Many full truckload pick-up and delivery problems in the intermodal freight container transport industry can be modeled as Asymmetric Traveling Salesman Problems (ATSPs). Several authors have noted that while ATSPs ar...
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Many full truckload pick-up and delivery problems in the intermodal freight container transport industry can be modeled as Asymmetric Traveling Salesman Problems (ATSPs). Several authors have noted that while ATSPs are NP-hard, some instances are readily solved to optimality in only a short amount of time. Furthermore, the literature contains several references to the Stacker Crane Problem (SCP) as an "easy" problem amidst the ATSPs. We put this hypothesis to test by using statistical methods to build a model relating measurable distance matrix structures to the amount of time required by two existing exact solvers in finding solutions to over 500 ATSP instances. From this analysis we conclude that SCPs are not necessarily easier than other ATSPs, but a special subset of SCPs, termed drayage problems, are more readily solved. We speculate that drayage problems are "easy" because of a comparatively high number of zeros in symmetric locations within the distance matrix. In real-world drayage problems (i.e. the movement of containers a short distance to/from a port or rail terminal), these zeros correspond to the prevalence of jobs originating at or destined to a fixed number of freight terminals. (c) 2011 Elsevier Ltd. All rights reserved.
The field of network science is a highly interdisciplinary area;for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions ...
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The field of network science is a highly interdisciplinary area;for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis-an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines-including statistical analyses-for the systematic evaluation of network analysisalgorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics;they are not only useful for network analysis, but also in related contexts.
Although experimental studies have been widely applied to the investigation of algorithm performance, very little attention has been given to experimental method in this area. This is unfortunate, since much can be do...
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Although experimental studies have been widely applied to the investigation of algorithm performance, very little attention has been given to experimental method in this area. This is unfortunate, since much can be done to improve the quality of the data obtained;often, much improvement may be needed for the data to be useful. This paper gives a tutorial discussion of two aspects of good experimental technique: the use of variance reduction techniques and simulation speedups in algorithm studies. In an illustrative study, application of variance reduction techniques produces a decrease in variance by a factor 1000 in one case, giving a dramatic improvement in the precision of experimental results. Furthermore, the complexity of the simulation program is improved from THETA(mn/H(n)) to THETA(m + n log n) (where m is typically much larger than n), giving a much faster simulation program and therefore more data per unit of computation time. The general application of variance reduction techniques is also discussed for a variety of algorithm problem domains.
The purpose of this special issue of algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuni...
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The purpose of this special issue of algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues and standards in algorithmic experimentation were encouraged. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing were also welcome. In this regard, we solicited contributions from all most prominent areas of applied algorithmic research, which include but are not limited to graphs, databases, computational geometry, big data, networking, combinatorial aspects of scientific computing, and computational problems in the natural sciences or engineering.
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