Water resources managers rely on information collected by hydrometric networks without a quantitative way to assess their efficiency, and most Canadian water monitoring networks still do not meet the minimum density requirements. There is also no established way to quantify the importance of each existing station in a hydrometric network. This research examines the properties of Combined Regionalization Dual Entropy Multi-Objective Optimization (CR-DEMO), a robust network design technique which combines the merits of information theory and multi-objective optimization. Another information theory based method called transinformation (TI) which can rank the contribution of unique information from each specific hydrometric station in the network is tested for use with CR-DEMO. When used in conjunction, these methods can not only provide an objective measure of network efficiency and the relative importance of each station, but also allow the user to make recommendations to improve existing hydrometric networks across Canada. The Ottawa River Basin, a major Canadian watershed in Ontario and Quebec, was selected for analysis. Various regionalization methods which could be used in CR-DEMO such as distance weighting and a rainfall runoff model were compared in a leave one out cross validation. The effect of removing stations with regulated and unnatural flow regimes from the regionalization process is also tested. The analysis is repeated on a smaller tributary of the Ottawa River Basin, the Madawaska Watershed, to examine scale effects in TI analysis and CR-DEMO application. In this study, tests were conducted to determine whether to include stations outside of the river basin in order to provide more context to the basin boundaries. It was found that the TI analysis complemented CR-DEMO well and it provided a detailed station ranking which was supported by CR-DEMO results. The inverse distance weighting drainage area ratio method was found to provide more accurate regionalization results compared to the rainfall-runoff model, and was thus chosen for CR-DEMO. Regionalization was shown to be more accurate when the regulated basins were omitted using leave one out cross validation. It was discovered that CR-DEMO is sensitive to scaling because some sub-basins which are relatively “well-equipped” compared to others in dire conditions may be penalized. The TI analysis was not as sensitive to scaling. Including stations outside of the Ottawa River Basin improved the information density and regionalization accuracy in the Madawaska Watershed because they provided context to sparse areas. Finally, Pareto optimal network solutions for both the Ottawa River Basin and the Madawaska Watershed were presented and analyzed. A number of optimal networks are proposed for each watershed along with “hot-spots” where new stations should be added whatever the end users’ choice of network. / Thesis / Master of Applied Science (MASc)

Exposure to severe levels of hand-arm vibration can lead to hand-arm vibration syndrome. Towards curbing the development of hand-arm vibration syndrome, studies have shown that anti-vibration gloves effectively reduce the transmission of unwanted vibration from vibrating equipment to the human hand. However, most of these studies have focused on the study of anti-vibration gloves for power tools such as chipping hammers, and not much work has been done to design anti-vibration gloves for steering wheels. Also, as most of these studies are based on experimental or modeling techniques, the level of effectiveness and optimum glove properties for better performance remains unclear. To fill this gap, the dynamics of the hand-arm system, with and without gloves, coupled to a steering wheel is studied analytically in this work. A lumped parameter model of the hand-arm system with hand-tool interaction is modeled as a linear spring-damper system. The model is validated by comparing transmissibility obtained numerically to transmissibility obtained from experiments. The resulting governing equations of motion are solved analytically using the method of undetermined coefficients. Parametric analysis is performed on the biomechanical model of the hand-arm system with and without a glove to identify key design parameters. It is observed that the effect of glove parameters on its performance varies based on the frequency range. This observation further motivates us to optimize the glove parameters, using multi-objective optimization, to minimize the overall transmissibility in different frequency ranges. / Master of Science / When the human hand is exposed for a long time to vibrations generated from hand-held tools, such as Jack-hammers, rock breakers and chipping hammers, humans are in danger of developing hand-arm vibration syndrome. Hand-arm vibration syndrome is dangerous as severe episodes of this syndrome could lead to gangrene and eventually amputation of the fingers. To prevent the occurrence of hand-arm vibration syndrome, some researchers have explored the effectiveness of anti-vibration gloves through experiments. However, no work has been performed to identify the optimal glove design that best optimizes an anti-vibration glove for steering wheel applications. To address this issue, this thesis studied a mathematical model of the human-hand, wearing an anti-vibration glove attached to a steering wheel system. To ensure this model could successfully replicate real life applications, measurements computed with the model were compared with measurements on the human-hand obtained from experiments. After successfully ensuring that the model closely replicated real-life measurements, the model was used to design an Anti-vibration glove with optimal values to protect the hand from hand-arm vibration syndrome.

