Integer and combinatorial optimization
Integer and combinatorial optimization
GEMPACK: General-purpose software for applied general equilibrium and other economic modellers
Computer Science in Economics and Management
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
A modeling language for mathematical programming
Management Science
A worldwide flock of Condors: load sharing among workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
High-throughput resource management
The grid
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
FATCOP: A Fault Tolerant Condor-PVM Mixed Integer Programming Solver
SIAM Journal on Optimization
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
A Parallel, Linear Programming-based Heuristic for Large-Scale Set Partitioning Problems
INFORMS Journal on Computing
An Enabling Framework for Master-Worker Applications on the Computational Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid
INFORMS Journal on Computing
Operations Research Letters
Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid
INFORMS Journal on Computing
International Journal of Decision Support System Technology
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We describe a framework for modeling optimization problems for solution on a grid computer. The framework is easy to adapt to multiple grid engines and can seamlessly integrate evolving mechanisms from particular computing platforms. It facilitates the widely used master-worker model of computing and is shown to be flexible and powerful enough for a large variety of optimization applications. In particular, we summarize a number of new features of the GAMS modeling system that provide a lightweight, portable, and powerful framework for optimization on a grid. We provide downloadable examples of its use for embarrasingly parallel financial applications, decomposition of complementarity problems, and for solving very difficult mixed-integer programs to optimality. Computational results are provided for a number of different grid engines, including multicore machines, a pool of machines controlled by the Condor resource manager, and the grid engine from Sun Microsystems.