Abstract
This paper presents a new integrated model for the multi-skilled project scheduling and multi-project scheduling problems, where the activities are technically linked through the generalized precedence relations (GPR). The proposed model is energy-efficient since the concerns regarding the energy consumptions of projects have been embraced as well. Reviewing the pertinent literature has revealed that the energy-efficient formulations for the multi-skill and multi-project scheduling problems with the GPR connections are very rare. For this formulation, two minimization objectives have been defined: (1) the overall duration to finish all projects and (2) the total energy consumption of all projects. This research offers a new version of the multi-objective vibration damping optimization (MOVDO) method to solve the proposed model. For the MOVDO, a new crossover-like operator has been designed that generates offspring solutions through the swapping procedure. Furthermore, a new population-updating strategy inspired by the Toom’s rule cellular automaton has been devised for the MOVDO. In this strategy, each solution of the population can be replaced with its neighbor solutions based on the dominance criterion and the rules of the Toom’s cellular automaton. Three other solution methodologies have been hired to solve the model in order to have a fair judgment on the efficacy of the MOVDO. The comparisons between the MOVDO and other optimizers have been conducted based on five performance assessment metrics. The results demonstrate the remarkable dominance of the MOVDO over other algorithms.




















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Ehsan Goudarzi: Subject, Methodology, Algorithm, Mathematical modeling, Writing, Analysis. Hamid Esmaeeli: Validation, Supervision, Project administration. Kia Parsa: Validation, Supervision, Consultations. Shervin Asadzadeh: Validation, Supervision, Consultations.
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Goudarzi, E., Esmaeeli, H., Parsa, K. et al. Energy-efficient resource-constrained multi-project scheduling problem with generalized precedence relations and multi-skilled resources. J Supercomput 80, 13837–13872 (2024). https://doi.org/10.1007/s11227-024-05933-0
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DOI: https://doi.org/10.1007/s11227-024-05933-0