Abstract
Sustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g., the Earth’s global climate, social organisations, Boeing’s supply chain, automotive products and plants’ operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems’ responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system’s responses experiencing interaction change or interruption (i.e., interactive disruption). Having this knowledge will allow the optimisation of systems’ efficiency and would ultimately reduce the system’s total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system following interactive changes and interruptions. Moreover, a dynamic adaptive response of the global system over time should be taken into account. This dynamic behaviour can capture the interactive behaviour of components and sub-systems within a complex global system. Such assessment would benefit many systems including information systems. Due to emergence and expansion of big data analytics and cloud computing systems, such life-cycle assessments can be considered as a strategic planning framework before implementation of such information systems.
Original language | English |
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Title of host publication | Strategic Engineering for Cloud Computing and Big Data Analytics |
Editors | Amin Hosseinian-Far, Muthu Ramachandran, Dilshad Sarwar |
Publisher | Springer Nature Link |
Pages | 3-27 |
Number of pages | 25 |
ISBN (Print) | 9783319849157 |
DOIs | |
Publication status | Published - 15 Feb 2017 |