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Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense.

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In legacy software, non-functional concerns tend to cut across the system and manifest themselves as tangled or scattered code. If these crosscutting concerns could be modularized and the system refactored, then the system would become easier to understand, modify, and maintain. Modularized crosscutting concerns are known as aspects and the process of identifying aspect candidates in legacy software is called aspect mining. One of the techniques used in aspect mining is clustering and there are many clustering algorithms. Current aspect mining clustering algorithms attempt to form clusters by optimizing one objective function. However, the objective function to be optimized tends to bias the formation of clusters towards the data model implicitly defined by that function. One solution is to use algorithms that try to optimize more than one objective function. These multiobjective algorithms have been used successfully in data mining but, as far as this author knows, have not been applied to aspect mining. This study investigated the feasibility of using multiobjective evolutionary algorithms, in particular, multiobjective genetic algorithms, in aspect mining. The study utilized an existing multiobjective genetic algorithm, MOCK, which had already been tested against several popular single objective clustering algorithms. MOCK has been shown to be, on average, as good as, and sometimes better than, those algorithms. Since some of those data mining algorithms have counterparts in aspect mining, it was reasonable to assume that MOCK would perform at least as good in an aspect mining context. Since MOCK's objective functions were not directly trying to optimize aspect mining metrics, the study also implemented another multiobjective genetic algorithm, AMMOC, based on MOCK but tailored to optimize those metrics. The reasoning hinged on the fact that, since the goal was to determine if a clustering method resulted in optimizing these quality metrics, it made sense to attempt to optimize these functions directly instead of a posteriori. This study determined that these multiobjective algorithms performed at least as good as two popular aspect mining algorithms, k-means and hierarchical agglomerative. As a result, this study has contributed to both the theoretical body of knowledge in the field of aspect mining as well as provide a practical tool for the field.

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

Ces travaux proposent une méthode d’assistance à la sélection des techniques d’assemblage et à l’allocation de tolérances sur les écarts géométriques des composants dans le cadre de l’assemblage de structures aéronautiques composites. Cette méthode consiste à formuler et à résoudre un problème d’optimisation multiobjectif afin de minimiser un indicateur de cout et un indicateur de non-conformité des structures assemblées. L’indicateur de coût proposé prend en compte le coût associé à l’allocation des tolérances géométriques ainsi que le coût associé aux opérations d’assemblage. Les indicateurs de non-conformités proposés sont évalués à partir des probabilités de non-respect des exigences géométriques sur les structures assemblées. Ces probabilités sont évaluées en propageant les tolérances géométriques allouées et les dispersions des techniques sélectionnées au travers d’une fonction appelée Relation de Comportement de l’assemblage (RdCa). Dans le cas de l’assemblage de structures aéronautiques composites, des exigences peuvent porter sur les jeux aux interfaces entre composants. Dans ce cas, la RdCa est évaluée par la résolution d’un problème mécanique quasi-statique non-linéaire par la méthode des éléments finis. Un méta-modèle de la RdCa est construit afin de la rendre compatible avec les méthodes probabilistes utilisées pour évaluer la non-conformité. Finalement, la définition d’un modèle structuro-fonctionnel du produit et d’une bibliothèque de techniques d’assemblage permet de construire un avant-projet de gamme d’assemblage paramétrique. Ce paramétrage permet de formuler le problème d’optimisation multiobjectif résolu à l’aide d’un algorithme génétique. / The purpose of this PhD is to develop a method to assist assembly technique selection and component geometrical tolerance allocation in the context of composite aeronautical structure assembly. The proposed method consists in formulating and solving a multiobjective optimisation problem aiming at minimising a cost indicator and a non-conformity indicator. The cost indicator account for both the cost involved by the geometrical tolerance allocation and the cost associated with the assembly operations. The proposed non-conformity indicators are evaluated according to the probabilities of non-satisfied requirements on the assembled structures. These probabilities are computed thanks to Geometrical Variation Propagation Relation (GVPR) that expresses the characteristics of the product as a function of the geometrical deviation of the components and the dispersion occurring during the assembly. In the case of composite aeronautical structures, the product characteristics can be gaps at interfaces between components. In this case, the GVPR is evaluated by solving a non-linear quasi-static mechanical problem by the mean of the finite element method. A metamodel of the GVPR is built in order to reduce the computing time and to make it compatible with the probabilistic methods used to evaluate the non-conformity. Finally, the use of a structure-functional model of the product together with an assembly technique library allows defining a parametric assembly process plan. The multiobjective optimisation problem built thanks to set of parameters defining the assembly process plan is solved using a genetic algorithm.

With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide quality of services in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem using evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things. Accordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Schedule recovery Problem (VSRP) is reformulated and a bi-objective optimization approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimizing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their performance significance. According to the experiments, the problem complexity varies in different scenarios, while NSGAII performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimization problem called G-S-VSRP with three objectives (i.e., minimizing loss, delay, and maximizing compliance) where the compliance objective maximizes the compliance of optimized speeds with the historical data. Assuming that the historical speed profiles are reliable to trust for actual operational speeds based on other ships' experience, maximizing the compliance of optimized speeds with these historical data offers some degree of avoiding risks. Three MOEAs tackled the problem and provided the stakeholder with a Pareto front which reflects the trade-off among the three objectives. Geohash granularity and dimensionality reduction techniques were evaluated and discussed for the model. G-S-VSRPis a large-scale optimization problem and suffers from the curse of dimensionality (i.e. problems are difficult to solve due to exponential growth in the size of the multi-dimensional solution space), however, due to a special characteristic of the problem instance, a large number of function evaluations in MOEAs can still find a good set of solutions. Finally, when the compliance objective in G-S-VSRP is changed to minimization, the regular MOEAs perform poorly due to the curse of dimensionality. We focus on improving the performance of the large-scale G-S-VSRP through a novel distributed multiobjective cooperative coevolution algorithm (DMOCCA). The proposed DMOCCA improves the quality of performance metrics compared to the regular MOEAs (i.e. NSGAII, NSGAIII, and GDE3). Additionally, the DMOCCA results in speedup when running on a cluster.

