Orientador: Fernando Augusto da Silva Marins / Resumo: Em diversas áreas de trabalho, da Engenharia à Economia, os problemas se apresentam como sendo multiobjetivos, característica que torna complexa a tomada de decisão. Geralmente, estes objetivos são conflitantes e faz-se necessário o uso de técnicas de otimização para a obtenção de melhores resultados. Na presente dissertação serão estudados alguns métodos para a resolução destes problemas, com o objetivo de aplicar métodos de aglutinação em problemas de projetos de experimentos com múltiplas respostas. Deste modo, inicialmente foi realizada uma análise bibliométrica sobre os diferentes métodos utilizados para a resolução destes problemas. A partir disto, foi desenvolvida uma nova abordagem, utilizando a Programação por Compromisso (Compromise Programming – CP) e a Programação por Metas (Goal Programming – GP), bem como diferentes algoritmos (Gradiente Reduzido Generalizado – GRG e a metaheurística do software Optquest) que são usualmente adotados, com comparação de resultados e análise. De modo geral, esta nova proposta apresentou resultados melhores em relação à abordagem tradicional (desirability), qualificando este procedimento como uma alternativa na otimização de múltiplas respostas. / Mestre

Le présent travail s’intéresse à la prise en compte de l’incertitude et du risque pour l’optimisation de la planification de production au niveau tactique d’une entreprise multi-sites d’une chaîne logistique. La méthode proposée permet d’assurer une planification des opérations de production et d’approvisionnement tout en intégrant au sein de son processus décisionnel un mécanisme de gestion de risque, en présence de diverses sources d’incertitude et d’ambigüité. Pour cela, une «bibliothèque» de critères structurés en deux classes indépendantes : critères de performance et critères de risque a été proposée, dans laquelle le décideur peut sélectionner ceux qui sont en cohérence avec ses préférences et sa stratégie de planification. La méthode doit chercher le bon compromis entre les performances et les risques prédéfinis par le décideur. Pour cela, nous nous somme dirigés dans un premier temps sur le développement d’une approche d’aide à la décision multicritères floue couplant un modèle analytique et la méthode TOPSIS floue. Cette approche consiste à générer un éventail de plans réalisables, caractérisés par leur performance et leur résistance aux risques. Le décideur peut alors choisir le plan qui reflète le compromis le plus adapté à sa stratégie de décision. Une deuxième approche d’optimisation multi-objectifs floue a été proposée dans un deuxième temps pour faire face à des problèmes de planification de grande taille au sein des chaînes logistiques opérant dans un environnement dynamique et incertain. Cette approche combine la méthode TOPSIS Floue, la programmation multi-objectifs possibiliste et la méthode du Goal Programming. L’objectif est de déterminer un plan jugé de bon compromis vis-à- vis des préférences du décideur par rapport aux objectifs de performance et de résistance aux risques. L’instanciation des deux approches proposées sur un exemple numérique a montré leur applicabilité et leur efficacité pour faire face à des problèmes de planification des chaînes logistiques utilisant des données incertaines et des préférences subjectives. Les expérimentations des deux approches permettant de tirer un ensemble d’enseignements utiles.

Multi-service outsourcing has become an important business approach since it can significantly reduce service cost, shorten waiting time, improve the customer satisfaction and enhance the firm’s core competence. In fact, on-demand cloud resources can lead manufacturers to improve their business processes and use an integrated and intelligent supply chain network. In addition, cloud manufacturing, as an emerging manufacturing system technology, will likely enable small and medium sized enterprises (SMEs) to move towards using dynamic scalability and ‘free’ available data resources in a virtual manner. Although there has been some research in these areas, there is still a lack of proper cloud based solutions for the whole manufacturing supply chain network. In addition, of the research papers studied, only a few reviewed and implemented the cloud based supply chain from a decision-making point of view, especially in suppliers evaluation and selection studies. Most studies only focused on cloud-based supply chain definitions, architectures, applications, advantages and limitations which can be offered to SMEs. Hence, a comprehensive research study to find an optimum set of suppliers for a number of goods and services required for a project within the cloud manufacturing context is necessary. Providing real and multi-way relationships through a suppliers selection process based on an intelligent cloud-based manufacturing supply chain network, by using the Internet, is the main aim of this research. The research has an emphasis on multi-criteria decision making approach. The proposed model is based on ‘Goal Integer 0-1 Programming’ method for the suppliers selection part and ‘Linear Programming’ method for the project planning part. The proposed framework consists of four modules, namely a) multi-criteria module, b) bidding module, c) optimisation module, and d) learning module. Learning module allows the model to learn about the suppliers’ past performance over the course of the system’s life. Average performance measures are calculated over a moving fixed period, results of which are stored in a ‘dynamic memory’ element as linked to the suppliers’ database. The methodological approach is validated based on a case study in the oil and gas industry, characterised by 29 services linked together in a network structure, 108 suppliers, and 128 proposals for the services. The case study covers a variety of services from designing to manufacturing and delivery. On the implementation side, a cloud manufacturing based suppliers selection system (OPTiSupply.uk®) is designed and uploaded on the virtual server of Amazon EC2. The system enables customers and suppliers to offer and receive various services on the Web. Apart from the user interface functionality, the system also allows interaction with the MS-Excel© based data and the associated mathematical programming.

