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Earlier studies in the context of windows and blinds selection have mostly tried to increase the awareness regarding various effects of windows and blinds selection on subjective well-being, including their effect on visual comfort, thermal comfort, energy consumption and life cycle cost. However, the main problem is the potential conflicts between visual comfort, thermal comfort, energy consumption and life cycle cost. Increased awareness about the contradictory effect of windows and blinds selection on subjective well-being on one hand and lack of a feasible method in managing the conflicts on the other hand may bind individuals, as decision-makers, in a situation where they follow the immediate economic benefits rather than the long-term visual and thermal benefits. To solve the mentioned problem, this study analysed first the degree of the conflicts between average daylight illuminance and total energy consumption in Sweden. This decision was made due to large variation in solar elevation angle and solar intensity between summer and winter in Sweden, which has significant effects on daylight illuminance and total energy consumption. Analysing the conflicts was accomplished by developing two multivariate linear regression models for calculating average daylight illuminance and total energy consumption. Comparison and analysis of the multivariate linear regression models showed the existence of a high degree of conflicts, which makes window and blind selection a rather complex multidimensional problem. Specifying the degree of the conflicts formed a hypothesis as: “A multi criteria decision-making method increases the controllability and manages the conflicts in selecting windows and blinds”. The developed hypothesis was later tested by employing analytical hierarchy process, as widely used multi criteria decisionmaking method. The analytical hierarchy process prioritizes decision-maker’ preferences and introduces a desired trade-off solution. The results of employing analytical hierarchy process showed the capability of it in managing the conflicts among visual comfort, thermal comfort, energy consumption and life cycle cost. Finally, the application of the analytical hierarchy process was expanded by integrating it with nondominated sorting genetic algorithm-II, as an optimization algorithm. Through this integration, optimization algorithm combines windows’ and blinds’ design variables and analyses a large number of solutions, while analytical hierarchy process ranks the solutions based on decision-makers’ preferences and introduces a desired trade-off solution. The integration between analytical hierarchy process and the nondominated sorting genetic algorithm-II was presented later as a conceptual framework. The developed conceptual framework can be used for selecting windows and blinds II in both residential and commercial buildings. In selecting windows and blinds, the conceptual framework is a novel solution to the lack of a feasible method for increasing the controllability for decision-makers and obtaining a desired trade-off solution. / Tidigare studier avseende val av fönster och solskydd har främst försökt fastställa olika effekter som valet av fönster och solskydd har på det subjektiva välbefinnandet. Detta inkluderar dessa föremåls effekt på den visuella komforten, den termiska komforten, energiförbrukningen och livscykelkostnaderna. Det huvudsakliga problemet är dock de potentiella konflikterna mellan visuell komfort, termisk komfort, energiförbrukning och livscykelkostnader. Avsaknaden av en metod för att hantera denna konflikt leder till att beslutfattaren fastnar i en situation där de snarare gör sitt val utifrån omedelbara ekonomiska fördelar än de långsiktiga visuella och termiska fördelarna. För att lösa ovan nämnda problem analyserades konflikterna mellan det genomsnittliga dagsljusinsläppet och den totala energiförbrukningen i Sverige. En av huvudanledningarna till konflikterna är att solens infallsvinkel och intensitet varierar kraftigt mellan sommar och vinter i Sverige. Detta har betydande effekter på dagsljusinfallet och den totala energiförbrukningen. Konflikterna analyserades genom att utveckla två multivariata linjära regressionsmodeller för att beräkna det genomsnittliga dagsljusinfallet och den totala energiförbrukningen. En jämförelse och analys av de multivariata linjära regressionsmodellerna påvisade en hög grad av konflikter, vilket gör valet av fönster och solskydd till ett komplext och flerdimensionellt problem. Bestämningen