Assignment Problem

Assignment Problem is one of the first fundamental problems in the area of combinatorial optimization. Despite its historical roots, the problem has tremendous importance to date, due to its numerous real life applications in like facility location, personnel scheduling, job scheduling, production planning, project assignment, task assignment, time-tabling, vehicle routing, storage space allocation, etc. This book comprises some real life assignment and related problems involving imprecise cost/time parameters. In order to represent those imprecise parameters, interval numbers have been used here as this representation is the best among others. The associated problems have been solved using Genetic Algorithm efficiently. A salient feature of this book is that most of the algorithms have been written in a lucid manner with suitable examples using figures. This book should interest academicians, OR/GA practitioners and executives of different industries and business organizations.

Stress in Moroccan Arabic, and also in other languages, poses challenging problems for phonological theory. This is due to the fact that stress exhibits conflicting properties; which makes it difficult to provide answers to a number of issues. This book provides an investigation into a prominent aspect of prosodic phonology in Moroccan Arabic (a case study of the dialect of Rabat city), namely, stress assignment in the grammatical category of verbs within the framework of optimality theory as proposed in Prince and Smolensky and McCarthy and Prince. It particularly addresses a range of issues such as: the position of stress, the basis on which stress is located, the factors on the basis of which stress location is effected, the importance of the perceptual and the instrumental tests in shedding light on stress assignment, and the extent to which the optimality theoretic analysis confirm or disconfirm the results of the perceptual and the instrumental tests concerning stress placement. This book will be of interest to linguistics, teachers, researchers and to anyone interested in language study and phonological theory.

For the last decades, Transport Demand and Mobility has been a continuously developing branch in the transport literature. This is reflected in the great amount of papers published on scientific magazines trying to solve the traffic assignment and trip matrix estimation problems. This fact shows the difficulty to elaborate an effective model able to reproduce the real behaviour. These models use several data inputs (prior trip matrix, link counts, etc.), only from a subset of the problem variables, and its size will depend on the available budget. One problem of these models is the high number of possible solutions which is usually solved by finding one where the model results and the real data match up. Nevertheless a model does not have to reproduce only the real data, but also all the variables. Therefore the problem must consist of reproducing the reality in a precise form with minimum cost. The aim of this work consists of proposing new models, which allow us to update the traffic flow predictions from a small subset of real data. To this end, a Gaussian Bayesian Network is used, and so, probability intervals are obtained to get an idea of the associated uncertainties.

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