A graphical processing unit-based parallel hybrid genetic algorithm for resource-constrained multi-project scheduling problem

Date

2021-03-01

Author

Uysal, Furkan
Sönmez, Rifat
İŞLEYEN, SELÇUK KÜRŞAT

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this article, we present a parallel graphical processing unit (GPU)-based genetic algorithm (GA) for solving the resource-constrained multi-project scheduling problem (RCMPSP). We assumed that activity pre-emption is not allowed. Problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU-based GA, problem is solved together with a CPU and a GPU. The results showed that GPU-based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large-scale problems.

URI

https://hdl.handle.net/11511/90003

Journal

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

DOI

https://doi.org/10.1002/cpe.6266

Collections

Department of Civil Engineering, Article

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Citation Formats

F. Uysal, R. Sönmez, and S. K. İŞLEYEN, “A graphical processing unit-based parallel hybrid genetic algorithm for resource-constrained multi-project scheduling problem,” CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/90003.