Theoretical and implementation improvements for difference evaluation functions

Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each individual agent on the overall system performance must be ascertained. Difference evaluation functions aim to solve this credit assignment problem, by approximating the effect that each agent has on the system evaluation function. Difference evaluations have proven to produce superior learned policies in many multiagent settings. Although difference evaluations have produced excellent empirical results, there are still three key research questions that must be addressed regarding their usefulness in real-world systems. More specifically, the performance, theoretical advantages, and methodology for implementation must be addressed in order to demonstrate that difference evaluations are practical for use in real-world multiagent learning. These research questions are addressed in this dissertation. The first contribution of this dissertation is to demonstrate that difference evaluations may be extended and combined with other coordination mechanisms, resulting in superior learned performance. The second contribution of this dissertation is to derive conditions which guarantee that difference evaluations will outperform traditional coordination mechanisms. The third and final contribution of this dissertation is to demonstrate that difference evaluations may be approximated using only local knowledge, allowing for their implementation in any generic multiagent learning setting. By addressing the performance, theoretical foundation, and implementation concerns of difference evaluations, this dissertation provides a detailed analysis demonstrating the usefulness of difference evaluation functions in multiagent learning systems

Otimização do scheduling de nafta petroquímica utilizando algoritmos genéticos

Atualmente, as indústrias petroquímicas enfrentam um aumento nos preços da nafta, matéria-prima para a primeira geração, o que torna necessária a busca por novos fornecedores e, muitas vezes, a compra de lotes que apresentam preços mais baixos em função da presença de contaminantes. O gerenciamento otimizado dos lotes recebidos através de operações de blending viabiliza o recebimento dos lotes que apresentam contaminantes e o enquadramento destes nos limites de processamento das unidades. Desta forma, técnicas de otimização aplicadas ao blending e ao scheduling dos recebimentos podem fornecer ferramentas que ajudem a flexibilizar a compra de matérias-primas, diminuindo os gastos com este insumo e aumentando o lucro da empresa. O objetivo principal deste estudo é a solução do problema de recebimento de matéria-prima de uma indústria petroquímica de primeira geração via otimização matemática, visando auxiliar no processo de tomada de decisões. Como resultado, tem-se a definição das quantidades das matérias-primas disponíveis que irão compor a mistura final que será entregue para processamento nas unidades. O modelo leva em consideração os estoques de nafta disponíveis e a suas respectivas composições no instante inicial da otimização, a disponibilidade de navios para descarregamento, as demandas de consumo das unidades, as restrições operacionais de bombeamento e armazenagem e as restrições de qualidade. Estas últimas englobam os limites de processamento de contaminantes e o percentual mínimo de parafinicidade, principal parâmetro de rendimento da nafta, que serve como parâmetro para definir a mistura ideal dos componentes de modo a maximizar o seu rendimento em produtos finais desejados para cada cenário de produção. O modelo de otimização foi desenvolvido baseado em programação mista inteira não-linear (MINLP), com representação discreta do tempo. As variáveis de decisão envolvem a alocação de descarga de navios em tanques de armazenagem, bem como operações de transferência entre tanques de diferentes parques de tancagem através de oleodutos. Sendo assim, para fins de modelagem, as variáveis de decisão do problema foram descritas como o status de abertura e fechamento das válvulas de entrada e saída de cada tanque do sistema, as quais totalizam 34 válvulas, para cada um dos instantes da simulação, os quais totalizam 56, obtendo-se assim um total de 1.904 variáveis de decisão. Foram consideradas restrições operacionais relacionadas a volumes de produto nos tanques, status de abertura e fechamento das válvulas dos tanques e trocas excessivas de tanques de recebimento/expedição, assim como restrições de qualidade relacionadas aos limites de processamento de contaminantes das unidades. Para a resolução do problema de otimização, foi empregado um algoritmo genético e adotado um horizonte de predição de tamanho igual a 56. O modelo proposto foi aplicado ao sistema de recebimento de matéria-prima de uma indústria petroquímica real e os resultados mostram o desempenho do modelo quando aplicado a cenários distintos, envolvendo diferentes graus de dificuldade. A partir dos resultados obtidos e do seu comparativo com uma programação realizada por um especialista ad hoc através da Tabela 2, evidenciou-se que o algoritmo foi capaz de resolver cada um dos cenários avaliados, sempre mostrando aderência à estratégia de blending adotada pela indústria

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Evolutionary computing for routing and scheduling applications

Ph.DDOCTOR OF PHILOSOPH

Solution Strategies in Short-term Scheduling for Multitasking Multipurpose Plants

This thesis addresses challenges in short-term scheduling of multipurpose facilities using mathematical optimization. Such approach involves the formulation of a predictive model and an objective function, and the development of a solution strategy around such scheduling model formulation in order to obtain an operating schedule that achieves certain objectives, such as maximization of throughput or minimization of makespan. There are many choices that must be made in these aspects of short-term scheduling, and these choices often lead to a trade-off between the solution quality and computational time. This thesis presents two studies analyzing the quality-CPU time trade-off in two major aspects: time representations in model formulation, and the strategy for handling multiple conflicting objectives. The ultimate goal is to develop bi-objective short-term scheduling approaches to tackle industrial-sized problems for multitasking multipurpose plants that are computationally inexpensive, but provide practical schedules with a good balance between throughput and makespan. The first study addresses the first aspect of interest and compares two different time representation approaches: discrete-time and continuous-time approaches. This comparison is made considering maximization of throughput as the sole objective. We show that, for the modeling framework implemented in this work, the selected discrete-time formulation typically obtained higher quality solutions, and required less time to solve compared to the selected continuous-time formulation, as the continuous-time formulation exhibited detrimental trade-off between computational time and solution quality. We also show that within the scope of this study, non-uniform discretization schemes typically yielded solutions of similar quality compared to a fine uniform discretization scheme, but required only a fraction of the computational time. The second study builds on the first study and develops a strategy around an efficient non-uniform discretization approach to handle the conflicting objectives of throughput maximization and makespan minimization, focusing on a priori multi-objective methods. Two main contributions are presented in this regard. The first contribution is to propose a priori bi-objective methods based on the hybridization of compromise programming and the U+03B5-constraint method. The second is to present short-term operational objective functions, that can be used within short-term scheduling to optimize desired long term objectives of maximizing throughput and minimizing makespan. Two numerical case studies, one in a semiconductor processing plant and an analytical services facility, are presented using a rolling horizon framework, which demonstrate the potential for the proposed methods to improve solution quality over a traditional a priori approac