In a world where calls for climate action grow louder by the day, the role of renewable energy and energy efficient generation sources has become extremely important. One such energy efficient resource that can increase the penetration of renewable energy into the grid is the Combined Heat and Power technology. Combined Heat and Power (CHP) plants produce useful thermal and electrical power output from a single input fuel source and are widely used in the industrial and commercial sectors for reliable on-site power production. However, several unfavorable policies combined with inconsistent regulations have discouraged investments in this technology and reduced participation of such facilities in grid operations. The potential benefits that could be offered by this technology are numerous - improving grid resiliency during emergencies, deferring transmission system updates and reducing toxic emissions, to name a few. With increased share of renewable energy sources in the generation mix, there is a pressing need for reliable base generation that can meet the grid requirements without contributing negatively to the environment. Since CHP units are good candidates to help achieve this two-fold requirement, it is important to understand the present barriers to their deployment and grid involvement. In this thesis work, we explore some of these challenges and propose suitable grid integration technology as well as market participation approaches for better involvement of distributed CHP units in the industrial and commercial sectors. / Master of Science / Combined Heat and Power is a generation technology which uses a single fuel source to produce two useful outputs - electric power and thermal energy - by capturing and reusing the exhaust steam by-product. These generating units have much higher efficiencies than conventional power plants, lower fuel emissions and have been a popular choice among several industries and commercial buildings with a need for uninterrupted heat and power. With increasing calls for climate action and large scale deployment of renewable based energy generation sources, there is a higher need for reliable base-line generation which can handle the fluctuations and uncertainty of such renewables. This need can be met by CHP units owing to their geographic distribution and their high operating duration. CHPs also provide a myriad of other benefits for the grid operators and environmental benefits, compared to the conventional generators. However, unfavorable and inconsistent regulatory procedures have discouraged these facility owners from actively engaging in providing grid services. Therefore, it is imperative to look into some of the existing policies and understand where the changes and incentives need to be made. In this work, we look into methods that can ease CHP integration from a technological and an economic point of view, with the aim of encouraging grid operators and CHP owners to be more active participants.

The Levenberg-Marquardt algorithm is a classical method for solving nonlinear systems of equations that can come from various applications in engineering and economics. Recently, Levenberg-Marquardt methods turned out to be a valuable principle for obtaining fast convergence to a solution of the nonlinear system if the classical nonsingularity assumption is replaced by a weaker error bound condition. In this way also problems with nonisolated solutions can be treated successfully. Such problems increasingly arise in engineering applications and in mathematical programming. In this thesis we use Levenberg-Marquardt algorithms to deal with nonlinear equations, multi-objective optimization and complementarity problems. We develop new algorithms for solving these problems and investigate their convergence properties. For sufficiently smooth nonlinear equations we provide convergence results for inexact Levenberg-Marquardt type algorithms. In particular, a sharp bound on the maximal level of inexactness that is sufficient for a quadratic (or a superlinear) rate of convergence is derived. Moreover, the theory developed is used to show quadratic convergence of a robust projected Levenberg-Marquardt algorithm. The use of Levenberg-Marquardt type algorithms for unconstrained multi-objective optimization problems is investigated in detail. In particular, two globally and locally quadratically convergent algorithms for these problems are developed. Moreover, assumptions under which the error bound condition for a Pareto-critical system is fulfilled are derived. We also treat nonsmooth equations arising from reformulating complementarity problems by means of NCP functions. For these reformulations, we show that existing smoothness conditions are not satisfied at degenerate solutions. Moreover, we derive new results for positively homogeneous functions. The latter results are used to show that appropriate weaker smoothness conditions (enabling a local Q-quadratic rate of convergence) hold for certain reformulations. / Der Levenberg-Marquardt-Algorithmus ist ein klassisches Verfahren zur Lösung von nichtlinearen Gleichungssystemen, welches in verschiedenen Anwendungen der Ingenieur-und Wirtschaftswissenschaften vorkommen kann. Kürzlich, erwies sich das Verfahren als ein wertvolles Instrument für die