Generalized Network Design Problems


Book Description

Combinatorial optimization is a fascinating topic. Combinatorial optimization problems arise in a wide variety of important fields such as transportation, telecommunications, computer networking, location, planning, distribution problems, etc. Important and significant results have been obtained on the theory, algorithms and applications over the last few decades. In combinatorial optimization, many network design problems can be generalized in a natural way by considering a related problem on a clustered graph, where the original problem's feasibility constraints are expressed in terms of the clusters, i.e., node sets instead of individual nodes. This class of problems is usually referred to as generalized network design problems (GNDPs) or generalized combinatorial optimization problems. The express purpose of this monograph is to describe a series of mathematical models, methods, propositions, algorithms developed in the last years on generalized network design problems in a unified manner. The book consists of seven chapters, where in addition to an introductory chapter, the following generalized network design problems are formulated and examined: the generalized minimum spanning tree problem, the generalized traveling salesman problem, the railway traveling salesman problem, the generalized vehicle routing problem, the generalized fixed-charge network design problem and the generalized minimum vertex-biconnected network problem. The book will be useful for researchers, practitioners, and graduate students in operations research, optimization, applied mathematics and computer science. Due to the substantial practical importance of some presented problems, researchers in other areas will find this book useful, too.




Hybrid Metaheuristics for Generalized Network Design Problems


Book Description

In this book, we consider several generalized network design problems which belong to the family of NP-hard combinatorial optimization problems. In contrast to their classical counterparts, the generalized versions are defined on graphs whose node sets are partitioned into clusters. The goal is to find a subgraph which spans exactly one node from each cluster and also meets further constraints respectively. Applicable methodologies for solving combinatorial optimization problems can roughly be divided into two mainstreams. The first class consists of algorithms which aim to solve these problems to proven optimality - provided that they are given enough run-time and memory. The second class are metaheuristics which compute approximate solutions but usually require significantly less run-time. By combining these two classes, we are able to form collaboration algorithms that benefit from advantages of both sides. Such approaches are considered for solving the generalized network design problems in this book.




Design and Implementation of Data Structures for Generalized Networks


Book Description

The specialization of the simplex algorithm for the solution of generalized network flow problems rests on the fact that a basis for the problem may be represented graphically as a spanning forest in which each component is either a one-tree or a rooted tree. The design of a specialized algorithm for efficient solution of generalized network problems necessarily depends on data structures chosen to represent the basis. This paper presents the design and detailed algorithmic specification of the primal simplex algorithm for such problems. Computational testing to determine the overhead required by generalized network data structures over pure network data structures indicates that generalized network algorithms are on the order of 2.5 to 3.5 times slower than pure network algorithms. Computational testing with generalized network problems with up to 1000 nodes and 7000 arcs establishes the suitability of the data-structures for efficient implementation of primal simplex calculations. Keywords: Linear programming. (Author).




Network Optimization Problems: Algorithms, Applications And Complexity


Book Description

In the past few decades, there has been a large amount of work on algorithms for linear network flow problems, special classes of network problems such as assignment problems (linear and quadratic), Steiner tree problem, topology network design and nonconvex cost network flow problems.Network optimization problems find numerous applications in transportation, in communication network design, in production and inventory planning, in facilities location and allocation, and in VLSI design.The purpose of this book is to cover a spectrum of recent developments in network optimization problems, from linear networks to general nonconvex network flow problems./a




Solving Network Design Problems via Decomposition, Aggregation and Approximation


Book Description

Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time.







Routing, Flow, and Capacity Design in Communication and Computer Networks


Book Description

In network design, the gap between theory and practice is woefully broad. This book narrows it, comprehensively and critically examining current network design models and methods. You will learn where mathematical modeling and algorithmic optimization have been under-utilized. At the opposite extreme, you will learn where they tend to fail to contribute to the twin goals of network efficiency and cost-savings. Most of all, you will learn precisely how to tailor theoretical models to make them as useful as possible in practice.Throughout, the authors focus on the traffic demands encountered in the real world of network design. Their generic approach, however, allows problem formulations and solutions to be applied across the board to virtually any type of backbone communication or computer network. For beginners, this book is an excellent introduction. For seasoned professionals, it provides immediate solutions and a strong foundation for further advances in the use of mathematical modeling for network design. - Written by leading researchers with a combined 40 years of industrial and academic network design experience. - Considers the development of design models for different technologies, including TCP/IP, IDN, MPLS, ATM, SONET/SDH, and WDM. - Discusses recent topics such as shortest path routing and fair bandwidth assignment in IP/MPLS networks. - Addresses proper multi-layer modeling across network layers using different technologies—for example, IP over ATM over SONET, IP over WDM, and IDN over SONET. - Covers restoration-oriented design methods that allow recovery from failures of large-capacity transport links and transit nodes. - Presents, at the end of each chapter, exercises useful to both students and practitioners.




Generalized Connectivity of Graphs


Book Description

Noteworthy results, proof techniques, open problems and conjectures in generalized (edge-) connectivity are discussed in this book. Both theoretical and practical analyses for generalized (edge-) connectivity of graphs are provided. Topics covered in this book include: generalized (edge-) connectivity of graph classes, algorithms, computational complexity, sharp bounds, Nordhaus-Gaddum-type results, maximum generalized local connectivity, extremal problems, random graphs, multigraphs, relations with the Steiner tree packing problem and generalizations of connectivity. This book enables graduate students to understand and master a segment of graph theory and combinatorial optimization. Researchers in graph theory, combinatorics, combinatorial optimization, probability, computer science, discrete algorithms, complexity analysis, network design, and the information transferring models will find this book useful in their studies.




Hybrid Artificial Intelligence Systems


Book Description

This volume constitutes the proceedings of the 9th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2014, held in Salamanca, Spain, in June 2014. The 61 papers published in this volume were carefully reviewed and selected from 199 submissions. They are organized in topical sessions on HAIS applications; data mining and knowledge discovery; video and image analysis; bio-inspired models and evolutionary computation; learning algorithms; hybrid intelligent systems for data mining and applications and classification and cluster analysis.




Design Optimization


Book Description

Design Optimization deals with the application of the ideas of optimization to design, taking as its central theme the notion that design can be treated as a goal-seeking, decision-making activity. Emphasis is on design optimization rather than on optimization techniques. This book consists of nine chapters, each focusing on a particular class of design optimization and demonstrating how design optimization problems are formulated and solved. The applications range from architecture and structural engineering to mechanical engineering, chemical engineering, building design and layout, and siting policy. The first five chapters are all concerned with design problems where it is convenient to express the goals in a single objective or criterion to be optimized. In particular, optimal space planning and shape optimization of structures are discussed, along with approximation concepts for optimum structural design; application of nonlinear programming to design; and generalized Steiner network problems in engineering design. The last four chapters focus on multicriteria programming; multicriteria optimization for engineering and architectural design; and a system for integrated optimal design. This monograph will be of interest to designers and others concerned with the use of optimization concepts and tools in design optimization.