Scalable Enterprise Systems Phase II: Emergent Organizational Structures in Multi-Agent Scalable Enterprises

这个可扩展企业系统第二阶段项目的主要目标是开发新兴企业的模型，这些模型捕捉所涉及的组织的独立行为，以及各个组织之间的相互作用的影响，以便准确地预测新兴企业的动态和系统的长期性能。 企业的参与者可以使用这些结果来选择适当的运营战略，以改善当地的绩效指标，如利润或现金流在一个固定的规划范围。 此外，这些模型将提供洞察机制，导致有效的联盟和组织设计。 建模方法有三个步骤。 首先，一个代表性的计划是开发用于捕获的个人参与者，结合任务信息，组织关系，本地和系统级的目标，以及可能的变化，在环境中的特性。 因此，企业是每个组织以及这些组织之间的交互的几个这样的详细表示的集合。 其次，一个微观宏观建模方法的开发，其中每个代理的任务（表示在微观层面的仿真框架）的细节被聚合成一组在宏观层面的交互参数。宏观层次的企业模型制定为一个相互作用的粒子系统。最后，第三步涉及使用现场数据验证理论。现场数据将收集来自不同的行业，有大量的参与者在供应网络中，如食品行业和汽车行业，构建实用的代理表示，并验证理论的性能估计。使用多代理模拟器的大规模模拟也将用于经验估计给定组织配置的性能。这项研究将使规划者能够先验地估计不同运营条件下的整体企业绩效，并最终设计出最佳的新兴社会。此外，系统组成的分布式决策者遇到在各种应用领域，如交通公路网络，空中交通管理系统，设计团队组成的分散的设计人员，分布式传感器网络，多智能体系统等，在这项研究中取得的基本进展将有显着的影响，我们的能力来预测这样的系统的性能，从而制定适当的规划和控制策略。

This Scalable Enterprise Systems Phase II project's primary goal is to develop models of emergent enterprises that capture the independent behavior of the organizations involved, as well as the effects of interactions among individual organizations, in order to accurately predict dynamics of the emergent enterprise and performance of the system in the long run. Participants in the enterprise can use these results to select appropriate operational strategies in order to improve local performance measures such as profit or cash flow over a fixed planning horizon. In addition, these models will provide insight into the mechanisms that result in effective alliances and organizational designs. The modeling approach has three steps. First, a representation scheme is developed for capturing characteristics of individual participants that incorporates task information, organizational relationships, local and system level goals, and possible changes in the environment. An enterprise is thus a collection of several of such detailed representations for each organization and the interactions between these organizations. Second, a micro-macro modeling approach is developed, where the details of each agent's tasks (represented in the micro-level simulation framework) are aggregated into a set of interaction parameters at the macro-level. The macro-level enterprise model is formulated as an interacting particle system. Finally, the third step involves validation of the theories developed using field data. Field data will be collected from different industries that have large number of participants in the supply network, such as the food industry and the automotive industry, to construct practical agent representations and to verify the theoretical performance estimates. Large-scale simulations using a multi-agent simulator will also be used to empirically estimate the performance of a given organizational configuration. This research will allow planners to estimate a priori the overall enterprise performance under different operating conditions, and eventually, design optimal emergent societies. Moreover, systems composed of distributed decision-makers are encountered in various application domains, such as traffic on highway networks, air traffic management systems, design teams composed of dispersed designers, distributed sensor networks, multi-agent systems, etc. The fundamental advances made in this research will have significant impact on our ability to predict the performance of such systems, and therefore devise appropriate planning and control strategies.