Combining giant pacific octopus optimizer and multi-objective optimization for the multi-mode resource-constrained multi-project scheduling problem

Purpose This research aims to introduce an algorithm, the Multi-objective Giant Pacific Octopus Optimizer (MOGPOO) and demonstrate its use in balancing three parameters: time, cost, quality in the Multi-Mode Resource-constrained Multi-Project Scheduling Problem (MRCMPSP). Design/methodology/approach The aim of the project is to combine the Multi-objective Optimization with a Giant Pacific Octopus Optimizer (GPOO) – swarm intelligence approach. The study compared the derivatives of the MOGPOO optimization method to those of other algorithms, such as the Multi-objective Slime Mold Algorithm (MOSMA) and the Multi-objective Grey Wolf Optimization (MOGWO), and testing the model’s performance and assessing the construction problem, a total of nine projects in the essential MRCMPSP problem are taken into consideration from the resources that are accessible. Findings Compared to the other approaches, MOGPOO performed better on the majority of the assessment criteria. This study and its results can be useful for researchers working on multi-project scheduling models. In addition, the superiority of MOGPOO becomes increasingly evident as the complexity of the problem increases. Originality/value A hybrid swarm intelligence model and strategy that allows Multi-Mode Resource-constrained Multi-Project Scheduling Problem in construction management is presented in the study in order to optimize the ideal solution in the search space. Based on the findings of this study’s testing procedure, a strong model that outperformed the examined models was constructed.