A FRAMEWORK FOR STRATEGIC PROJECT ANALYSIS AND PRIORITIZATION

Projects that support the long-term strategic intent and alignment are considered strategic projects. Therefore, these projects must consider their alignment with the organization’s current strategy and focus on the risk, organizational capability, resources availability, political influence, and socio-cultural factors. Quantitative and qualitative methods prioritize the projects; however, they are usually suitable for specific industries. Although prioritization models are used in the private sector, the same in the public sector is not widely seen in the literature. The lack of models in the public sector has happened because of the projects’ social implications, the value perception of different projects in the public sector, and potentially differing value perceptions attached to the types of projects in different decision-making environments in the public sector. The thesis proposes a generic framework to develop a priority list of the available basket of projects and decide on projects for the next undertaking. The focus of the thesis is on public projects. The analysis in the framework considers the critical factors for prioritization obtained from the literature clustered through the agglomerative text clustering technique. In the proposed framework, 13 critical clusters are identified and weighted using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to develop their ranking using the Technique for Order of Preference Similarity Ideal Solution (TOPSIS) method. In addition, the proposed framework uses vector weighting to prioritize projects across industries. The applicability of the framework is demonstrated through Qatar’s real estate and transportation projects. The outcome obtained from the framework is compared with those obtained through the experts using the System Usability Scale (SUS). The comparison shows that the framework provides good predictability of the projects for implementation

New Strategies for Global Optimization of Chemical Engineering Applications by Differential Evolution

Ph.DDOCTOR OF PHILOSOPH

Engineering Modular Synthetic Microbial Consortia for Sustainable Bioproduction From CO2

Engineering of synthetic microbial consortia has emerged as a new and powerful biotechnology platform with enormous potential for the production of biobased commodity chemicals. In this dissertation, I have designed, constructed, and optimized a tripartite system in which three microbes of differentiated specializations can convert sunlight, carbon dioxide, and atmospheric nitrogen into desired molecules or materials. Specifically, Synechococcus elongatus, a photosynthetic cyanobacterium that exports sucrose, and Azotobacter vinelandii, a nitrogen-fixing bacterium that secretes ammonia, form a symbiotic foundation hypothesized to support a third producer specialist. The tripartite consortia were implemented using a novel experimental set-up for continuous culture and extensive optimization was carried out with insights and guidance from computational modeling of the system dynamics. As a clear and strong proof of concept, I demonstrated various realizations of this tripartite platform, employing producer specialist strains ranging from model microorganism Escherichia coli to widely used industrial chassis such as Corynebacterium glutamicum and Bacillus subtilis. This versatile and modular technology platform offers potential for bioproduction without environmentally or monetarily expensive nutrient inputs thereby a pathway towards sustainable manufacturing of a wide range of bio-products. As an important component of the effort of engineering the tripartite system described above, I also carried out genetic modifications of E. coli K-12, the most widely used microbial chassis in synthetic biology, to enable efficient utilization of sucrose. A multigene csc operon encoding non-PTS sucrose catabolism was randomly transposed into E. coli K-12 using Tn5 transposase. Isolates from the transposon library yielded a range of growth rates on sucrose, including some that were comparable to that of E. coli K-12 on glucose. Narrowness of the growth rate distributions, improved gene expression conferring faster growth compared to that of plasmids, and enhanced growth rate upon transduction into strains that underwent adaptive laboratory evolution indicate that efficient csc expression is attainable and not limiting to cellular growth. Transduction of a csc fast-growth locus into an isobutanol production strain also yielded high titer with significant sustainability benefits. This work demonstrated that random integration is a viable and effective strategy for optimizing heterologous expression within the context of cellular metabolism for certain desirable phenotypes. In the last part of my thesis, through life cycle assessment, I investigated multi-species algal polycultures, which are different yet related CO2-fixing microbial communities. Experimental studies have previously shown that algal polycultures can be designed to enhance biomass production, stability, and nutrient recycling compared to monocultures. However, it remains unclear whether these impacts of biodiversity make polycultures more sustainable than monocultures. I have conducted a comparative life cycle assessment which showed that when algae were grown in outdoor experimental ponds, certain bicultures improved the energy return on investment and greenhouse gas emissions substantially, compared to the best monoculture. Bicultures outperformed monocultures by performing multiple functions simultaneously (e.g., improved stability, nutrient efficiency, biocrude characteristics), which outweighed the higher productivity attainable by a monoculture. These results demonstrated that algal polycultures with optimized multi-functionality lead to enhanced life cycle metrics, highlighting the significant potential of ecological engineering for enabling future environmentally sustainable algal bio-refineries. Collectively, this dissertation demonstrates how CO2-fixing microbial communities may be engineered to enhance sustainability metrics compared to monocultures. By successfully engineering more sustainable bioproduction platforms, we move closer to a society with lower dependence on petrochemicals.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163222/1/dcarruth_1.pd

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research