Gewährleistung einer schnelleren Konvergenz für eine Lösung des nichtlinearen Systems, wenn die klassische nichtsinguläre Annahme durch eine schwächere Fehlerschranke der eingebundenen Bedingung ersetzt wird. Auf diese Weise, lassen sich ebenfalls Probleme mit nicht isolierten Lösungen erfolgreich behandeln. Solche Probleme ergeben sich zunehmend in den praktischen, ingenieurwissenschaftlichen Anwendungen und in der mathematischen Programmierung. In dieser Arbeit verwenden wir Levenberg-Marquardt- Algorithmus für nichtlinearere Gleichungen, multikriterielle Optimierung - und nichtlineare Komplementaritätsprobleme. Wir entwickeln neue Algorithmen zur Lösung dieser Probleme und untersuchen ihre Konvergenzeigenschaften. Für ausreichend differenzierbare nichtlineare Gleichungen, analysieren und bieten wir Konvergenzergebnisse für ungenaue Levenberg-Marquardt-Algorithmen Typen. Insbesondere, bieten wir eine strenge Schranke für die maximale Höhe der Ungenauigkeit, die ausreichend ist für eine quadratische (oder eine superlineare) Rate der Konvergenz. Darüber hinaus, die entwickelte Theorie wird verwendet, um quadratische Konvergenz eines robusten projizierten Levenberg-Marquardt-Algorithmus zu zeigen. Die Verwendung von Levenberg-Marquardt-Algorithmen Typen für unbeschränkte multikriterielle Optimierungsprobleme im Detail zu untersucht. Insbesondere sind zwei globale und lokale quadratische konvergente Algorithmen für multikriterielle Optimierungsprobleme entwickelt worden. Die Annahmen wurden hergeleitet, unter welche die Fehlerschranke der eingebundenen Bedingung für ein Pareto-kritisches System erfüllt ist. Wir behandeln auch nicht differenzierbare nichtlineare Gleichungen aus Umformulierung der nichtlinearen Komplementaritätsprobleme durch NCP-Funktionen. Wir zeigen für diese Umformulierungen, dass die bestehenden differenzierbaren Bedingungen nicht zufrieden mit degenerierten Lösungen sind. Außerdem, leiten wir neue Ergebnisse für positiv homogene NCP-Funktionen. Letztere Ergebnisse werden verwendet um zu zeigen, dass geeignete schwächeren differenzierbare Bedingungen (so dass eine lokale Q-quadratische Konvergenzgeschwindigkeit ermöglichen) für bestimmte Umformulierungen gelten.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das / Anil Pahwa / The task of short-term optimal thermal generation scheduling can be cast in the form of a multi-objective optimization problem. The goal is to determine an optimal operating strategy to operate power plants, in such a way that certain objective functions related to economic and environmental issues, as well as transmission losses are minimized, under typical system and operating constraints. Due to the problem’s inherent complexity, and the large number of associated constraints, standard multi-objective optimization algorithms fail to yield optimal solutions. In this dissertation, a novel, two-phase multi-objective evolutionary approach is proposed to address the short-term optimal thermal generation scheduling problem. The objective functions, which are based on operation cost, emission and transmission losses, are minimized simultaneously. During the first phase of this approach, hourly optimal dispatches for each period are obtained separately, by minimizing the operation cost, emission and transmission losses simultaneously. The constraints applied to this phase are the power balance, spinning reserve and power generation limits. Three well known multi-objective evolutionary algorithms, NSGA-II, SPEA-2 and AMOSA, are modified, and several new features are added. This hourly schedule phase also includes a repair scheme that is used to meet the constraint requirements of power generation limits for each unit as well as balancing load with generation. The new approach leads to a set of highly optimal solutions with guaranteed feasibility. This phase is applied separately to each hour long period. In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and another improved version of the three multi-objective evolutionary algorithms, are used again to obtain a set of Pareto-optimal schedules for the integral interval of time (24 hours). During this phase, the hourly optimal schedules that are obtained from the first phase are used as inputs. A bi-objective version of the problem, as well as a three-objective version that includes transmission losses as an objective, are studied. Simulation results on four test systems indicate that even though NSGA-II achieved the best performance for the two-objective model, the improved AMOSA, with new features of crossover, mutation and diversity preservation, outperformed NSGA-II and SPEA-2 for the three-objective model. It is also shown that the proposed approach is effective in addressing the multi-objective generation dispatch problem, obtaining a set of optimal solutions that account for trade-offs between multiple objectives. This feature allows much greater flexibility in decision-making. Since all the solutions are non-dominated, the choice of a final 24-hour schedule depends on the plant operator’s preference and practical operating conditions. The proposed two-phase evolutionary approach also provides a general frame work for some other multi-objective problems relating to power generation as well as in other real world applications.

