A Computational Intelligence Approach to System-of-Systems Architecting Incorporating Multi-Objective Optimization

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization

System-of-Systems Tools for the Analysis of Technological Choices in Space Propulsion

Difficulties in space mission architecture design arise from many factors. Performance, cost, and risk constraints become less obvious due to complex interactions between the systems involved in the mission; decisions regarding long-term goals can heavily impact technological choices for short-term parts of the mission, while conversely decisions in the near future will impact the whole flexibility of long-term plans. Furthermore, the space community is broadening its borders, and space agencies from different countries are collaborating with industry and commercial partners towards large-scale endeavors. This paradigm shift is prompting the development of non-traditional approaches to the design of space missions. This paper reports the results of the first year of a continuing collaboration of the authors to develop and demonstrate System-of-System engineering methodologies for the deep analysis of dependencies and synthesis of robust architectures in exploration mission contexts. We present the procedure that we followed to develop and apply our methodology, obstacles found, steps taken to improve the methods based on the needs of experts and decision makers, required data for the analysis, and results produced by our holistic analysis. In particular, we focus on the analysis of technological choices for space propulsion for a generic cislunar mission, including both complex interactions between subsystems in different type of propulsion and availability of different providers. We identify critical systems and sets of systems based on cascading effects of performance degradation, assessment of the robustness of different designs in the operational domain, and simultaneous analysis of schedule dependencies between the constituent systems

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