Article
Solving the University Class Scheduling Problem Using Advanced ILP Techniques
01/2007;

Article: Taskresource scheduling problem
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ABSTRACT: Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The taskresource management is the key role in cloud computing systems. Taskresource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Taskresource scheduling problem is NPcomplete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the taskresource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.International Journal of Automation and Computing 08/2012; 9(4). 
Conference Paper: Using SAT & ILP techniques to solve enhanced ILP formulations of the Clustering Problem in MANETS
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ABSTRACT: Improvements over recent years in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers have encouraged the modeling of complex engineering problems as ILP. An example is the Clustering Problem in Mobile AdHoc Networks (MANETs). The Clustering Problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads, and ensuring that regular nodes are connected to clusterheads such that the lifetime of the network is maximized. This paper proposes enhanced ILP formulations for the Clustering Problem, through the enablement of multihop connections and intracluster communication, and assesses the performance of stateofthe art generic ILP and SAT solvers in solving the enhanced formulations.Wireless Communications and Mobile Computing Conference (IWCMC), 2012 8th International; 01/2012  [Show abstract] [Hide abstract]
ABSTRACT: Over the course of the last decade, there have been several improvements in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers. These improvements have encouraged the application of SAT and ILP techniques in modeling complex engineering problems. One such problem is the Clustering Problem in Mobile AdHoc Networks (MANETs). The Clustering Problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads, and ensuring that regular nodes are connected to clusterheads such that the lifetime of the network is maximized. This paper proposes the development of an improved ILP formulation of the Clustering Problem. Additionally, various enhancements are implemented in the form of extensions to the improved formulation, including the establishment of intracluster communication, multihop connections and the enforcement of coverage constraints. The improved formulation and enhancements are implemented in a tool designed to visually create network topologies and cluster them using stateofthe art Generic ILP and SAT solvers. Through this tool, feasibility of using the proposed formulation and enhancements in a reallife practical environment is assessed. It is observed that the Generic ILP solvers, CPLEX, and SCIP, are able to handle large network topologies, while the 0–1 SATbased ILP solver, BSOLO, is effective at handling the smaller scale networks. It is also observed that while these enhanced formulations enable the generation of complex network solutions, and are suitable for small scale networks, the time taken to generate the corresponding solution does not meet the strict requirements of a practical environment. Index Terms— Boolean satisfiability (SAT), integer linear programming, mobile adhoc networks (MANETs), optimization.IEEE Sensors Journal 06/2013; 13(6):24002412. · 1.48 Impact Factor
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