av graden av konflikt formade följande hypotes: ” En multikriterieanalysbaserat beslutsstöd ökar kontrollerbarheten och hanterar konflikter vid valet av fönster och solskydd”. Den utvecklade hypotesen testades senare med hjälp av Analytical Hierarchy Process (AHP), en ofta använd multikriterieanalys metod för beslutsfattande. Metoden tar fram lösningar genom att göra prioriteringar enligt beslutsfattarens preferenser. Resultaten av att tillämpa metoden visade metodens förmåga att lösa konflikterna kring visuell komfort, termisk komfort, energiförbrukning och livscykelkostnad. Slutligen utökades metoden genom att integrera AHP med optimeringsalgoritmen Non-dominated sorting genetic algorithm-II. Genom denna integrering kombinerar optimeringsalgoritmen fönstrens och solskyddens design variabler till ett stort antal lösningsförslag. Dessa lösningsförslag analyseras och till sist rangordnas lösningsförslagen med hjälp av AHP baserat på beslutsfattarnas preferenser. Integreringen av AHP och optimeringsalgoritmen presenterades som ett konceptuellt ramverk. I valet av fönster och solskydd är det konceptuella ramverket en ny lösning för att öka den upplevda kontrollen och därmed förstärka det subjektiva välbefinnandet. / ProWood

Background: Multiple Objective Optimization problems(MOOPs) are common and evident in every field. Container port terminals are one of the fields in which MOOP occurs. In this research, we have taken a case in logistics management and modelled Multi-agent systems to solve the MOOP using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Objectives: The purpose of this study is to build AI-based models for solving a Multiple Objective Optimization Problem occurred in port terminals. At first, we develop a port agent with an objective function of maximizing throughput and a customer agent with an objective function of maximizing business profit. Then, we solve the problem using the single-objective optimization model and multi-objective optimization model. We then compare the results of both models to assess their performance. Methods: A literature review is conducted to choose the best algorithm among the existing algorithms, which were used previously in solving other Multiple Objective Optimization problems. An experiment is conducted to know how well the models performed to solve the problem so that all the participants are benefited simultaneously. Results: The results show that all three participants that are port, customer one and customer two have gained profits by solving the problem in multi-objective optimization model. Whereas in a single-objective optimization model, a single participant has achieved earnings at a time, leaving the rest of the participants either in loss or with minimal profits. Conclusion: We can conclude that multi-objective optimization model has performed better than the single-objective optimization model because of the impartial results among the participants.

Made available in DSpace on 2014-12-17T15:47:52Z (GMT). No. of bitstreams: 1 ThatianaCNS_DISSERT.pdf: 1637234 bytes, checksum: 8b38ce4a7a358efe654d9bb1c23c15bc (MD5) Previous issue date: 2010-03-02 / The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated / A distribui??o de produtos de petr?leo atrav?s de redes de polidutos ? um importante problema que se coloca no planejamento de produ??o das refinarias. Consiste em determinar o que ser? feito em cada est?gio de produ??o dado um determinado horizonte de tempo, no que respeita ? distribui??o de produtos de n?s fonte ? procura de n?s, passando por n?s intermedi?rios. Restri??es relativas a limites de armazenamento, tempo de entrega, disponibilidade de fontes, limites de envio ou recebimento, entre outros, t?m de ser satisfeitas. Este problema pode ser visto como um problema biobjetivo, que visa minimizar o tempo necess?rio para transportar o conjunto de pacotes atrav?s da rede e o envio sucessivo de produtos diferentes no mesmo duto que ? chamado de fragmenta??o. Neste trabalho, s?o desenvolvidos tr?s algoritmos que s?o aplicados a esse problema: o primeiro algoritmo ? discreto e baseia-se na Otimiza??o por Nuvem de Part?culas (PSO), com procedimentos de busca local e path-relinking propostos como operadores de velocidade, o segundo e o terceiro algoritmos tratam de duas vers?es baseadas no Non-dominated Sorting Genetic Algorithm II (NSGA-II). Os algoritmos propostos s?o comparados a outras abordagens para o mesmo problema, em termos de qualidade de solu??o e tempo computacional despendido, a fim de se avaliar a efici?ncia dos m?todos desenvolvidos