Abstract Construction managers often face with projects containing multiple units wherein activities repeat from unit to unit. Therefore effective resource management is crucial in terms of project duration, cost and quality. Accordingly, researchers have developed several models to aid planners in developing practical and near-optimal schedules for repetitive projects. Despite their undeniable benefits, such models lack the ability of pure simultaneous optimization because existing methodologies optimize the schedule with respect to a single factor, to achieve minimum duration, total cost, resource work breaks or various combinations, respectively. This study introduces a novel approach called “opposition multiple objective symbiotic organisms search” (OMOSOS) for scheduling repetitive projects. The proposed algorithm used an opposition-based learning technique for population initialization and for generation jumping. Further, this study integrated a scheduling module (M1) to determine all project objectives including time, cost, quality and interruption. The proposed algorithm was implemented on two application examples in order to demonstrate its capabilities in optimizing the scheduling of repetitive construction projects. The results indicate that the OMOSOS approach is a powerful optimization technique and can assist project managers in selecting appropriate plan for project. Highlights This study presents an advanced multiple optimization algorithm OMOSOS. Opposition technique is utilized to spreading the initial population and generation jumping. OMOSOS is applied to solve time, cost, quality and work continuity tradeoff problem. The model performance is demonstrated in the experimental results.
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