacturing system in the context of random arrival of cloud orders is proposed.Secondly,for the dynamic mixed flow production model,a dynamic scheduling bi-objective mixed integer programming model for intelligent manufacturing production lines of capacity supply enterprises is constructed.Since this problem can be regarded as a multi-objective hybrid flow shop dynamic scheduling problem,it has been proved to be NP-hard.Therefore,this thesis develops a hybrid heuristic intelligent algorithm based on discrete differential evolution algorithm to solve the Pareto approximate optimal solution set of the problem.Finally,numerical experiments verify the effectiveness and computational efficiency of the scheduling model and algorithm constructed in this thesis.Starting from the challenges faced by manufacturing enterprises in production and operation management under the shared manufacturing mode,this thesis studies the collaborative production scheduling of cloud orders and local orders.The research results can not only provide better management ideas and methods for manufacturing enterprises,but also improve the participation rate of shared manufacturing and promote the development of shared manufacturing mode,which has important practical application value and reference significance.