This dissertation addresses resource allocation problems that occur in both public and private health care settings with the objective of characterizing the tradeoffs that occur when simultaneously incorporating multiple objectives and developing methods to address these tradeoffs. We examine three resource allocation problems (i) strategic allocation of financial resources and limited staffing capacity for the mobile delivery of health care within African countries, (ii) real-time allocation of hospital beds to internal patient requests, and (iii) development of patient redirection policies in response to limited bed availability in units within a system of hospitals. For each problem we define models, each with a different methodology, and utilize the models to develop allocation strategies that account for multiple competing objectives and examine the performance of the strategies with computational studies. In Chapter 2, we model African health care delivery systems utilizing a mixed-integer program (MIP) which accounts for financial and personnel constraints as well as infrastructure quality. We characterize tradeoffs in effectiveness, efficiency, and equity resulting from four allocation strategies with computational experiments representing the variety of spatial patterns that occur throughout the continent. The main contributions include (i) the development of a model that incorporates spatial and infrastructure characteristics and allows for a study of equity in the delivery of care, rather than access to care, and (ii) the characterization of tradeoffs in the three objectives under a variety of settings. In Chapter 3, we model the real-time assignment of bed requests to available beds as a queueing system and a Markov decision process (MDP). Through the development of bed assignment algorithms and simulation experiments, we illustrate the value of implementing strategic bed assignment practices which balance the bed management objectives of timeliness and appropriateness of assignments. The main contributions of this section include (i) the development of new bed assignment algorithms which use stochastic optimization techniques and outperform algorithms which mimic processes currently used in practice and (ii) the definition of a model and methods for the control of a large complex system that includes flexible units, multiple patient types, and type-dependent routing. In Chapter 4, we model the impact of a patient redirection policy in a hospital unit as a Markov chain. Assuming preferences for patient redirection are aligned with costs, we examine the impact of incremental changes to redirection policies on the probability of the unit being completely occupied, the long-run average utilization, and the long-run average cost of redirection. The main contributions of this chapter include (i) the introduction of a model of patient redirection with multiple patient thresholds and patient preference constraints and (ii) the definition of necessary conditions for an optimal patient redirection policy that minimizes the average cost of redirection.

In this thesis, the COMAC ARJ21 fuselage’s final assembly process is used as a case study. High production rate (i.e. number of aircraft assembled per year) with reasonable cost is the overall aim in this example. The output of final assembly will essentially affect the prior and subsequent processes of the overall ARJ21 production. From the collected field data, it was identified that a number of disruptions (or bottlenecks) in the assembly sequence were caused by breakdowns and maintenance of the (semi-)automatic assembly machines like portable computer numerical control (CNC) drilling machine, rivet gun and overhead crane. The focus of this thesis is therefore on the maintenance strategies (i.e. Condition-Based Maintenance (CBM)) for these equipment and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modelled and analysed by using agent-based simulation in this thesis. The agent approach allows complex process interactions of assembly, equipment and maintenance to be captured and empirically studied. In this thesis, the built network is modelled as the sequence of activities in each stage. Each stage is broken down into critical activities which are parameterized by activity lead-time and equipment used. CBM based models of uncertain degradation and imperfect maintenance are used in the simulation study. A scatter search is used to find multi-objective optimal solutions for the CBM regime, where the maintenance-related cost and production rate are the optimization objectives. In this thesis, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multi-objective formulation, multiple Min-Max weightings are applied to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how these objectives are influenced by the design parameters.

