abstract: A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework for Large-Scale Decisions to address these limitations in three phases as follows. The first phase proposes a binary integer program (BIP) to intelligently decompose a PCM into several mutually exclusive subsets using interdependence scores. As a result, the number of pairwise comparisons is reduced and the consistency of the PCM is improved. Since the subsets are disjoint, the most independent pivot element is identified to connect all subsets. This is done to derive the global weights of the elements from the original PCM. The proposed BIP is applied to both AHP and ANP methodologies. However, it is noted that the optimal number of subsets is provided subjectively by the DM and hence is subject to biases and judgement errors. The second phase proposes a trade-off PCM decomposition methodology to decompose a PCM into a number of optimally identified subsets. A BIP is proposed to balance the: (1) time savings by reducing pairwise comparisons, the level of PCM inconsistency, and (2) the accuracy of the weights. The proposed methodology is applied to the AHP to demonstrate its advantages and is compared to established methodologies. In the third phase, a beta distribution is proposed to generalize a wide variety of imprecise pairwise comparison distributions via a method of moments methodology. A Non-Linear Programming model is then developed that calculates PCM element weights which maximizes the preferences of the DM as well as minimizes the inconsistency simultaneously. Comparison experiments are conducted using datasets collected from literature to validate the proposed methodology. / Dissertation/Thesis / Ph.D. Industrial Engineering 2013

Pairwise comparison matrices (PCMs) play a key role in multi-criteria decision making, especially in the analytic hierarchy process. It could be necessary for an expert to compare alternatives based on various criteria. However, for a variety of reasons, such as lack of time or insufficient knowledge, it may happen that the expert cannot provide judgments on all pairs of alternatives. In this case, an incomplete pairwise comparison matrix is formed. In the first research part, an optimization algorithm is proposed for the optimal completion of an incomplete PCM. It is intended to numerically minimize a constrained eigenvalue problem, in which the objective function is difficult to write explicitly in terms of variables. Numerical simulations are carried out to examine the performance of the algorithm. The simulation results show that the proposed algorithm is capable of solving the minimization of the constrained eigenvalue problem. In the second part, a comparative analysis of eleven completion methods is studied. The similarity of the eleven completion methods is analyzed on the basis of numerical simulations and hierarchical clustering. Numerical simulations are performed for PCMs of different orders considering various numbers of missing comparisons. The results suggest the existence of a cluster of five extremely similar methods, and a method significantly dissimilar from all the others. In the third part, the filling in patterns (arrangements of known comparisons) of incomplete PCMs based on their graph representation are investigated under given conditions: regularity, diameter and number of vertices, but without prior information. Regular and quasi-regular graphs with minimal diameter are proposed. Finally, the simulation results indicate that the proposed graphs indeed provide better weight vectors than alternative graphs with the same number of comparisons. This research problem’s contributions include a list of (quasi-)regular graphs with diameters of 2 and 3, and vertices from 5 up to 24.

[pt] Muitos problemas de transportes, assim como de outras áreas do conhecimento, envolvem tomada de decisão. Em decisões complexas, a escolha da melhor alternativa ou plano de ação pode envolver mais de um critério e é necessário estudar como cada ação afeta cada critério. O método AHP, Analytic Hierarchy Process, proposto por Thomas L. Saaty, é um método de decisão multicriterial que funciona para os mais diversos tipos de decisões, solucionando problemas com fatores quantitativos e qualitativos. Ele reúne a opinião dos tomadores de decisão em matrizes de comparação. Este trabalho faz uma revisão geral de conceitos básicos do método, mostrando diferentes maneiras de cálculo da solução. A primeira explorada é o cálculo exato através dos autovalores e autovetores das matrizes. Para esse cálculo, foi utilizado o software francês Scilab, semelhante ao mais conhecido Matlab, mas distibuído gratuitamente na internet. É discutida a questão da consistência dos julgamentos, com maneiras de medi-la e melhorá-la. Finalmente, é feita uma proposta de solução aproximada, que questiona a idéia original de que um certo nível de inconsistência é desejável. É uma solução simplificada que, supondo consistência absoluta, facilita não só os cálculos como o trabalho inicial dos tomadores de decisão. Em vez de comparar todas as alternativas com as outras, duas a duas, passa a ser necessário comparar apenas uma alternativa com as outras. A nova solução aproximada é comparada com a solução exata em três casos retirados da literatura. / [en] Several transportation problems, as well as problems in other knowledge areas, request decision making. In complex decisions, the choice of best alternative or course of action can contain more than one criterion and it is necessary to study how each alternative affects each criterion. The AHP, Analytic Hierarchy Process, proposed by Thomas L. Saaty, is a multicriteria decision method that works well for very diverse decision types, solving problems with tangible and intangible factors. It gathers the opinion of decision makers in comparison matrices. This study makes a general review of basic concepts of the method, showing different manners of calculating the solution. The first one to be displayed is the exact solution using the eigenvalues and eigenvectors of the matrices. For this solution the French software Scilab was used, which is similar to the well-known Matlab, but free and distributed on the web. The issue of judgment consistency is discussed, including ways of measuring and improving it. Finally, a proposal of approximated solution is made, questioning the original idea which says that a certain level of inconsistency is desirable. It is a simplification that, considering absolute consistency, facilitates not only the calculations but also the early work of decision makers when judging the alternatives. Instead of making pair wise comparisons of all alternatives with each other, it becomes necessary to compare only one alternative with the others. The new approximated solution is compared to the real solution in three cases taken from the literature.