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A graphical processing unit‐based parallel hybrid genetic algorithm for resource‐constrained multi‐project scheduling problem
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DOI：10.1002/cpe.6266.png)
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Abstract
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SummaryIn 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.
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