Practical Measurement of Complexity in Dynamic Systems

A difficulty in complexity theory is lack of a clear definition for complexity, particularly one that is measurable. Those approaches that provide measurable definitions for the absolute complexity of a system often impose the requirement of perfect or near-perfect knowledge of system structure.In practice, it is intractable or impossible to measure the complexity of most dynamic systems.However, by measuring behavioral complexity in context with environmental scenarios, it is ossible to set bounds on a system\u27s absolute (maximum) complexity and estimate its total complexity. As this paper shows, behavioral complexity can be determined by observing a system\u27s changes in kinetic energy.This research establishes a methodology for measuring complexity in dynamic systems without the requirement of system structure knowledge. This measurement can be used to compare systems, understand system risks, determine failure dynamics, and guide system architecture

USEFUL MEASURES OF COMPLEXITY: A MODEL OF ASSESSING DEGREE OF COMPLEXITY IN ENGINEERED SYSTEMS AND ENGINEERING PROJECTS

Many modern systems are very complex, a reality which can affect their safety and reliability of operations. Systems engineers need new ways to measure problem complexity. This research lays the groundwork for measuring the complexity of systems engineering (SE) projects. This research proposes a project complexity measurement model (PCMM) and associated methods to measure complexity. To develop the PCMM, we analyze four major types of complexity (structural complexity, temporal complexity, organizational complexity, and technological complexity) and define a set of complexity metrics. Through a survey of engineering projects, we also develop project profiles for three types of software projects typically used in the U.S. Navy to provide empirical evidence for the PCMM. The results of our work on these projects show that as a project increases in complexity, the more difficult and expensive it is for a project to meet all requirements and schedules because of changing interactions and dynamics among the project participants and stakeholders. The three projects reveal reduction of project complexity by setting a priority and a baseline in requirements and project scope, concentrating on the expected deliverable, strengthening familiarity of the systems engineering process, eliminating redundant processes, and clarifying organizational roles and decision-making processes to best serve the project teams while also streamlining on business processes and information systems.Civilian, Department of the NavyApproved for public release. Distribution is unlimited

The Impact of Service Complexity on New Service Development – A Contingency Approach

This dissertation explores the limitations and impact on performance of applying formalised NPD processes in service innovation. Hypotheses build on a structural framework of organisational factors, which have been linked to successful product innovation but have not been consistently supported in a service context. Evidence is presented for a moderating influence of service complexity on the relationship between factors relating to NSD process organisation and new service performance. The findings create a link between product and service innovation and demonstrate that whereas complex new services benefit from formalised development processes and structure, services with lower complexity do not

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics