Abstract
<p>In the paper it is shown that time necessary to solve the NP-hard Resource-Constrained Project Scheduling Problem (RCPSP) could be considerably reduced using a low-cost multicomputer. We consider an extension of the problem when resources are only partially available and a deadline is given but the cost of the project should be minimized. In such a case nding an acceptable solution (optimal or even semi-optimal) is computationally very hard. To reduce this complexity a distributed processing model of a metaheuristic algorithm, previously adapted by us for working with human resources and the CCPM method, was developed. Then, a new implementation of the model on a low-cost multicomputer built from PCs connected through a local network was designed and compared with regular implementation of the model on a cluster. Furthermore, to examine communication costs, an implementation of the model on a single multi-core PC was tested, too.<br />The comparative studies proved that the implementation is as ecient as on more expensive cluster. Moreover, it has balanced load and scales well.</p>
Highlights
Resource allocation, called the Resource-Constrained Project Scheduling Problem (RCPSP), attempts to reschedule project tasks efficiently using limited renewable resources minimising the maximal completion time of all activities [3 - 5]
The algorithm scalability depends on the number of human resource (HR) because it is related to the number of schedule modifications
M In the research, a distributed model was used in order to reduce the computation time for a solution of the RCPSP when resources are partially available
Summary
S KEY WORDS: RCPSP, multicomputer, distributed processing model C ABSTRACT: In the paper it is shown that time necessary to solve the NP-hard Resource-. We consider an extension of the problem when resources are only partially available and a deadline is given but the cost of the project should be minimized In such a case finding an acceptable solution (optimal or even semi-optimal) is computationally very hard. To reduce this complexity a distributed processing model of a metaheuristic algorithm, previously adapted by us for working with human resources and the CCPM method, was developed. The comparative studies proved that the implementation is as efficient as on more expensive cluster. It has balanced load and scales well
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