Health care, and especially access to health care, has always been a critical metric for countries. In 2017, South Africa spent 9% of its GDP on health care. Despite the GDP health care allocation being 5% higher than recommended by the World Health Organisation for a country of its socio-economic status, South Africa's health status is poor compared to similar countries. In 1994, South Africa implemented a health care policy to make health care accessible to all South Africans. A primary health care facility within 5km of the place of residence is deemed accessible. There is still a significant gap between the actual and desired accessibility, especially for the lower-income communities. There is a need to improve access to public health care for all South Africans. Cost-effective and sustainable solutions are required to solve this problem. Therefore, an opportunity was identified to investigate the location of low-cost container clinics in lower-income communities. This report uses robust optimisation and goal programming to find robust sites for cost-effective container clinics over multiple years in an uncertain environment using multiple future city development scenarios. The study area of the report includes three metro municipalities (City of Tshwane, City of Johannesburg, and City of Ekurhuleni) in Gauteng, South Africa. Three future development scenarios were created for this study using a synthetic population and urban growth simulation model developed by the CSIR. The model provided the population distribution from 2018 to 2030 for all three of the scenarios. The simulation model provides household attribute tables as an output. Household attributes that have a causal relationship with health care demand were investigated during the literature review. Based on the literature and the available household attributes, four attributes were selected to forecast the health care demand. The four attributes are household income, the number of children in the household, the household size, and the nearest clinic's distance. Using associative forecasting, the primary health care demand was forecasted from 2018 to 2030. These forecasts were used as input into the facility location models. A p-median facility location model was developed and implemented in Python. Since facility location problems are classified as NP-hard problems, heuristics and metaheuristics were investigated to speed up the problem solving. A GA selected as the metaheuristic be used to determine a suitable configuration of facilities for each scenario. The model determined good locations of clinics from a set of candidate locations. A good year to open each clinic is also determined by the model. These decisions are made by minimising three variables: total distances travelled by the households to their nearest clinics, the total distance from the selected distribution centre to the open clinics and the total building cost. An accessibility target of 90% was added to the model to ensure that at least 90% of the households are within 5km of the nearest clinic within the first five years. In these models, operating costs were not included. Therefore all the results are skewed, with most of the clinics being opened in the first year when it is the cheapest since there is no penalty for opening a clinic before it is needed — the exclusion of operating costs is a shortcoming to address in future work. A goal programming model was developed with the variables of the individual scenarios as the goals. The goal programming model was implemented in Python and used to determine a robust configuration of where and in what year to open container clinics. A difference of 25% was set as the upper limit for the difference between the robust configuration variables and the good or acceptable variables for the individual scenarios as the scenarios investigated are very different. This ensured that the robust solution would perform well for any of the three scenarios. The model was able to find locations that provided a relatively good solution to all the scenarios. This came with a cost increase, but that is a trade-off that must be made when dealing with uncertainty. This model is a proof of concept to bridge the gap between urban planning with multiple development scenarios and facility location, more specifically robust facility location. The biggest rendement was achieved by constructing and placing the container clinics in the shortest space of time because the 90% accessibility requirement can be addressed cost-effectively without an operating cost penalty ― this is unfortunately not possible in reality due to budget constraints. An accessibility analysis was conducted to investigate the impact of the accessibility percentage on the variable values and to test the model in a scenario closer resembling the real world by adding a budget constraint. The time limit of the accessibility requirement was removed. In this case, a gradual improvement in the accessibility over the 12 years was observed due to the gradual opening of clinics over the years. Based on the analyses results, it was concluded that the model is sensitive to changes in parameters and that the model can be used for different scenarios. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted

