Adaptive and flexible approaches for water resources planning under uncertainty

Planning for water supply infrastructure includes identifying interventions that cost-effectively secure an acceptably reliable water supply. In investigating a range of feasible interventions, water planners are challenged by two main factors. First, uncertainty is inherent in the predictions of future demands and supplies due for example to hydrological variability and climate change. This makes fixed invest-ment plans brittle as they are likely to fail if future conditions turn out to be different than assumed. Therefore, adaptability to changing future conditions is increasingly viewed as a valuable strategy of water planning. However, there is a lack of approaches that explicitly seek to enhance the adaptivity of water resource system developments. Second, water resource system development typically af¬fects multiple societal groups with at times competing interests. The diversity of objectives in water resource systems mean that considering trade-offs between competing objectives implied by the highest performing interventions is useful. Nonetheless, few multi-objective applications have aimed at adaptive scheduling of interventions in long-term water resource planning. This thesis introduces two novel decision-making approaches that address these two challenges in turn. Both approaches apply principles of real option analysis via two different formulations (1) a multistage stochastic mathematical programme and (2) a multi-objective evolutionary algorithm coupled to a river basin simula¬tion. In both cases, a generalised scenario tree construction algorithm is used to efficiently approximate the probabilistic uncertainty. The tree consists of possible investment paths with multiple decision stages to allow for frequent and regu¬lar modifications to the investment strategies. Novel decision-relevant metrics of adaptivity and flexibility are introduced, evolving their definition in the context of water resources planning. The approaches are applied to London’s urban water resources planning problem. Results from this thesis demonstrate that there is value in adopting adaptive and flexible plans suggesting that flexibility in activating, delaying and replacing en-gineering projects should be considered in water supply intervention scheduling. To evaluate the implementation of Real Option Analysis (ROA), the use of two metrics is proposed: the Value of the Stochastic Solution (VSS) and the Expected Value of Perfect Information (EVPI) that quantify the value of adopting adaptive and flexible plans respectively. The investment decisions results are a mixture of ‘long-term’ and ‘contingency schemes’ that are optimally chosen considering different futures. The VSS shows that by considering uncertainty, adaptive invest-ment decisions avoid £100 million NPV cost, 15% of the total NPV. The EVPI demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Additionally, a comparison study of alternative optimisation approaches to water supply capacity expansion problem demonstrate that there is benefit in waiting to allow for improvements around supply uncertainty in the case of London’s urban water resources planning problem. The results from the case study suggest that the proposed adaptive planning approach achieves substantial improvement in performance compared to alternative optimisation approaches with fixed plans saving more than £377 million, reducing NPV cost by 35%. Using a multi-objective multi-stage real-options formulation of the water planning problem, the trade-offs between a long-term water management plan’s resilience and its financial costs under supply and demand uncertainty are explored. The set of trade-off solutions consist of different investment plans that are adaptive to demand growth, approximated by a scenario tree, while robust to the effects of climate change supply uncertainty, represented by an ensemble of supply (hydro-logical) scenarios. Results show that, by being adaptive to demand uncertainty, the total NPV of the most resilient plans is lowered by 58.7%. The value in de¬laying investments by waiting for more accurate supply and demand estimates is 28.9% of total NPV. It should be noted that the results from the case study are indicative and should not be considered prescriptively as they are based in a simplified representation of London’s water supply system and should be further tested with the more detailed simulation model employed by the water utility which includes the latest proposed option designs, includes requirements to supply neighbouring water utilities, and considers more objectives

Artificial Intelligence for Small Satellites Mission Autonomy

Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

Systems Theory Based Architecture Framework for Complex System Governance

The purpose of this research was to develop a systems theory based framework for complex system governance using grounded theory approach. Motivation for this research includes: 1) the lack of research that identifies modeling characteristics for complex system governance, 2) the lack of a framework rooted in systems theory to support performance of complex system governance functions for maintaining system viability. This research focused on answering: What systems theoretic framework can be developed to inform complex system governance and enable articulation of governance function performance? The grounded theory research approach utilized three phases. First, the literature in systems theory, management cybernetics, governance and enterprise architecture was synthesized and open-coded to generalize main themes using broad analysis in NVivo software, researcher note taking in EndNote, and cataloging in Excel spreadsheets. Second, the literature underwent axial-coding to identify interconnections and relevance to systems theory and complex system governance, primarily using Excel spreadsheets. Finally, selective coding and interrelationships were identified and the complex system governance architecture framework was shaped, reviewed, and validated by qualified experts. This research examined a grounded theory approach not traditionally used in systems theory research. It produced a useful systems theory based framework for practical application, bridging the gap between theory and practice in the emerging field of complex system governance. Theoretical implications of this research include identifying the state of knowledge in each literature domain and the production of a unique framework for performing metasystem governance functions that is analytically generalizable. Management cybernetics, governance, and systems theory are expanded through a testable tool for meta-level organizational and system governance theories. Enterprise architecture is advanced with a multi-disciplinary framework that coherently presents and facilitates new use for architecture at the metasystem level. Methodological implications of this research include using grounded theory approach for systems theory research, where it is atypical. Although a non-traditional method, it provides an example for conducting fruitful research that can contribute knowledge. Practical implications of this research include a useable framework for complex system governance which has never before existed and a living structure adaptable to evolutionary change coming from any related domain or future practical application feedback

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Socio–Technical Software Engineering: a Quality–Architecture–Process Perspective

This dissertation provides a model, which focuses on Quality, Architecture, and Process aspects, to manage software development lifecycles in a sustainable way. Here, with sustainability is meant a context-aware approach to IT, which considers all relevant socio-technical units of analysis. Both social (e.g., at the level of the stakeholders community, organization, team, individual) and technical (e.g., technological environments coding standards, language) dimensions play a key role to develop IT systems which respond to contingent needs and may implement future requirements in a flexible manner. We used different research methods and analyzed the problem from several perspectives, in a pragmatic way, to deliver useful insights both to the research and practitioners communities. The Software Quality, Architecture, and Process (SQuAP) model, highlights the key critical factors to develop systems in a sustainable ways. The model was firstly induced and then deduced from a longitudinal research of the financial sector. To support the model, SQuAP-ont, an OWL ontology was develop as a managerial and assessment tool. A real-world case study within a mission-critical environment shows how these dimensions are critical for the development of IT applications. Relevant IT managers concerns were also covered with reference to software reuse and contracting problems. Finally, a long-term contribution for the educational community presents actionable teaching styles and models to train future professionals to act in a Cooperative Thinking fashion

STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL

Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage. In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems. The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics. To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs