José Manuel García Sánchez - Modelling in Mathematical Programming: Methodology and Techniques

More information about this series at International Series in Operations Research and http://www.springer.com/series/6161 The book series

Management Science encompasses the various areas of operations research and management science. Both theoretical and applied books are included. It describes current advances anywhere in the world that are at the cutting edge of the field. The series is aimed especially at researchers, doctoral students, and sophisticated practitioners. The series features three types of books:

The series emphasizes the following four areas:Mathematical Programming: Including linear programming, integer programming, nonlinear programming, interior point methods, game theory, network optimization models, combinatorics, equilibrium programming, complementarity theory, multiobjective optimization, dynamic programming, stochastic programming, complexity theory, etc.Applied Probability: Including queuing theory, simulation, renewal theory, Brownian motion and diffusion processes, decision analysis, Markov decision processes, reliability theory, forecasting, other stochastic processes motivated by applications, etc.Production and Operations Management: Including inventory theory, production scheduling, capacity planning, facility location, supply chain management, distribution systems, materials requirements planning, just-in-time systems, flexible manufacturing systems, design of production lines, logistical planning, strategic issues, etc.Applications of Operations Research and Management Science: Including telecommunications, health care, capital budgeting and finance, marketing, public policy, military operations research, service operations, transportation systems, etc.

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Generally, modelling has been considered a little regulated technique, based on knowledge of the types of optimization problems and the experience of the modeler. This book presents the first methodology for the building of a mathematical model in an integral way, as well as new techniques that help us if we want to follow our own building criteria. The objective is to provide a simple work dynamic that facilitates the modelling process.

The book is a basic tool for learning to model in mathematical programming, from models without much complexity to complex system models. The book presents a structure the models, and complex constraints models more easily. It is a basic modelling guide for any system, and explains models already existing in the literature.

The book presents a structure that guides the orderly learning of the components that the methodology establishes in an optimization problem, within a system:

The elements participate in the actions that occur in the system and support its specifications. They are closely related to the activities that occur in the system, and the joint identification of both components is sometimes effective.

The elements are configured taking into account their quantitative nature, their associated data and their reference in the specifications. The quantitative nature of the elements will be used as a tool to help define decision activities. It is probably an unnecessary tool for an experienced modeler, but it may be useful for people who start modelling in mathematical programming.