A produção de alimentos, de grande importância para a humanidade, é realizada por meio de processos produtivos muitas vezes responsáveis pelo consumo desordenado de recursos naturais. Tais atividades também são consideradas de alto risco econômico, pois utilizam, em sua maioria, técnicas de baixo rendimento, culturas inapropriadas e até mesmo a utilização de técnicas avançadas, sem o devido controle, como a irrigação, acarretam desperdício de recursos. O uso de técnicas multiobjetivo permite um ordenamento de sistemas produtivos sendo capaz de levar em conta critérios muitas vezes conflitantes e essenciais para a produção agrícola, como: retorno econômico da atividade ao produtor, impacto ambiental, geração de emprego e aproveitamento da mão de obra. O objetivo do presente trabalho é utilizar a técnica multiobjetivo SPEA - Strenght Pareto Evolutionary Algorithm - no auxílio ao produtor rural quanto à tomada de decisão, oferecendo uma alternativa para a gestão de processos produtivos em propriedades rurais do Estado de São Paulo. As técnicas multiobjetivo foram empregadas para obter a ordenação em relação ao tipo de sistema de irrigação a ser adotado, cultura de frutíferas a ser empregada e seus possíveis desempenhos. Investiga-se também o impacto do valor unitário a ser aplicado pelo uso da água de irrigação. Os resultados obtidos permitiram confirmar que a técnica multiobjetivo utilizada se mostrou adequada para as condições adotadas neste trabalho, sendo a cultura da atemóia e o sistema de irrigação por gotejamento as que mais se destacaram em termos de viabilidade econômica de acordo com os parâmetros utilizados. / Food production, of great importance to humanity, is realized through agricultural activities, responsible for disorderly consumption of natural resources. Those activities are considered of high economic risk, for using low yield techniques, inappropriate crops, and even using advanced techniques, such as irrigation, without proper control, leading to natural resources waste. The use of multiobjective techniques allows the ordering of productive systems, being able to consider conflicting criteria such as: economic income, environmental impact, employment generation and workforce utilization. The objective of this study is to use the multiobjective optimization technique SPEA - Strenght Pareto Evolutionary Algorithm - to auxiliate rural producers concerning to decision making, offering an alternative for productive process management for small agricultural properties from São Paulo state, Brazil. The multiobjective techniques were used to obtain the ranking of irrigation system to be adopted, fruit crop and related performance. This work also studies the impact of unit cost to be applied for irrigation water. The results confirmed that the multiobjective technique used was appropriate to the actual conditions of this work, and atemoya crop in a drip irrigation the one that stood out as the best economic performance according to adopted parameters.

Diante dos desafios impostos pelo sistema econômico, características dos mercados e exigências dos clientes, as empresas são forçadas a operar com lotes de produção cada vez menores, dificultando a gestão de operações e a otimização dos sistemas produtivos. Desse modo, intensifica-se nos meios corporativos e acadêmicos a busca por abordagens que possibilitem a criação de diferenciais competitivos de mercado, sendo esta a justificativa prática deste trabalho, que propõe uma sistemática integrada para alocação, sequenciamento e balanceamento de lotes em um horizonte de programação em múltiplas linhas de produção em um sistema multiproduto com operadores polivalentes. A sistemática proposta foi dividida em três fases. A primeira fase utiliza um algoritmo genético multiobjetivo com o intuito de determinar a linha de produção em que cada lote será produzido. A segunda fase é responsável pelo sequenciamento dos lotes produtivos e se apoia em uma alteração da regra Apparent Tardiness Cost (ATC). Na terceira fase utilizou-se o método Ranked Positional Weight (RPW) para balancear a distribuição das tarefas entre os operadores polivalentes de cada linha de produção, respeitando a precedência das tarefas. A sistemática foi aplicada em dados reais do segmento têxtil, aprimorando os indicadores produtivos e de entrega e conferindo maior flexibilidade ao processo frente à demanda sazonal. / Faced with the challenges imposed by the economic system, characteristic of the markets and requirements of the customers, the companies are forced to operate with smaller production batches, making it difficult to manage operations and optimization of the production systems. In this way, the search for improvements that allow the creation of competitive differentials of market is intensified in the corporate and academic circles. This is the practical justification for this work, which proposes an integrated systematics for the allocation, sequencing and balancing of batches in a horizon of programming in multiple production lines in a multiproduct system with multipurpose operators. The systematic proposal was divided into three phases. The first phase uses a multiobjective genetic algorithm with intention to determine the production line in which each batch will be produced. The second phase is responsible for the sequencing of productive batches and is based on a change in the rule Apparent Tardiness Cost (ATC). In the third phase the method Ranked Positional Weight (RPW) was used to balance the distribution of the tasks between the multipurpose operators of each line of production, respecting the precedence of the tasks. The systematics was applied in real data of the textile segment, improving the productive and delivery indicators and giving greater flexibility of the process against the seasonal demand.