8 research outputs found
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
Task scheduling system for UAV operations in indoor environment
The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported