Classification of Network Optimization Software Packages

Classification of Network Optimization Software Packages

Angelo Sifaleras (University of Macedonia, Greece)
DOI: 10.4018/978-1-4666-5888-2.ch695
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Abstract

Network optimization models and algorithms constitute a core research area of Mathematical Optimization and Computing. We present a wide range of state-of-the-art network optimization software packages and provide up-to-date definitions of the most important issues in algorithm engineering concerning fundamental network optimization problems. Furthermore, we discuss new concepts, trends, and emerging technologies in this research area. Apart from presenting important recent developments in network optimization and their impact on modern organizations and society, the proposed article is a classification of network optimization software packages with a special focus on solvers and benchmark collections. Finally, discussion is made regarding the societal impact and practical managerial significance of network optimization models and software tools.
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Background

During the last decades several advances have emerged in the field of network and combinatorial optimization (Cook, 2010). Nowadays, optimization software packages are required for the efficient solution of complex network optimization problems, even of moderate dimensions, due to their computational difficulty. Briefly speaking, an optimization software package is a software package specifically designed to be used for optimization problems. Such network optimization software packages have various differences in their characteristics, technology, and scope (Maros & Khaliq, 2002). The most well-known types of optimization software packages include optimization solvers, problem generators, performance analyzers/profilers, and educational software packages using visualization/animation techniques. An optimization solver is an optimization software package including efficient implementations of optimization algorithms specifically designed for the solution of optimization problems. Furthermore, a network generator is an optimization software package designed for random generating instances of network optimization problems with specific structure and dimension. Moreover, modern performance analyzer/profilers are now available to analyze the efficiency of the source code of an optimization algorithm, identify the bottlenecks in its performance, and suggest ways of computational improvements.

Benchmark collections for mathematical programming problems are also very important for comparing the computational efficiency of network optimization solvers. Roughly speaking, a benchmark collection constitutes a set of computationally difficult problems, either from real-world applications (e.g., real data from railway networks for robust train timetabling problems) or randomly generated. Such sets of benchmark problem instances allow researchers to compare the efficiency of their optimization solvers in common, usually publicly available, problem sets.

Key Terms in this Chapter

Integer Optimization: A research sub-area of Optimization, concerning problems whose decision variables take integer values.

Benchmark Problems: A data set of computationally difficult problems, either from real-world applications or randomly generated, used by researchers in order to test the efficiency of optimization solvers.

Network Optimization: A research sub-area of Optimization concerning problems that can be modeled using graphs and networks.

Optimization Solver: An optimization software package, including efficient implementations of optimization algorithms, specifically designed for the solution of optimization problems.

Optimization Software Package: A software package specifically designed to be used for optimization problems.

Network Generator: An optimization software package designed for random generating instances of network optimization problems with specific structure and dimension.

Performance Analyzer: A software package designed to analyze the efficiency of the source code of an optimization algorithm, identify the bottlenecks in its performance, and suggest ways of computational improvements.

Dynamic Network Optimization Problems: Network optimization problems whose parameters change (e.g., time varying) and are not static.

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