A graphical processing unit‐based parallel hybrid genetic algorithm for resource‐constrained multi‐project scheduling problem. (16th March 2021)

• Record Type: Journal Article
• Authors: Uysal, Furkan; Sonmez, Rifat; Isleyen, Selcuk Kursat
• Is Part Of: Concurrency and computation. Volume 33:Number 16(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-16
• Subjects: parallel genetic algorithm -- resource constrained multi‐project scheduling problem -- resource constrained project scheduling problem -- resource management; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6266
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.