Abstract
The resource constrained multi-project scheduling problem (RCMPSP) is a well-known NP-hard problem. In this study, a surrogate-assisted genetic algorithm (SaGA) is presented for solving the RCMPSP. A non-random initialization starts the SaGA with a certain diversity and quality. A forward-backward improvement (FBI) based local search is utilized to intensify high quality solutions. Surrogation in genetic algorithm (GA) estimates few individual’s fitness rather than determining the actual fitness value. It maintains population’s diversity while optimizing the solution of the GA in the meantime. The performance of the proposed SaGA is examined on standard 10 benchmark examples ranging from 2 to 5 projects in a multi-project set. The comparative computational results with the state-of-the-art algorithms show the effectiveness of the proposed SaGA to achieve a lower value of projects total makespan (TMS).
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Asadujjaman, M., Rahman, H.F., Chakrabortty, R.K., Ryan, M.J. (2023). Surrogate-assisted Genetic Algorithm for Multi-project Scheduling. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_9
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