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Multiple objectives are often used by agencies trying to manage pavement networks. Often alternative investment strategies can accomplish the agencies’ target objectives. If the goal is to achieve the target objectives at the minimum cost, an approach is needed to assist agencies in identifying investment strategies capable of meeting the targets while minimizing costs. The approach used by the agency should not be limited to an analytical method to mathematically solve the funding allocation problem. Finding mechanisms to ensure the sustainability and efficiency of the investment strategy over time is a great challenge that needs to be addressed by the approach. The challenge is even greater for local agencies where resources are usually limited. This research develops a multi-objective strategic management approach oriented to improving decisions for pavement management practices in local agencies. In this approach, target objectives are tied to key pavement network parameters in the management process. A methodology to identify the best combination of projects to meet target objectives at the minimum cost while maximizing treatment effectiveness is provided as a result of the research. Concepts from the pavement management program (PMP) of the Metropolitan Transportation Commission (MTC) of the San Francisco Bay Area were used as a basis for developing the methodology. Four pavement network parameters are considered for setting the target objectives over the agency’s planning horizon: the average network pavement condition index (PCI), average network remaining life, percent of the pavement network in good condition, and percent of the pavement network in poor and very poor condition. Results from a case study show that funding allocation methods influence the allocation of preservation and rehabilitation funds among pavement network groups, affecting budget estimates and future condition of the pavement network. It is also concluded that the use of mechanisms that facilitate data integration and the flow of knowledge across management levels can contribute to making better informed decisions. Hence, the adoption of the multi-objective strategic pavement management approach developed in this dissertation should lead to identifying more efficient investment strategies for achieving the pavement network state desired by a local agency at a minimum cost.

In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights according to the Tchebycheff distance function. Some seed solutions at initial population and a crowding measure also help to achieve satisfactory results. In order to test the performance of our evolutionary algorithm, we use some continuous and combinatorial problems. The continuous test problems taken from the literature have special difficulties that an evolutionary algorithm has to deal with. Experimental results of our algorithm on these problems are provided. One of the combinatorial problems we address is the multi-objective knapsack problem. We carry out experiments on test data for this problem given in the literature. We work on two bi-criteria p-hub location problems and propose an evolutionary algorithm to approximate the Pareto frontiers of these problems. We test the performance of our algorithm on Turkish Postal System (PTT) data set (TPDS), AP (Australian Post) and CAB (US Civil Aeronautics Board) data sets. The main contribution of this thesis is in the field of developing a multi-objective evolutionary algorithm and applying it to a number of multi-objective continuous and combinatorial optimization problems.

Desirability Functions continue to attract attention of scientists and researchers working in the area of multi-response optimization. There are many versions of such functions, differing mainly in formulations of individual and overall desirability functions. Derringer and Suich&rsquo / s desirability functions being used throughout this thesis are still the most preferred ones in practice and many other versions are derived from these. On the other hand, they have a drawback of containing nondifferentiable points and, hence, being nonsmooth. Current approaches to their optimization, which are based on derivative-free search techniques and modification of the functions by higher-degree polynomials, need to be diversified considering opportunities offered by modern nonlinear (global) optimization techniques and related softwares. A first motivation of this work is to develop a new efficient solution strategy for the maximization of overall desirability functions which comes out to be a nonsmooth composite constrained optimization problem by nonsmooth optimization methods. We observe that individual desirability functions used in practical computations are of mintype, a subclass of continuous selection functions. To reveal the mechanism that gives rise to a variation in the piecewise structure of desirability functions used in practice, we concentrate on a component-wise and generically piecewise min-type functions and, later on, max-type functions. It is our second motivation to analyze the structural and topological properties of desirability functions via piecewise max-type functions. In this thesis, we introduce adjusted desirability functions based on a reformulation of the individual desirability functions by a binary integer variable in order to deal with their piecewise definition. We define a constraint on the binary variable to obtain a continuous optimization problem of a nonlinear objective function including nondifferentiable points with the constraints of bounds for factors and responses. After describing the adjusted desirability functions on two well-known problems from the literature, we implement modified subgradient algorithm (MSG) in GAMS incorporating to CONOPT solver of GAMS software for solving the corresponding optimization problems. Moreover, BARON solver of GAMS is used to solve these optimization problems including adjusted desirability functions. Numerical applications with BARON show that this is a more efficient alternative solution strategy than the current desirability maximization approaches. We apply negative logarithm to the desirability functions and consider the properties of the resulting functions when they include more than one nondifferentiable point. With this approach we reveal the structure of the functions and employ the piecewise max-type functions as generalized desirability functions (GDFs). We introduce a suitable finite partitioning procedure of the individual functions over their compact and connected interval that yield our so-called GDFs. Hence, we construct GDFs with piecewise max-type functions which have efficient structural and topological properties. We present the structural stability, optimality and constraint qualification properties of GDFs using that of max-type functions. As a by-product of our GDF study, we develop a new method called two-stage (bilevel) approach for multi-objective optimization problems, based on a separation of the parameters: in y-space (optimization) and in x-space (representation). This approach is about calculating the factor variables corresponding to the ideal solutions of each individual functions in y, and then finding a set of compromised solutions in x by considering the convex hull of the ideal factors. This is an early attempt of a new multi-objective optimization method. Our first results show that global optimum of the overall problem may not be an element of the set of compromised solution. The overall problem in both x and y is extended to a new refined (disjunctive) generalized semi-infinite problem, herewith analyzing the stability and robustness properties of the objective function. In this course, we introduce the so-called robust optimization of desirability functions for the cases when response models contain uncertainty. Throughout this thesis, we give several modifications and extensions of the optimization problem of overall desirability functions.

