2,671 research outputs found
Edge Routing with Ordered Bundles
Edge bundling reduces the visual clutter in a drawing of a graph by uniting
the edges into bundles. We propose a method of edge bundling drawing each edge
of a bundle separately as in metro-maps and call our method ordered bundles. To
produce aesthetically looking edge routes it minimizes a cost function on the
edges. The cost function depends on the ink, required to draw the edges, the
edge lengths, widths and separations. The cost also penalizes for too many
edges passing through narrow channels by using the constrained Delaunay
triangulation. The method avoids unnecessary edge-node and edge-edge crossings.
To draw edges with the minimal number of crossings and separately within the
same bundle we develop an efficient algorithm solving a variant of the
metro-line crossing minimization problem. In general, the method creates clear
and smooth edge routes giving an overview of the global graph structure, while
still drawing each edge separately and thus enabling local analysis
Edge routing with ordered bundles
Edge bundling reduces the visual clutter in a drawing of a graph by uniting the edges into bundles. We propose a method of edge bundling that draws each edge of a bundle separately as in metro-maps and call our method ordered bundles. To produce aesthetically looking edge routes, it minimizes a cost function on the edges. The cost function depends on the ink, required to draw the edges, the edge lengths, widths and separations. The cost also penalizes for too many edges passing through narrow channels by using the constrained Delaunay triangulation. The method avoids unnecessary edge-node and edge-edge crossings. To draw edges with the minimal number of crossings and separately within the same bundle, we develop an efficient algorithm solving a variant of the metro-line crossing minimization problem. In general, the method creates clear and smooth edge routes giving an overview of the global graph structure, while still drawing each edge separately and thus enabling local analysis. © 2015 Elsevier B.V
Efficient Generation of Geographically Accurate Transit Maps
We present LOOM (Line-Ordering Optimized Maps), a fully automatic generator
of geographically accurate transit maps. The input to LOOM is data about the
lines of a given transit network, namely for each line, the sequence of
stations it serves and the geographical course the vehicles of this line take.
We parse this data from GTFS, the prevailing standard for public transit data.
LOOM proceeds in three stages: (1) construct a so-called line graph, where
edges correspond to segments of the network with the same set of lines
following the same course; (2) construct an ILP that yields a line ordering for
each edge which minimizes the total number of line crossings and line
separations; (3) based on the line graph and the ILP solution, draw the map. As
a naive ILP formulation is too demanding, we derive a new custom-tailored
formulation which requires significantly fewer constraints. Furthermore, we
present engineering techniques which use structural properties of the line
graph to further reduce the ILP size. For the subway network of New York, we
can reduce the number of constraints from 229,000 in the naive ILP formulation
to about 4,500 with our techniques, enabling solution times of less than a
second. Since our maps respect the geography of the transit network, they can
be used for tiles and overlays in typical map services. Previous research work
either did not take the geographical course of the lines into account, or was
concerned with schematic maps without optimizing line crossings or line
separations.Comment: 7 page
Evaluating Mobility Pattern Space Routing for DTNs
Because a delay tolerant network (DTN) can often be partitioned, the problem
of routing is very challenging. However, routing benefits considerably if one
can take advantage of knowledge concerning node mobility. This paper addresses
this problem with a generic algorithm based on the use of a high-dimensional
Euclidean space, that we call MobySpace, constructed upon nodes' mobility
patterns. We provide here an analysis and the large scale evaluation of this
routing scheme in the context of ambient networking by replaying real mobility
traces. The specific MobySpace evaluated is based on the frequency of visit of
nodes for each possible location. We show that the MobySpace can achieve good
performance compared to that of the other algorithms we implemented, especially
when we perform routing on the nodes that have a high connection time. We
determine that the degree of homogeneity of mobility patterns of nodes has a
high impact on routing. And finally, we study the ability of nodes to learn
their own mobility patterns.Comment: IEEE INFOCOM 2006 preprin
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off
Bundling of graph edges (node-to-node connections) is a common technique to
enhance visibility of overall trends in the edge structure of a large graph
layout, and a large variety of bundling algorithms have been proposed. However,
with strong bundling, it becomes hard to identify origins and destinations of
individual edges. We propose a solution: we optimize edge coloring to
differentiate bundled edges. We quantify strength of bundling in a flexible
pairwise fashion between edges, and among bundled edges, we quantify how
dissimilar their colors should be by dissimilarity of their origins and
destinations. We solve the resulting nonlinear optimization, which is also
interpretable as a novel dimensionality reduction task. In large graphs the
necessary compromise is whether to differentiate colors sharply between locally
occurring strongly bundled edges ("local bundles"), or also between the weakly
bundled edges occurring globally over the graph ("global bundles"); we allow a
user-set global-local tradeoff. We call the technique "peacock bundles".
Experiments show the coloring clearly enhances comprehensibility of graph
layouts with edge bundling.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding
Can the success of reinforcement learning methods for simple combinatorial
optimization problems be extended to multi-robot sequential assignment
planning? In addition to the challenge of achieving near-optimal performance in
large problems, transferability to an unseen number of robots and tasks is
another key challenge for real-world applications. In this paper, we suggest a
method that achieves the first success in both challenges for robot/machine
scheduling problems.
Our method comprises of three components. First, we show a robot scheduling
problem can be expressed as a random probabilistic graphical model (PGM). We
develop a mean-field inference method for random PGM and use it for Q-function
inference. Second, we show that transferability can be achieved by carefully
designing two-step sequential encoding of problem state. Third, we resolve the
computational scalability issue of fitted Q-iteration by suggesting a heuristic
auction-based Q-iteration fitting method enabled by transferability we
achieved.
We apply our method to discrete-time, discrete space problems (Multi-Robot
Reward Collection (MRRC)) and scalably achieve 97% optimality with
transferability. This optimality is maintained under stochastic contexts. By
extending our method to continuous time, continuous space formulation, we claim
to be the first learning-based method with scalable performance among
multi-machine scheduling problems; our method scalability achieves comparable
performance to popular metaheuristics in Identical parallel machine scheduling
(IPMS) problems
Operations Research Games: A Survey
This paper surveys the research area of cooperative games associated with several types of operations research problems in which various decision makers (players) are involved.Cooperating players not only face a joint optimisation problem in trying, e.g., to minimise total joint costs, but also face an additional allocation problem in how to distribute these joint costs back to the individual players.This interplay between optimisation and allocation is the main subject of the area of operations research games.It is surveyed on the basis of a distinction between the nature of the underlying optimisation problem: connection, routing, scheduling, production and inventory.cooperative games;operational research
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