Orientador: Antonio Roberto Balbo / Resumo: A produção de energia eólica tem se destacado no Brasil e mostrado grande importância na questão ambiental, pois auxilia na redução da emissão dos gases poluentes na atmosfera, provenientes de outras fontes de energia. Neste trabalho é proposta uma modelagem matemática de otimização multiobjetivo a qual explora a produção de energia eólica em um problema de despacho econômico e ambiental termo-eólico. O principal objetivo é mostrar que uma metodologia determinística envolvendo os métodos de otimização multiobjetivo de restrições canalizadas progressivas e de técnicas de programação por metas ponderadas, em conjunto com o método de pontos interiores, é eficiente à resolução deste problema. É proposta uma nova técnica, a qual é uma combinação entre os métodos de otimização multiobjetivo citados. As soluções dos subproblemas gerados por estes métodos serão determinadas através de pacotes computacionais, onde são apresentados resultados de casos distintos de produção de energia, mostrando a insuficiência da energia eólica nos custos operacionais da geração e no impacto ambiental / Abstract: The production of wind energy has stood out in Brazil and has shown great importance in the environmental issue, as it assists to reduce of polluting gases in the atmosphere arising out of other sources of energy. In this work a mathematical modeling of optimization multiobjective is proposed, which explores the wind energy production in a thermal-wind environmental and economic dispatch problem. The main objective is to show that a deterministic methodology involving the multiobjective optimization methods, progressive bounded constraints and weighted goal programming techniques, together with an interior point method, is e cient to solve this problem. A new technique is proposed, which is a combination of the mentioned multiobjective optimization methods. The solutions of the generated subproblems by these methods will be determined through of computational package and the results of distinct cases of energy production will be presented, showing the in uence of the wind power on the generation and on the environmental impact. / Doutor

Orientador: Helenice de Oliveira Florentino Silva / Resumo: Esta pesquisa teve por objetivo propor um modelo matemático para auxílio no planejamento da aplicação de agentes maturadores e da colheita da cana-de-açúcar, visando obter uma matéria-prima para o setor sucroenergético com máxima qualidade tecnológica relacionada ao teor de sacarose presente no caldo da cana-de-açúcar. Para tanto, inicialmente, foi realizada a revisão teórica acerca da temática relacionada ao ciclo produtivo da cana-de-açúcar, e, ainda, foram pesquisados os resultados de investigações científicas voltadas para essa atividade e que contemplam o uso de agentes químicos de maturação. Na sequência, buscou-se identificar o conjunto de variáveis e processos que ocorrem na fase de maturação da cana, e, também, foi analisada a temática relacionada à utilização de modelos matemáticos aplicados à otimização de processos envolvidos na cultura em questão. Foi formulado um modelo matemático de programação linear inteira a partir de técnicas de Programação por Metas Ponderadas. A avaliação do modelo proposto foi realizada mediante testes computacionais, utilizando quatro cenários baseados em instâncias compostas por 18, 50, 100 e 500 talhões, nos quais foi simulado o cultivo de 18 variedades de cana-de-açúcar adaptáveis à região centro-sul do Brasil. O modelo proposto neste trabalho foi validado e sua utilidade pôde ser evidenciada pela sua aplicabilidade na identificação do momento ideal para realizar a aplicação do maturador, pela identificação do momento ótimo para real... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This research aimed to propose a mathematical model to support the planning of the application of ripening agents and sugarcane harvesting, aiming to obtain raw material for the sugarcane industry with maximum technological quality related to the sucrose content present in the sugarcane juice. Initially, the theoretical review was carried out on the theme related to the sugarcane production stages. Then, a research was conducted in order to identify the results of scientific investigations focused on that activity approaching the use of chemical maturation agents. Next, it was sought to identify the set of variables and processes that occurred during the sugarcane maturation phase, and it was also analyzed the theme related to the use of optimization mathematical modeling applied to the crop in question. Finally, an integer linear programming mathematical modeling based on Weighted Goal Programming techniques was formulated. The evaluation of the proposed model was performed through computational tests using four scenarios based on 18, 50, 100 and 500 fields, in which the cultivation of 18 sugarcane varieties adaptable to the south-central region of Brazil was simulated. The model proposed in this research was validated and its usefulness could be evidenced not only by its applicability in the identification of the ideal moment for the application of the ripener, as well as in the identification of the optimum moment for the sugarcane harvest, but, also for the possibility of... (Complete abstract click electronic access below) / Doutor

