Optimization Tools for Logistics covers the theory and practice of the main principles of operational research and the ways it can be applied to logistics and decision support with regards to common software. The book is supported by worked problems and examples from industrial case studies, providing a comprehensive tool for readers from a variety of industries.
Goal Programming (GP) is perhaps the oldest and most widely used approach within the Multiple Criteria Decision Making (MCDM) paradigm. GP combines the logic of optimisation in mathematical programming with the decision maker's desire to satisfy several goals. The primary purpose of this book is to identify the critical issues in GP and to demonstrate different procedures capable of avoiding or mitigating the inherent pitfalls associated with these issues. The outcome of a search of the literature shows many instances where GP models produced misleading or even erroneous results simply because of a careless formulation of the problem. Rather than being in itself a textbook, Critical Issues in Goal Programming is designed to complement existing textbooks. It will be useful to students and researchers with a basic knowledge of GP as well as to those interested in building GP models which analyse real decision problems.
The importance of submodular functions has been widely recognized in recent years in combinatorial optimization. This is the first book devoted to the exposition of the theory of submodular functions from an elementary technical level to an advanced one. A unifying view of the theory is shown by means of base polyhedra and duality for submodular and supermodular systems. Among the subjects treated are: neoflows (submodular flows, independent flows, polymatroidal flows), submodular analysis (submodular programs, duality, Lagrangian functions, principal partitions), nonlinear optimization with submodular constraints (lexicographically optimal bases, fair resource allocation). Special emphasis is placed on the constructive aspects of the theory, which lead to practical, efficient algorithms.