Theoretical Research And Application Of Iterative Learning Strategies In Network Systems In Supply Chain Inventory

Advances in computer and communication technologies have propelled human civilization into the era of network intelligence,giving rise to new technologies and business models.Driven by new technologies,new models and new ideas,the supply chain is also undergoing profound and continuous changes,and its structure is characterized by network systems.These changes are also bound to impact and bring new problems and challenges to the inventory management of supply chains,etc.Thus,prompting corresponding research,which is one of the motivations for this paper.In the supply chain inventory management,inventory fluctuation and other problems have long been prominent,and how to achieve consistent and secure supply chain inventory is a key issue.Studies have been conducted to analyze the optimization and control problems of inventory systems by adopting a centralized regulation strategy.However,the centralized approach requires processing and analysis of all data,which is difficult to implement and cumbersome to solve,posing a significant obstacle to practical application.As a result,the novel and effective regulation strategies should be designed to dissect the above problems,which is another motivation of the paper.In addition,the supply chain system has a distributed network structure,so it is more meaningful to design control and optimization strategies to analyze the inventory management problem from the network system perspective.In summary,the thesis combines the network system theory,distributed coordination theory,and iterative learning strategies to focus on the network system and supply chain inventory consistency under multiple influencing factors to improve the internal coordination capability and operational efficiency of the system.The research content and results of this thesis are summarized as follows:(1)Research on the consistency of network systems and supply chain inventory under the framework of network systemsStarting from the perspective of network systems,this thesis constructs inventory systems as multi-agent system models.It then combines iterative learning methods to design control strategies,enabling the states of the agents to reach the consensus.In addition,network systems are affected by factors such as information asymmetry and logistics congestion,such as time delays in production,procurement,and inventory management,which are one of the main reasons for system instability.Time delay is one of the main reasons for system instability.The thesis further analyzes the consensus problem when the inventory node states are affected by time lag factors,and provides the strategy conditions for reaching node state consistency.The research results show that regardless of the presence of time delay,the control strategy proposed in the paper can make the supply chain inventory status consistent and achieve inventory stability.(2)Research on the consensus and security issues of the network system when information interaction process is subject to external interferenceModern supply chain inventory mostly uses a distributed warehousing model.In order to ensure the coordinated and orderly operation of warehouses distributed in various regions,information interaction between warehouses is indispensable.Information will inevitably be subject to various interferences during the interaction process,resulting in information risks and endangering the normal operation of the inventory system.The thesis further explores the consensus and security issues of distributed multi-inventory systems when the information interaction process is disturbed.Aiming at the changes in information network structure and external disturbances,the corresponding control strategies are provided by using the iterative learning methods,and we discuss the conditions for achieving the consensus or security among inventory states.The results show that the proposed control strategies can ensure the consistency or security of multiple inventory states under information interference,thereby ensuring inventory stability and improving the ability of multiple inventory systems to resist information risks.(3)Research on the optimization problem of network system and supply chain inventory based on iterative learning strategiesInventory optimization is another important problem in supply chain inventory management,which can improve efficiency and reduce management costs through rational optimization of resources.Combined with the research method of distributed optimization of network systems,we investigate the problem of supply chain inventory optimization and resource allocation.Based on an iterative learning approach,the thesis proposes optimization strategies that satisfies certain constraints,which is easy to implement,and avoids the shortage of global information required in centralized strategies.Also,it provides a new approach to solve the similar problems.The research results show that the interaction of local information can achieve both reasonable allocation of inventory resources and thus better management cost savings.According to the research content,the main innovation points of this thesis are summarized as follows:(1)For the three main problems studied in the thesis,the supply chain inventory systems are abstracted as the multi-agent system model from the network system perspective.The model can fully reflect the relationship between the distributed inventory system and its network structure and dynamic characteristics of nodes,which is more suitable for the real situation.As for the analysis method,the thesis combines theories of network system and iterative learning to carry out mathematical derivation,avoiding the drawbacks of the overly complicated operation process and conservative theoretical results of Lyapunov method,and the conclusion conditions obtained from the paper are relatively easy to verify.(2)To analyze and solve the main problems in our research,the thesis designs the corresponding control and optimization strategies based on the construction of a network system model,combined with iterative learning methods.These strategies train and correct themselves using historical data to obtain ideal regulation methods,thereby achieving the goal of regulation or optimization.Compared with the traditional centralized management and control methods,the iterative learning control and optimization strategies adopted in this thesis has better flexibility and adaptability,and can save computing resources.At the same time,it also avoids the lack of global information required in centralized strategies,has better efficiency in solving problems.And it is more suitable for modern supply chain inventory management with distributed structure.(3)Combined with the theoretical research results,the thesis proposes corresponding control and optimization measures for supply chain inventory management.To a certain extent,these measures provide novel technical support and valuable theoretical references for managers to make scientific decisions.In addition,the results of the thesis broaden the application scenarios of network system theory,iterative learning approach and other methods,which are conducive to enriching the relevant theories of supply chain inventory management.