The general practice in performance and production efficiency measurement has been to ignore additional products of most transformation processes that can be classified as "undesirable outputs" — which are a subset of the output set. Without the inclusion of these factors, the efficiency evaluation becomes a purely technical measure of the system alone, and does not account for the interaction of the system with the surrounding environment and the impact of policy decisions on the system. In addition, there are also technological dependencies arising due to the relationships between the desirable and the undesirable outputs. One of the analytical tools normally used in efficiency evaluation is Data Envelopment Analysis, DEA. In the course of addressing these problems, a decision-maker encounters multiple and contradictory objectives with respect to the output set. This motivates the exploration of new arenas of measurement of efficiency to facilitate policy decisions and address technological relationships. This research presents five modifications of the traditional DEA technique to give a more realistic and comprehensive score of production efficiency considering both, desirable and undesirable outputs. The models address the following problems: (i) technological dependency between desirable and undesirable outputs; (ii) decision-maker's preferences over inputs, desirable outputs and undesirable output performance and finally (iii) conflicting production objectives through a formulation that uses Goal Programming in conjunction with DEA, a concept known as GoDEA. / Master of Science

Le présent travail s’intéresse à la prise en compte de l’incertitude et du risque pour l’optimisation de la planification de production au niveau tactique d’une entreprise multi-sites d’une chaîne logistique. La méthode proposée permet d’assurer une planification des opérations de production et d’approvisionnement tout en intégrant au sein de son processus décisionnel un mécanisme de gestion de risque, en présence de diverses sources d’incertitude et d’ambigüité. Pour cela, une «bibliothèque» de critères structurés en deux classes indépendantes : critères de performance et critères de risque a été proposée, dans laquelle le décideur peut sélectionner ceux qui sont en cohérence avec ses préférences et sa stratégie de planification. La méthode doit chercher le bon compromis entre les performances et les risques prédéfinis par le décideur. Pour cela, nous nous somme dirigés dans un premier temps sur le développement d’une approche d’aide à la décision multicritères floue couplant un modèle analytique et la méthode TOPSIS floue. Cette approche consiste à générer un éventail de plans réalisables, caractérisés par leur performance et leur résistance aux risques. Le décideur peut alors choisir le plan qui reflète le compromis le plus adapté à sa stratégie de décision. Une deuxième approche d’optimisation multi-objectifs floue a été proposée dans un deuxième temps pour faire face à des problèmes de planification de grande taille au sein des chaînes logistiques opérant dans un environnement dynamique et incertain. Cette approche combine la méthode TOPSIS Floue, la programmation multi-objectifs possibiliste et la méthode du Goal Programming. L’objectif est de déterminer un plan jugé de bon compromis vis-à- vis des préférences du décideur par rapport aux objectifs de performance et de résistance aux risques. L’instanciation des deux approches proposées sur un exemple numérique a montré leur applicabilité et leur efficacité pour faire face à des problèmes de planification des chaînes logistiques utilisant des données incertaines et des préférences subjectives. Les expérimentations des deux approches permettant de tirer un ensemble d’enseignements utiles. / The work reported in this dissertation deals with risk-oriented integrated procurement–production approaches for tactical planning in a multi-echelon supply chain network presenting various sources of uncertainty and ambiguity. The proposed method allows planning of production and supply operations while integrating a risk management mechanism into its decision-making process, in the presence of various sources of uncertainty and ambiguity. So, a library" of criteria structured into two independent classes: Performance-based and risk-based decision criteria were proposed, in which the decision-maker can select those that are consistent with his preferences and his planning strategy. The method must seek the right compromise between performance and risk predefined by the decision-maker. To reach this goal, we initially focused on the development of a fuzzy multi-criteria decision making approach coupling an analytical model and the fuzzy TOPSIS method. This approach generates a range of feasible plans, characterized by their performance and their resistance to risks. The decision-maker can then choose the plan that reflects the compromise that best suits its decision strategy. Afterwards, a fuzzy multi-objective optimization approach was proposed to deal with large-scale planning problems within supply chains operating in a dynamic and uncertain environment. This approach second combines the Fuzzy TOPSIS method, the possibilistic multi-objective programming and the Goal Programming method. The objective is to determine a plan that is judged to be a good compromise compared to the decision maker's preferences regarding the performance and risk objectives. The instantiation of the two proposed approaches on a numerical example has shown their applicability and tractability to deal with supply chain planning problems in the presence of uncertain data and subjective preferences. The experiments of the two approaches make it possible to draw a useful set of lessons. The experiments of the two approaches show a set of useful issues.

