Scalarized Preferences in Multi-objective Optimization

Multikriterielle Optimierungsprobleme verfügen über keine Lösung, die optimal in jeder Zielfunktion ist. Die Schwierigkeit solcher Probleme liegt darin eine Kompromisslösung zu finden, die den Präferenzen des Entscheiders genügen, der den Kompromiss implementiert. Skalarisierung – die Abbildung des Vektors der Zielfunktionswerte auf eine reelle Zahl – identifiziert eine einzige Lösung als globales Präferenzenoptimum um diese Probleme zu lösen. Allerdings generieren Skalarisierungsmethoden keine zusätzlichen Informationen über andere Kompromisslösungen, die die Präferenzen des Entscheiders bezüglich des globalen Optimums verändern könnten. Um dieses Problem anzugehen stellt diese Dissertation eine theoretische und algorithmische Analyse skalarisierter Präferenzen bereit. Die theoretische Analyse besteht aus der Entwicklung eines Ordnungsrahmens, der Präferenzen als Problemtransformationen charakterisiert, die präferierte Untermengen der Paretofront definieren. Skalarisierung wird als Transformation der Zielmenge in diesem Ordnungsrahmen dargestellt. Des Weiteren werden Axiome vorgeschlagen, die wünschenswerte Eigenschaften von Skalarisierungsfunktionen darstellen. Es wird gezeigt unter welchen Bedingungen existierende Skalarisierungsfunktionen diese Axiome erfüllen. Die algorithmische Analyse kennzeichnet Präferenzen anhand des Resultats, das ein Optimierungsalgorithmus generiert. Zwei neue Paradigmen werden innerhalb dieser Analyse identifiziert. Für beide Paradigmen werden Algorithmen entworfen, die skalarisierte Präferenzeninformationen verwenden: Präferenzen-verzerrte Paretofrontapproximationen verteilen Punkte über die gesamte Paretofront, fokussieren aber mehr Punkte in Regionen mit besseren Skalarisierungswerten; multimodale Präferenzenoptima sind Punkte, die lokale Skalarisierungsoptima im Zielraum darstellen. Ein Drei-Stufen-Algorith\-mus wird entwickelt, der lokale Skalarisierungsoptima approximiert und verschiedene Methoden werden für die unterschiedlichen Stufen evaluiert. Zwei Realweltprobleme werden vorgestellt, die die Nützlichkeit der beiden Algorithmen illustrieren. Das erste Problem besteht darin Fahrpläne für ein Blockheizkraftwerk zu finden, die die erzeugte Elektrizität und Wärme maximieren und den Kraftstoffverbrauch minimiert. Präferenzen-verzerrte Approximationen generieren mehr Energie-effiziente Lösungen, unter denen der Entscheider seine favorisierte Lösung auswählen kann, indem er die Konflikte zwischen den drei Zielen abwägt. Das zweite Problem beschäftigt sich mit der Erstellung von Fahrplänen für Geräte in einem Wohngebäude, so dass Energiekosten, Kohlenstoffdioxidemissionen und thermisches Unbehagen minimiert werden. Es wird gezeigt, dass lokale Skalarisierungsoptima Fahrpläne darstellen, die eine gute Balance zwischen den drei Zielen bieten. Die Analyse und die Experimente, die in dieser Arbeit vorgestellt werden, ermöglichen es Entscheidern bessere Entscheidungen zu treffen indem Methoden angewendet werden, die mehr Optionen generieren, die mit den Präferenzen der Entscheider übereinstimmen

Operational research IO 2021—analytics for a better world. XXI Congress of APDIO, Figueira da Foz, Portugal, November 7–8, 2021

This book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.info:eu-repo/semantics/publishedVersio

Equipped for Change: A Grounded Theory Study of White Antiracist School Leaders’ Attitudes and Perceptions of Racial Consciousness in Educational Leadership

There is substantial evidence that issues of race and racism and are common in U.S. public schools, especially those greatly impacted by poverty and racial segregation. Unfortunately, it is highly likely many of these occurrences either go unrecognized, unacknowledged, or are perpetrated unknowingly by White educators and administrators—many of whom are well-intentioned, but lack the critical lens necessary in challenging and dismantling them. For White people, the enculturating normativity of White racial dominance, maintained by the social conditioning of Whiteness, facilitates an environment of racial ignorance and insignificance, leaving most painfully oblivious to the damaging complexities of racism in contemporary American society. The purpose of this qualitative study is to illuminate the perceptions and experiences of selected White school leaders who have committed themselves to (a) antiracist school leadership identity development, and (b) the promotion of racially-just school cultures. Responses to semi-structured interview questions were coded, analyzed, and organized into themes to generate an educational leadership theory. Constructivist grounded theory (CGT) methodologies, critical race theory (CRT), critical whiteness studies (CWS), and critical pedagogy (CP) informed the data collection methods and theoretical foundations of this study. Findings revealed a need to reexamine and revise existing antiracist education psychology and pedagogy with an emphasis on cohesion and clarity of purpose. This study contributes new knowledge and insight into the struggle to successfully implement effective, sustainable antiracist school efforts capable of establishing and normalizing racial equity in public education

