数据驱动的复杂供应链网络多主体协作的计算实验及决策优化方法研究

Nowadays computational experiment and decision optimization for complex supply chain network collaboration has arisen as an active research topic in the field of supply chain management and decision making. Current research in this area has limitations in their methods of computational experiments and decision optimization for supply chain network collaboration. In-depth and systematic research has yet to be conducted on the related methodologies. After addressing limitations of computational experiments and decision optimization for complex supply chain network collaboration in existing literature, this proposal aims to investigate data-driven computational experiments and decision optimization approaches for multi-agent based collaborative mechanisms of complex supply chain networks. The applicants will propose a novel four-dimensional-flow-based perspective for studying multi-agent based collaborative mechanisms of supply chain networks and establish corresponding collaborative mechanisms, coupling models, and multi-agent based collaborative systems. Building upon the collaborative mechanisms, the applicants will propose respectively a paradigm for data-driven computational modeling and a paradigm for computational implementation for multi-agent based collaborative mechanisms of supply chain networks. The applicants will further study the key issues in the paradigms and establish approaches for data-driven structural agent modeling and implementation, flow modeling and implementation, scenario modeling and implementation, model semantic representation and verification, and experimental semantic and utility validation. After research above, the applicants will creatively conduct a four-dimensional-flow-based decision optimization model and its common paradigm. By extending the model and paradigm, the applicants will establish a data-driven decision optimization approach based on computational experiments and its corresponding paradigm. Finally, these new models and approaches will be verified and applied to and calibrated with complex and multi-facet real-world case studies. It is expected that this proposed research will further develop and refine modeling theories, computational experiment methods and decision optimization systems for multi-agent based collaborative mechanisms of supply chain networks, and play a significant role in providing a scientific framework for supply chain network collaborative management and decision making and enhancing the quality and efficiency of management and decision making.

复杂供应链网络协作的计算实验及决策优化问题是当前时代背景下供应链管理与决策领域的一个热点问题。但目前该问题研究存在一些局限，缺乏深入研究。本项目针对研究局限，提出数据驱动的复杂供应链网络多主体协作的计算实验及决策优化方法研究思路；提出基于四维流的多主体协作机制研究新视角，建立流视角下协作机理、耦合模型和多主体协作系统；在此基础上，提出数据驱动的供应链网络多主体协作的计算建模与实现范式，研究范式中关键问题，建立数据驱动的结构主体、流和情景的建模与实现方法、模型语义表达与检验方法及实验语义效用验证方法；创造性地提出基于流的四维决策优化模型及其通用范式，扩展该模型和范式，建立基于计算实验的数据驱动决策优化方法及其范式；最后开展验证与应用研究。其研究成果对进一步发展和完善供应链网络多主体协作机制的建模理论、计算实验及决策优化方法体系，提高供应链网络协作的管理与决策科学化及其质量与效率具有重要意义。

复杂供应链网络协作的计算实验及决策优化问题是当前时代背景下供应链管理与决策领域的一个热点问题。但目前该问题研究存在一些局限，缺乏深入研究。本项目针对研究局限，开展了数据驱动的复杂供应链网络多主体协作的计算实验及决策优化方法研究；构建了多维流视角下供应链网络多主体协作的三维数据粒度模型、四维流数据模型、宏微观协作机理和定量耦合模型；在此基础上，提出了数据驱动的供应链网络多主体协作的计算建模与实现范式及其方法论框架，建立了数据驱动的结构主体、流和情景的建模与实现方法及其语义效用验证方法；提出了基于计算实验的数据驱动决策优化方法及其范式，拓展研究了基于演化计算实验的供应链网络协作的决策优化方法；最后开展了验证与应用研究。项目研究成果对进一步发展和完善供应链网络多主体协作机制的建模理论、计算实验及决策优化方法体系，提高供应链网络协作的管理与决策科学化及其质量与效率具有重要意义。