Dans le cadre de cette thèse, notre objectif est de développer une approche multicritère d'aide à la coordination des décideurs pour la résolution des problèmes de sélection dans les chaines logistiques. En effet, nous considérons le cas où nous avons k décideurs/experts notés ST1,...,STk qui cherchent à classer un ensemble de m alternatives/choix notées A1,...,Am évaluées en termes de n critères conflictuels notés C1,..., Cn. L'ensemble des données manipulées est flou. Chaque décideur est amené à exprimer ses préférences pour chaque alternative par rapport à chaque critère à travers une matrice dite matrice des préférences. Notre approche comprend principalement deux phases, respectivement une phase de consensus qui consiste à trouver un accord global entre les décideurs et une phase de classement qui traite le problème de classement des différentes alternatives.Comme résultats, pour la première phase, nous avons adapté deux mécanismes de consensus, le premier est basé sur l'opérateur mathématique neat OWA et le second sur la mesure de possibilité. De même, nous avons développé un nouveau mécanisme de consensus basé sur la programmation par but goal programming. Pour la phase de classement, nous avons adapté dans un premier temps la méthode TOPSIS et dans un second, le modèle du goal programming avec des fonctions de satisfaction. Pour illustrer l'applicabilité de notre approche, nous avons utilisé différents problèmes de sélection dans les chaines logistiques comme la sélection des systèmes de formation, la sélection des fournisseurs, la sélection des robots et la sélection des entrepôts. / This thesis presents a development of a multi-criteria group decision making approach to solve the selection problems in supply chains. Indeed, we start in the context where a group of k decision makers/experts, is in charge of the evaluation and the ranking of a set of potential m alternatives. The alternatives are evaluated in fuzzy environment while taking into consideration both subjective (qualitative) and objective (quantitative) n conflicting criteria. Each decision maker is brought to express his preferences for each alternative relative to each criterion through a fuzzy matrix called preference matrix. We have developed three new approaches for manufacturing strategy, information system and robot selection problem:1. Fuzzy consensus-based possibility measure and goal programming approach.2. Fuzzy consensus-based neat OWA and goal programming approach.3. Fuzzy consensus-based goal programming and TOPSIS approach.Finally, a comparison of these three approaches is conducted and thus was able to give recommendations to improve the approaches and provide decision aid to the most satisfying decision makers.

Actuellement, les fabricants sont soumis à une pression économique importante et à une concurrence internationale accrue due à la globalisation des marchés. Pour réussir, les fabricants doivent être capables de répondre rapidement aux changements de la demande en adaptant leurs systèmes de production. Cette adaptation aux changements peut être réalisée à travers multiples reconfigurations du système de production.Les travaux présentés dans ce mémoire portent sur l'élaboration des méthodes de recherche opérationnelle permettant d'accompagner le décideur lors de la reconfiguration d'une ligne de transfert ou d'assemblage. Ce problème apparaît lorsqu'un nouveau produit doit être fabriqué par une ligne existante ou lorsqu'il y a eu des changements dans les caractéristiques du produit. Par conséquent, il devient nécessaire de modifier la configuration du système de production tout en minimisant les coûts induits. Ces coûts sont évalués différemment pour les systèmes automatisésou manuels. Dans le premier cas, qui correspond au cas des lignes de transfert, pour limiter les investissements, il est souhaitable de réutiliser au maximum les équipements existants à condition que les contraintes techniques et technologiques soient respectées. Dans le cas des lignes manuelles qui sont représentées dans notre étude par les lignes d'assemblage, l'objectif est de minimiser les coûts liés à l'apprentissage des opérateurs causés par la réaffectation de leurs tâches.Les méthodes de résolution exactes basées sur la modélisation mathématique et la programmation linéaire en nombre mixtes ainsi qu'une méthode de type goal programming sont développées dans ce travail pour argumenter la prise de décisions lors de la reconfiguration des lignes de production. Les méthodes proposées ont été testées avec succès sur des échantillons de problèmes proches des cas industriels et ont montré leur efficacité. / Global competition causes fluctuations in product demand and requires more frequent modifications of product characteristics. As a consequence, the production systems have to be frequently adapted to new production requirements.This work develops new combinatorial optimization methods for supporting decision makers at the reconfiguration stage considered for transfer and assembly lines. If new products have to be manufactured at the line or existing products are modified, then the line has to be reconfigured in order to meet new production requirements. In highly automated lines, as the transfer lines, the reconfiguration problem is focused on the readjustment of the equipment. To reduce the investment costs, the decision makers aim to reuse the available equipment as much as possible. The existence of compatibility constraints between new operations to be performed and existing facilities makes the reconfiguration problem hard and combinatorial.In manual assembly lines also studied in this thesis, the reconfiguration problem mostly concerns the reassignment of tasks to workers ant the minimization of the cost of retraining operators.The developed methods are based on the mathematical modelling and mixed integer programming, a goal programming approach is designed as well. These methods were successfully tested on a dataset of problem instances close to real industrial problems. The obtained results show the effectiveness and the efficiency of the solution methods proposed.