Five Insights for Avoiding Global Collapse

Looming environmental and social breaking points, like climate change and massive inequalities, are becoming increasingly apparent and large in scale. In this book, Gaya Herrington puts today’s key societal challenges in perspective. Her analysis, rooted in her research on a 50-year-old model of the world that forecasted the onset of global collapse right around the present time, brings some structure to what otherwise might feel like the overwhelming task of achieving genuine societal sustainability. Herrington's research, first published in 2020 in Yale‘s Journal of Industrial Ecology, went viral after it revealed empirical data tracked closely with the predictions of this world model, which was introduced in the 1972 best seller The Limits to Growth. Her book Five Insights for Avoiding Global Collapse contains an exclusive research update based on 2022 data and is written in a more personable and accessible style than the journal article. Herrington also elaborates more in this book on the many interlinkages between our economic, environmental, and social predicaments, and on what her findings indicate for future global developments. Herington lays out why “business as usual” is not a viable option for global society and identifies the root cause of this unsustainable path. Most importantly, her book teaches us what systemic changes humanity still has time to make to achieve a better tomorrow. A future in which society has transformed beyond the mere avoidance of collapse and is truly thriving

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

Understanding and modeling food flow networks across spatial scales

We live in an increasingly global society, in which food commodity transfers enable production and consumption activities to be separated in space via complex supply chains. Here, we refer to the movement of food commodities from one location to another as ‘food flows’, reserving the term ‘food trade’ for the international exchange of food commodities between countries. Food flows underpin the complex food supply chains that are prevalent in our increasingly globalized world. Recently, much effort has been devoted to evaluating the resources (e.g. water, carbon, nutrients) embodied in food trade. Now, research is needed to understand the scientific principles of the food commodity flows that underpin these virtual resource transfers. What are the network properties of food flows within a country? How do food flows vary with spatial scale? How can we model food flows in locations without empirical information? This dissertation seeks to address these three overarching questions. First, this dissertation presents a novel application of network analysis to empirical information of domestic food flows within the USA, a country with global importance as a major agricultural producer and trade power. We find normal node degree distributions and Weibull node strength and betweenness centrality distributions. An unassortative network structure with high clustering coefficients exists. These network properties indicate that the USA food flow network is highly social and well-mixed. However, a power law relationship between node betweenness centrality and node degree indicates potential network vulnerability to the disturbance of key nodes. We perform an equality analysis which serves as a benchmark for global food trade, where the Gini coefficient = 0.579, Lorenz asymmetry coefficient = 0.966, and Hoover index = 0.442. These findings shed insight into trade network scaling and proxy free trade and equitable network architectures. Second, this dissertation presents an empirical analysis of food commodity flow networks across the full spectrum of spatial scales: global, national, and village. We discover properties of both scale invariance and scale dependence in food flow networks. The statistical distribution of node connectivity and mass flux are consistent across scales. Node connectivity follows a generalized exponential distribution, while node mass flux follows a Gamma distribution across scales. Similarly, the relationship between node connectivity and mass flux follows a power law across scales. However, the parameters of the distributions change with spatial scale. Mean node connectivity and mass flux increase with increasing scale. A core group of nodes exists at all scales, but node centrality increases as the spatial scale decreases, indicating that some households are more critical to village food exchanges than countries are to global trade. Remarkably, the structural network properties of food flows are consistent across spatial scales, indicating that a universal mechanism may underpin food exchange systems. Finally, we use our understanding of food flow networks across spatial scales to model food flows at resolutions for which empirical information is not available. Detailed spatial information on food flows is rare, but it is increasingly important to understand spatially resolved food flows to assess their embodied resources and vulnerability to supply chain disturbances. To this end, we develop the Food Flow Model, a data-driven methodology to estimate spatially explicit food flows for subnational locations without data. The Food Flow Model integrates machine learning, network properties, production and consumption statistics, mass balance constraints, and linear programming. We use the Food Flow Model to infer food flows between counties within the United States. Specifically, we downscale empirical information on food flows between 132 Freight Analysis Framework (FAF) locations (17,292 potential links) to the 3,142 counties and county-equivalents of the United States (9,869,022 potential links). Future work can build on these efforts to improve our understanding of vulnerabilities within a national food supply chain, determine critical infrastructures, and enable spatially detailed footprint assessments

Five Insights for Avoiding Global Collapse

Looming environmental and social breaking points, like climate change and massive inequalities, are becoming increasingly apparent and large in scale. In this book, Gaya Herrington puts today’s key societal challenges in perspective. Her analysis, rooted in her research on a 50-year-old model of the world that forecasted the onset of global collapse right around the present time, brings some structure to what otherwise might feel like the overwhelming task of achieving genuine societal sustainability. Herrington's research, first published in 2020 in Yale‘s Journal of Industrial Ecology, went viral after it revealed empirical data tracked closely with the predictions of this world model, which was introduced in the 1972 best seller The Limits to Growth. Her book Five Insights for Avoiding Global Collapse contains an exclusive research update based on 2022 data and is written in a more personable and accessible style than the journal article. Herrington also elaborates more in this book on the many interlinkages between our economic, environmental, and social predicaments, and on what her findings indicate for future global developments. Herington lays out why “business as usual” is not a viable option for global society and identifies the root cause of this unsustainable path. Most importantly, her book teaches us what systemic changes humanity still has time to make to achieve a better tomorrow. A future in which society has transformed beyond the mere avoidance of collapse and is truly thriving