Resilience and Robustness of Dynamic Manufacturing Supply Networks

高效和有效的制造供应网络（MSN）对全球经济的运作至关重要。根据EPSRC的呼吁，该提案的前提是坚信适当的数学理论和方法可以为MSN的行为提供根本性的新理解，并为MSN的分析、设计和管理提供有效的调查工具集。特别是，我们认为网络科学的力量可以用来支持微信网络的弹性和稳健性的新思维。这项工作将强有力地嵌入到三个领域的实际msn中：生产商驱动的入站msn和工业公司的出站分销渠道；用于高价值制造业的关键产品（例如钛或复合预浸料）的全球msn；以及为英国新兴产业（如可再生能源）不断发展的msn。该项目将发展和应用现有的和新的数学，特别是在复杂自适应网络理论中，借鉴博弈论、动力系统和贝叶斯信息学的技术。它还将学习生态学、新陈代谢模型和公用事业网格的相关建模方法。这笔拨款将是开发综合数学模型套件的第一次尝试，以支持msn在风险和不确定性背景下的有效决策。这项工作将建立在网络科学和复杂自适应动力系统、贝叶斯统计和运筹学的最新发展基础上，以开发新的模型和措施，以更好地理解和分析MSN的行为和性能。本文将对MSN中的风险和漏洞进行多角度和多层次的分析，包括战略性MSN设计和政策层面的物理、财务、信息、关系和治理视角，以及战略和运营层面的风险缓解策略，以支持MSN管理。这是一个冒险和具有挑战性的建议，因为以下原因：(1)基于pi的专业知识领域，从复杂网络理论和非线性动力学，到可靠性和风险评估等领域的应用统计，以及运筹学和运营管理方法的开发和应用到MSN管理和控制问题。然而，我们的专业知识是互补的，将增加大量的新知识，并为复杂网络、网络动力学和贝叶斯网络的理论带来新奇，而且，将这些新模型应用于现实世界的MSN问题将最终导致更好地理解复杂的MSN行为，并将改善MSN在风险和不确定性存在下的管理和控制。(2)该提案将汇集来自4所大学的pi和PDRAs。管理所涉及的资源本身就是一项挑战。然而，我们相信，一个精心设计的项目管理计划可以引导这项研究合作取得成功。此外，如果获得资助，这项研究项目可能会确保四所大学之间的合作继续下去。(3)该项目将涉及一系列广泛的工业合作伙伴，从制造业（例如航空航天和国防）到制造业贸易组织和顾问，再到一个全新行业（海上可再生能源）的代表，而该行业目前还不存在MSNn。

Efficient and effective manufacturing supply networks (MSN) are essential to the functioning of the global economy. In line with the EPSRC call, this proposal is premised on the strong belief that appropriate mathematical theory and methods can provide fundamentally new understanding on the behaviour of MSNs and provide an effective investigative toolset for MSN analysis, design and management. In particular we argue that the power of network science can be harnessed to underpin new thinking in MSNs for resilience and robustness. The work will be strongly embedded in real MSNs in three domains - producer-driven inbound MSNs and outbound distribution channels for industrial companies; global MSNs for critical products used in high-valued manufacturing (e.g. titanium or composite pre-preg materials); and evolving MSNs for emerging UK industries such as renewable energy. The project will develop and apply existing and new mathematics specifically in the theory of complex adaptive networks, drawing on techniques from game theory, dynamical systems and Bayesian informatics. It will also learn from related modelling approaches in ecology, metabolism modelling and utility grids.This grant will represent the first attempt to develop an integrated mathematical modelling suite to support effective decision making in MSNs in the context of risk and uncertainty. The work will build on disparate recent developments in network science and complex adaptive dynamical systems, Bayesian statistics and operational research to develop new models and measures to better understand and analyse MSN behaviour and performance. Multiple perspectives and a multi-level view of risks and vulnerabilities in MSNs will be taken, including physical, financial, informational, relational, and governance perspectives at the strategic MSN design and policy levels, and risk mitigating strategies at both strategic and operational levels to support MSN management.This is an adventurous and challenging proposal due to the following reasons: (1) The PIs based in have various domains of expertise, from theory of complex networks and nonlinear dynamics, to applied statistics in domains such as reliability and risk assessment, and development and application of operational research and operations management methods to MSN management and control problems. However, our expertise is complementary and will add a substantial body of new knowledge and bring novelties to the theory of complex networks, network dynamics and Bayesian networks, but also, applications of these new models to real-world MSN problems will ultimately lead to better understanding of complex MSN behaviour and will improve MSN management and control in the presence of risks and uncertainties. (2) This proposal will bring together PIs and PDRAs from 4 universities. The management of the resources involved is a challenge on its own. However, we believe that a very carefully designed project management plan can lead this research collaboration to its success. Furthermore, if funded, this research project can potentially secure the continuation of the collaboration among the four universities. (3) The project will involve a wide array of industrial partners from manufacturing primes (e.g. in Aerospace and Defence) to manufacturing trade organisations and consultants, to representatives of a brand new industry (offshore renewable energy) for which the in-bound MSNn does not yet exist.