The risk assessment method in prognostic models of production systems management with account of the time factor

The possibility of planning risks assessment and the setting of the planning horizon are analysed with account of the time factor basing. A risk metrics approach is used, with risks estimated as a time function given in tabular form for the purposes of master production. Dependence of the integrated planning risks on the accuracy of forecasts used is proved and a time point is identified after which the risk assessment value increases sharply. The study enables to obtain the values of planning risks integral assessment in management problems using forecast data for groups of indicators and parameters involved in decision-making.peer-reviewe

A meta-architecture analysis for a coevolved system-of-systems

Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments. Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance. This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

System of Systems Stakeholder Planning in a Multi-Stakeholder, Multi-Objective, and Uncertain Environment

The United States defense planning process is currently conducted in a partially consolidated manner driven by the Joint Capabilities Integration and Development System (JCIDS) process. Decisions to invest in technology, develop systems, and acquire assets are made by individual services with coordination at the higher joint level. These individual service’s decisions are made in an environment where resource allocation and need are influenced by external stakeholders (e.g. shared system development costs, additional levied requirements, and complementary system development). The future outcome of any given decision is subject to a high degree of uncertainty stemming from both the stakeholder execution of a decision and the environment in which that execution will take place. Uncertainty in execution stems from TRL advancement, development timelines, acquisition timelines, and final deployed performance. Environmental uncertainty factors include future stakeholder resource availability, the future threat environment, cooperative stakeholder decisions, and mirrored adversary decisions. The defense planning problem can be described as an acknowledged System of Systems (SoS) planning problem. Today, methodologies exist that individually address SoS Engineering processes, the evaluation of SoS performance, and SoS system deterministic evolution. However, few approaches holistically address the SoS planning and evolution problem at the level needed to assist individual defense stakeholders in strategic planning. Current approaches do not address the impact of multiple-stakeholder decisions, multiple goals for each stakeholder, the uncertainty of decision outcomes, and the temporal component to strategic decision making. This thesis develops and tests a methodology to address defense stakeholder planning in a multi-stakeholder, multi-objective, and uncertain environment. First, a decision space is populated and captured via sampling a game framework that represents multiple stakeholder decisions as well as decision outcomes over time. A compressed Markov Decision Process (MDP) based meta-model is constructed using state-space consolidation techniques. The meta-model is evaluated using a risk-based policy development algorithm derived from combining traditional Reinforcement Learning (RL) techniques with mean-variance portfolio theory. Policy sensitivity to stakeholder risk-tolerance levels is used to develop state-based risk-tolerance sensitivity profiles and identify Pareto efficient actions. The risk-tolerance sensitivity profiles are used to evaluate both state spaces and decision spaces to provide stakeholders with risk-based insights, or rule sets, to support immediate decision making and risk-based stakeholder playbook development. The capability of the risk-based policy algorithm is tested using both elementary and complex scenarios. It is demonstrated that the algorithm can be used to extract Pareto efficient decisions as a function of risk-tolerance. The state space compression is tested via the comparison of the loss of information between the risk-based policy solutions for uncompressed and compressed state space. The full methodology is then demonstrated using a full-complexity scenario based on the joint development by France, Germany, and Spain of the SoS based Future Combat Air System (FCAS). The full complexity scenario is used to baseline the risk-based methodology against current optimal policy solution techniques. A significant increase in resulting derived insights relative to optimal policy solutions in a high uncertainty scenario is demonstrated.Ph.D