DynSyst_Special_Topics: Optimization of Enterprise Dynamical Systems Described By Rules

能源管理、供应链管理、交通运输、灾害管理等企业都是复杂的工程系统。面对大量代理人之间的不确定性和复杂的相互作用，决策者努力降低成本和提高效率。依赖于静态混合整数规划模型的传统优化方法不能准确地捕捉动态企业的复杂性，并且受到计算复杂性的限制。我们提出了一种新的方法，将基于规则的系统与优化相结合，以动态控制现代企业。通过允许决策者使用IF-THEN规则对其复杂的企业进行建模，我们将使他们能够准确地反映现实生活环境，并轻松修改规则以捕捉系统中的意外变化。我们将考虑几个潜在的应用领域之一。一个领域是电力的传输和分配，其中使用规则或协议来决定如何分配电力储备，以适应电力市场中的不确定因素(由于天气、停电、需求等)。另一个可能的应用领域是运输管理，其中车辆路线和调度必须响应考虑交通、卡车维护和人员的动态取货和送货请求。我们还可以与灾难管理团队(包括医疗提供者和消防部门)一起测试我们的基于规则的方法，以设计有效应对灾难的协议。当前优化企业系统的方法受到其准确建模复杂系统的能力以及解决NP-Hard问题的计算限制的限制。我们方法的智力优势在于开发了一种将企业规则转换为自动机的数学方案，自动机提供了规则的动态。这使我们能够解决最优控制问题。我们通过自动机将解决方案转换回应用程序的规则。这项研究将通过创造一种新的混合方法论，为计算机科学、应用数学和运筹学研究社区做出贡献。我们的方法具有变革性，因为它几乎可以应用于任何企业，并以他们自己的自然语言为决策者提供规则。它的广泛影响在于有可能为组合型企业问题提供一种新的混合方法，例如路线、排序和调度，这些问题目前依赖于混合整数规划和启发式算法。在我们的研究和教育使命中，我们致力于促进多样性，并将招收和指导代表性不足的学生。

Enterprises, such as energy management, supply chain management, transportation and disaster management are complex engineering systems. Decision makers struggle to reduce costs and increase efficiencies in the face of uncertainty and complicated interactions among a large number of agents. Traditional optimization approaches that rely on static mixed integer programming models cannot accurately capture the complexities of the dynamic enterprise and are limited by computational complexity. We propose a novel approach to integrate rule-based systems with optimization to dynamically control modern enterprises. By allowing decision makers to model their complex enterprise with if-then rules, we will enable them to accurately reflect the real-life environment and to easily modify the rules to capture unexpected changes in the system. We will consider one of several potential application areas. One area is the transmission and distribution of electrical power, where rules or protocol are used to decide how to distribute power reserves to accommodate uncertainties in the power market (due to weather, power outage, demand, etc.). Another possible application area is transportation management, where vehicle routing and scheduling must respond to dynamic pick-up and delivery requests that consider traffic, truck maintenance, and personnel. We may also test our rule-based methodology with disaster management teams, including medical providers and fire departments, to design protocols for efficient responses to disasters.Current approaches to optimizing enterprise systems are limited by their ability to accurately model a complex system, as well as the computational limitations to solve NP-hard problems. The intellectual merit of our approach lies in developing a mathematical scheme that converts rules of an enterprise to an automaton, which provides the dynamics of the rules. That allows us to solve an optimal control problem. We translate the solution back into the rules of the application via the automaton. This research will contribute to computer science, applied math and operations research communities by creating a new hybrid methodology. Our approach is transformative in that it can apply to virtually any enterprise, and provides the decision makers with rules in their own natural language. Its broad impact resides in the potential to provide a new hybrid methodology for combinatorial type enterprise problems, such as routing, sequencing and scheduling, that currently rely on mixed integer programs and heuristics. In our research and education missions, we are committed to fostering diversity and will recruit and mentor underrepresented students.