Assignment problems in parallel and distributed computing
Assignment problems in parallel and distributed computing
Annals of Operations Research - Special issue on Tabu search
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Scheduling divisible jobs on hypercubes
Parallel Computing
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Scheduling computer and manufacturing processes
Scheduling computer and manufacturing processes
Machine learning and image interpretation
Machine learning and image interpretation
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Tabu Search
NETRA: A Hierarchical and Partitionable Architecture for Computer Vision Systems
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks
Journal of Scheduling
PASM: A Partitionable SIMD/MIMD System for Image Processing and Pattern Recognition
IEEE Transactions on Computers
Minimizing makespan in hybrid flowshops
Operations Research Letters
Performance of particle swarm optimization in scheduling hybrid flow-shops with multiprocessor tasks
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Journal of Parallel and Distributed Computing
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This paper presents the evaluation of the solution quality of heuristic algorithms developed for scheduling multiprocessor tasks for a class of multiprocessor architectures designed to exploit temporal and spatial parallelism simultaneously. More specifically, we deal with multi-level or partitionable architectures where MIMD parallelism and multiprogramming support are the two main characteristics of the system. We investigate scheduling a number of pipelined multiprocessor tasks with arbitrary processing times and arbitrary processor requirements in this system. The scheduling problem consists of two interrelated sub-problems, which are finding a sequence of pipelined multiprocessor tasks on a processor and finding a proper mapping of tasks to the processors that are already being sequenced. For the solution of the second problem, various techniques are available. However, the problem remains of generating a feasible sequence for the pipelined operations. We employed three well-known local search heuristic algorithms that are known to be robust methods applicable to various optimization problems. These are Simulated Annealing, Tabu Search, and Genetic Algorithms. We then conduct computational experiments and evaluate the reduction achieved in completion time by each heuristic. We have also compared the results with well-known simple list-based heuristics.