Leader-Follower Joint Configuration Of Manufacturing Resources And Production Line Optimization Based On Inverse Optimization In The Context Of Cloud Manufacturing

With the background of mass customization strategy,the limited products provided by a single enterprise are difficult to respond to the growing personalized demand.Cloud manufacturing can make full use of the horizontal integration of manufacturing resources between distributed enterprises and the vertical integration of the mixed-model assembly line(MMAL)system within a single enterprise to achieve small batch and diversified customers’ needs.However,cloud manufacturing involves three levels of stakeholders:dynamic personalized demand,distributed manufacturing resources among enterprises and MMAL within enterprises.There are conflicts of interest in each level,which aggravates the association and game of stakeholders in the manufacturing system.At the same time,cloud manufacturing produces multi-source real-time data,and its value is often ignored.How to effectively mine these data and feed them back to the main bodies of the cloud manufacturing system to achieve multilevel manufacturing resource collaboration and production line optimization for individual needs is also a key issue that needs to be solved urgently in the context of cloud manufacturing.In response to the above problems,this paper proposes a data-driven cloud manufacturing system inverse design theory and method based on the analysis of the multi-level coupling relationship between stakeholders;modeling the horizontal integration of manufacturing resources between enterprises and the vertical integration of MMAL within enterprises under the background of cloud manufacturing;strengthening the collaboration between distributed manufacturing resources and the internal of MMAL to adapt to the dynamic changes of individual needs.The research contents of this paper mainly include the following aspects:This paper defines an idealized cloud manufacturing system architecture,analyzes the general process of manufacturing resource allocation service based on cloud platform,and carries out data resource characteristics and demand analysis.A datadriven cloud manufacturing inverse design decision-making framework is proposed,and personalized requirements are configured through the mining and learning of actual system state data.From the perspective of game theory,analyzed the coupling of multiagent,multi-objective,multi constraint and hierarchical optimization relationship in decision-making problems in engineering design field,and establishes a leaderfollower joint optimization(LFJO)model with positive and negative interaction characteristics.The LFJO decision model is introduced to improve the traditional genetic algorithm to balance the conflict between the upper model and the lower model.Aiming at the problem of the unclear relationship between personalized demand and distributed resource level coordination,based on the reverse design method of manufacturing system,a LFJO decision-making framework based on cloud platform data-driven product requirements and manufacturing resources is proposed.The product configuration with positive customer demand acts as the leader,and the lower supplier joint configuration acts as the follower,which affects the decision of product configuration.Nested genetic algorithm is used to optimize the trade-off solution of the model,and the effectiveness of the method and algorithm is verified by an example.In order to solve the problem that capacity and operation time of multiple MMAL are not equivalent when order decomposition and production scheduling are coordinated,a parallel production planning model for flexible customization is proposed.The mechanism of interaction and restriction between them is analyzed,and the LFJO model is established.According to the characteristics of the model,a nested genetic algorithm combined with Pareto front solution is proposed.An example of a bus MMAL is introduced to verify the ability of the model to represent the actual situation of the enterprise.Analyze the real-time data problem that is ignored by the fixed beat when the sequencing and balancing are coordinated,and set the extreme value of the working hours of the real-time state workstation of the MMAL as the dynamic beat.Set up a joint decision-making model with static balance and dynamic balance as the goal.An improved artificial bee colony algorithm(IABC)is proposed to solve the joint optimization of balancing and sequencing.In the case set,the effectiveness of the algorithm and the rationality of the dynamic beat are verified by the evaluation index.As for the problem that the current integration method of assembly sequence planning and assembly line balancing is limited to one product,a joint game model of assembly sequence planning and assembly line balancing for MMAL is proposed based on the analysis of their non cooperative game decision characteristics.This paper studies the hierarchical relationship between game players,and puts forward a systematic analysis and decision-making mechanism.The nested genetic algorithm is used to optimize the model.A group of classic cases are introduced to verify the effectiveness of the proposed LFJO model and algorithm.Data-based inverse driven technology captures the basic evolution rule of the manufacturing system,and realizes precise management of the dynamic adjustment of the manufacturing system,which has important practical significance for personalized customization enterprises.The joint decision-making process of the MMAL manufacturing system is a process that contains multiple pairs of contradictory problems and dynamic balancing.By studying and mastering its basic rules,a joint decision-making model of manufacturing system is established,and the evolutionary theory system of MMAL manufacturing system is developed.