Combinational optimization (CO) is a topic in applied mathematics, decision science and computer science that consists of finding the best solution from a non-exhaustive search. CO is related to disciplines such as computational complexity theory and algorithm theory, and has important applications in fields such as operations research/management science, artificial intelligence, machine learning, and software engineering.Advances in Combinatorial Optimization presents a generalized framework for formulating hard combinatorial optimization problems (COPs) as polynomial sized linear programs. Though developed based on the 'traveling salesman problem' (TSP), the framework allows for the formulating of many of the well-known NP-Complete COPs directly (without the need to reduce them to other COPs) as linear programs, and demonstrates the same for three other problems (e.g. the 'vertex coloring problem' (VCP)). This work also represents a proof of the equality of the complexity classes 'P' (polynomial time) and 'NP' (nondeterministic polynomial time), and makes a contribution to the theory and application of 'extended formulations' (EFs).On a whole, Advances in Combinatorial Optimization offers new modeling and solution perspectives which will be useful to professionals, graduate students and researchers who are either involved in routing, scheduling and sequencing decision-making in particular, or in dealing with the theory of computing in general.
Combinatorial Engineering of Decomposable Systems presents a morphological approach to the combinatorial design/synthesis of decomposable systems. Applications involve the following: design (e.g., information systems; user's interfaces; educational courses); planning (e.g., problem-solving strategies; product life cycles; investment); metaheuristics for combinatorial optimization; information retrieval; etc.
Praise for the First Edition This book is refreshing to read since it takes an important topic... and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.” —Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author’s own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office® Excel®, the book provides: • A theoretical and applied perspective on location models and algorithms • An intuitive presentation of the uses and limits of modeling techniques • An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems • A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.
Composite decisions are decisions consisting of interconnected parts (subdecisions) and they correspond to a composite (composable, modular, decomposable) system. The material will be of interest to scientists (e.g., mathematicians, computer scientists, economists, social engineers,etc.). The book can be used as a text for courses (for example: systems engineering, system design, life cycle engineering, engineering design, combinatorial synthesis) at the level of undergraduate (a compressed version), graduate/PhD levels and for continuing education.
Combinational optimization (CO) is a topic in applied mathematics, decision science and computer science that consists of finding the best solution from a non-exhaustive search. CO is related to disciplines such as computational complexity theory and algorithm theory, and has important applications in fields such as operations research/management science, artificial intelligence, machine learning, and software engineering.Advances in Combinatorial Optimization presents a generalized framework for formulating hard combinatorial optimization problems (COPs) as polynomial sized linear programs. Though developed based on the 'traveling salesman problem' (TSP), the framework allows for the formulating of many of the well-known NP-Complete COPs directly (without the need to reduce them to other COPs) as linear programs, and demonstrates the same for three other problems (e.g. the 'vertex coloring problem' (VCP)). This work also represents a proof of the equality of the complexity classes 'P' (polynomial time) and 'NP' (nondeterministic polynomial time), and makes a contribution to the theory and application of 'extended formulations' (EFs).On a whole, Advances in Combinatorial Optimization offers new modeling and solution perspectives which will be useful to professionals, graduate students and researchers who are either involved in routing, scheduling and sequencing decision-making in particular, or in dealing with the theory of computing in general.
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