<p>Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several different types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.</p>

Modern mobile devices incorporate several transmit and receive antennas in highly constrained volumes. As miniaturized antennas impinge upon fundamental physical limits on efficiency, new design approaches are required to support ever-smaller devices with more varied and robust communication performance. We take an unconventional design approach in which an arbitrary metallic structure and its components can be modified to act as efficient radiators. Using eigenmode analysis and the theory of characteristic modes (TCM), we develop algorithms that allow for effective integration of antennas with mechanical structures and enable structure reuse, helping meet stringent space and weight constraints without sacrificing electromagnetic performance. We derive TCM-based objectives for effective exploration of the design space in the electromagnetic (EM) domain. The procedure includes a feed placement technique that identifies viable excitation points on the structure without running full EM analysis. In addition to computational advantages, this provides a point of comparison among a variety of antenna shapes. Empirical evaluation shows that the estimates of radiated power from TCM can effectively guide optimization toward structures with improved radiating properties. Automated feed placement increases the proportion of good-quality designs among the explored candidates by consistently selecting the most promising feed positions. The ability of the TCM-based algorithm to direct the search is further validated on two real-world applications: integration of a GPS antenna with the frame of a mobile phone and integration of an S-band antenna with the frame of a small spacecraft. To the best of our knowledge, this is the first work that applies TCM to automated optimization of antennas. We investigate how to leverage domain-specific methods and solution representations in the coupled optimization of antennas. We develop a novel multiobjective optimization framework based on local search in each domain. In this procedure, the local optima in each objective are obtained and modified to create a new population of candidate designs. On a number of benchmark problems, the proposed technique is competitive with leading multi-objective algorithms: while it finds a less uniform distribution along the Pareto front, it shows better performance in locating solutions at the boundaries of the tradeoff curve. The local search algorithm is successfully applied to topology optimization of an antenna for a CubeSat, a small low-cost satellite platform.

This research has been defined in response to the Winnipeg children's hospital (WCH) challenges such as long waiting times, delays and cancellations in surgical flow. Preliminary studies on the surgical flow revealed that definition and implementation of successful process improvement projects (PIPs) along with application of an efficient master surgical schedule (MSS) are efficient solutions to the critical problems in WCH. In the first phase of this work, a process improvement program including three major PIPs, is defined and implemented in WCH in order to improve the efficiency of the processes providing surgical service for patients. In the second phase, two new multi-objective mathematical models are presented to develop efficient MSSs for operating room department (OR) in WCH.

Every production system has its constraints. Ever since Goldratt presented the theory of constraints in the mid 80’s a lot of effort has been made to find the best methods for constraint identification and ways to minimize the constraints in order to gain higher capacity in production. A novel method presented is Simulation-based COnstraint Removal (SCORE). The SCORE method has been proved to be more effective and detailed in the identification and sorting of the constraints when compared with other bottleneck detection methods (Pehrsson 2013). The work in this bachelor’s project has been focused on applying the method to a complex production system in order to further explore the SCORE method’s ability to identify bottlenecks and reveal opportunities to increase the throughput of a production system. NorthStar Battery Company (NSB) wishes to perform a bottleneck analysis and optimization in order to find improvements to increase the throughput with 10%. By using the SCORE method, improvement options with a potential to meet the goals of NSB was identified. It also facilitated for the author to further exploit the possibilities of simulation-based optimization and knowledge extraction through the SCORE method. By building a valid discrete event simulation model of the production line and use it for optimization, followed by a knowledge extraction, it was possible to identify the top three constraints and the level of improvement needed in the constraining operations. The identified improvements could potentially increase the throughput of the production line by 10-15 percent. The project was delimited to exclude the finishing part of the production line and only one battery variant has been included. Through continued work and analysis of the line using the SCORE method it will most likely be possible to even further increase the throughput of the production system and to provide NSB with more knowledge and opportunities to enhance their production effectiveness.

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.

Power converter has played a very important role in modern electric power systems. The control of power converters is necessary to achieve high performance. In this study, a dc-dc buck converter is studied. The parameters of a notional proportional-integral controller are to be selected. Genetic algorithms (GAs), which have been widely used to solve multi-objective optimization problems, is used in order to locate appropriate controller design. The control metrics are specified as phase margin in frequency domain and voltage error in time-domain. GAs presented the optimal tradeoffs between these two objectives. Three candidate control designs are studied in simulation and experimentally. There is some agreement between the experimental results and the simulation results, but there are also some discrepancies due to model error. Overall, the use of multi-domain, multi-objective-optimization-based approach has proven feasible.