Data-Space-Based Multiple Value Chain Collaborative Production Scheduling Optimization Decision In Manufacturing Industry

Production scheduling in manufacturing enterprises is an important link in the transformation and upgrading of modern manufacturing industries.The resolution of production scheduling problems is related to the economic benefits of the manufacturing enterprises themselves and all the entities in their industrial chains.Currently,mixed-discrete production enterprises,represented by power equipment manufacturers,rely on some degree of digital technology in their production planning and scheduling decision-making processes.However,they have not been able to accurately apply a large amount of heterogeneous data from within and outside the enterprises to achieve intelligent and lean production scheduling decision-making requirements.Therefore,against the backdrop of the gradual maturity of data science and intelligent manufacturing technologies,this paper introduces the concept of data space technology and multi-value chain collaboration into the research of production scheduling in manufacturing enterprises,aiming to enhance the scientific and lean management level of production scheduling in manufacturing enterprises.Firstly,this paper comprehensively sorts out and summarizes the research progress on production scheduling,data space technology,and multi-value chain collaboration in manufacturing enterprises.By referring to the research and development directions proposed by existing studies and the frontier,the research content of production scheduling decision-making is determined.Second,this paper introduces the definition and collaborative concept of multi-value chains,conducts theoretical research on data collection and data integration evolution technologies in data space technology,classifies and introduces various mathematical models for production scheduling problems,and conducts a detailed review of objective functions,decision variables,and multi-objective solution methods.Third,through a current situation analysis and production scheduling problem analysis of a typical power equipment manufacturing enterprise,this paper extracts two main problems that need to be solved in production scheduling decision-making:production demand forecasting and multi-objective production scheduling optimization based on multi-value chain collaboration.Fourth,a manufacturing enterprise production demand forecasting model based on data space is constructed.The necessary influencing factor data for production demand forecasting is extracted from data space.Grey relational analysis is used to complete the factor selection,and the parameters of the least squares support vector regression(LSSVM)algorithm are optimized through the pelican optimization algorithm(POA),and an example prediction analysis of the model and algorithm is conducted.Fifth,after completing the theoretical analysis and problem modeling of production scheduling optimization,a collaborative production scheduling optimization decision-making model for the supply-production-marketing value chain based on the improved HC-NSGA-III method was constructed,and the modeling and solution methods for production scheduling problems under the model were proposed.Sixth,using the production scheduling data and production demand situation generated in the actual production of the above-mentioned typical enterprise,an optimization problem instance is established.The optimization and solution algorithm proposed in the fifth chapter is used for analysis and comparison with other types of multi-objective optimization algorithms and unimproved original algorithms,and the performance of the improved algorithm proposed in this paper is verified.This paper focuses on the research of production scheduling optimization decision-making in manufacturing enterprises based on the concept of data space technology and multi-value chain collaboration.The manufacturing enterprise production demand forecasting model and the production scheduling optimization model of the supply-production-marketing value chain collaboration proposed in this paper both have good problem adaptability and algorithm performance,and have effectively supplemented the research gaps in related fields.The research content can be promoted and applied to other decision-making management fields of relevant problems in manufacturing enterprises,which can help modern manufacturing industries complete intelligent manufacturing transformation and upgrading.