An option, a type of a financial derivative, is a contract that creates an opportunity for a market player to avoid risks involved in investing, especially in equities. An investor desires to know the accurate value of an option before entering into a contract to buy/sell the underlying asset (stock). There are various techniques that try to simulate real market conditions in order to price or evaluate an option. However, most of them achieved limited success due to high uncertainty in price behavior of the underlying asset. In this study, I propose two new Firefly variant algorithms to compute accurate worth for European and American option contracts and compare them with popular option pricing models (such as Black-Scholes-Merton, binomial lattice, Monte-Carlo, etc.) and real market data. In my study, I have first modelled the option pricing as a multi-objective optimization problem, where I introduced the pay-off and probability of achieving that pay-off as the main optimization objectives. Then, I proposed to use a latest nature-inspired algorithm that uses the bioluminescence of Fireflies to simulate the market conditions, a first attempt in the literature. For my thesis, I have proposed adaptive weighted-sum based Firefly algorithm and non-dominant sorting Firefly algorithm to find Pareto optimal solutions for the option pricing problem. Using my algorithm(s), I have successfully computed complete Pareto front of option prices for a number of option contracts from the real market (Bloomberg data). Also, I have shown that one of the points on the Pareto front represents the option value within 1-2 % error of the real data (Bloomberg). Moreover, with my experiments, I have shown that any investor may utilize the results in the Pareto fronts for deciding to get into an option contract and can evaluate the worth of a contract tuned to their risk ability. This implies that my proposed multi-objective model and Firefly algorithm could be used in real markets for pricing options at different levels of accuracy. To the best of my knowledge, modelling option pricing problem as a multi-objective optimization problem and using newly developed Firefly algorithm for solving it is unique and novel. / October 2016

The optimal design of geothermal power plants across the entire spectrum of meaningful geothermal brine temperatures and climates is investigated, while accounting for vital real-world constraints that are typically ignored in the existing literature. The constrained design space of both double-flash and binary geothermal power plants is visualized, and it is seen that inclusion of real-world constraints is vital to determining the optimal feasible design of a geothermal power plant. The effect of varying condenser temperature on optimum plant performance and optimal design specifications is analyzed. It is shown that condenser temperature has a significant effect on optimal plant design as well. The optimum specific work output and corresponding optimal design of geothermal power plants across the entire range of brine temperatures and condenser temperatures is illustrated and tabulated, allowing a scientifically sound assessment of both feasibility and appropriate plant design under any set of conditions. The performance of genetic algorithms and particle swarm optimization are compared with respect to the constrained, non-linear, simulation-based optimization of a prototypical geothermal power plant, and particle swarm optimization is shown to perform significantly better than genetic algorithms. The Pareto-optimal front of specific work output and specific heat exchanger area is visualized and tabulated for binary and double-flash plants across the full range of potential geothermal brine inlet conditions and climates, allowing investigation of the specific trade-offs required between specific work output and specific heat exchanger area. In addition to the novel data, this dissertation research illustrates the development and use of a sophisticated analysis tool, based on multi-objective particle swarm optimization, for the optimal design of geothermal power plants.

Cette thèse se focalise sur la proposition et l’application des approches pour la modélisation de l’éco-fabrication. Ces approches permettent de préparer et simuler une démarche de fabrication des produits en assurant le couplage entre les objectifs écologiques et économiques.Les approches développées dans cette thèse sont basées sur les notions d’aide à la décision ainsi que l’optimisation multi objectifs. L’aide à la décision permet l’intervention en deux différents niveaux : le choix des impacts environnementaux à quantifier ainsi que le choix du scénario final de fabrication. Pour l’optimisation multi objectifs, elle assure le couplage entre les deux piliers principaux de l’éco-fabrication : l’écologie et l’économie. Au niveau de l’aide à la décision multi critères, les méthodes Evamix et Promethee ont été appliqués, tandis que les essaims particulaires ont été développés dans le cadre de l’optimisation multi objectifs.Ces approches ont été appliquées tout d’abord aux quelques opérations d’usinage : tournage et fraisage. Finalement, la chaîne de fabrication de l’acide phosphorique ainsi que celle d’acide sulfurique ont été le sujet de l’application des deux approches développées. / This thesis focuses on the proposal and implementation of approaches for modeling sustainable manufacturing. These approaches are used to prepare and simulate a process of manufacturing products providing coupling between environmental and economic objectives.The approaches developed in this thesis are based on the concepts of decision support as well as multi-objective optimization. The decision support allows intervention in two different levels: the choice of indicator to quantify the environmental impacts and the choice of the final manufacturing scenario. For multi-objective optimization, it provides the coupling between the two main pillars of sustainable manufacturing: ecology and economy. In terms of multi criteria decision aid methods, Evamix and Promethee were applied, while particulate swarms were developed as part of the multi-objective optimization. These approaches have been applied initially to some machining operations: turning and milling. Finally, the production line of phosphoric acid and sulfuric acid were the subject of application of the two approaches developed.