Parallelized Population-based Multi-heuristic Approach for Solving RCPSP/max

Project scheduling with resource constraints plays a critical role in numerous application domains, including logistics, manufacturing, management, healthcare, and computer science. One of them is the Resource-Constrained Project Scheduling Problem with Generalized Precedence Constraints (RCPSP/max), which incorporates both minimum and maximum time lags. Due to its computational complexity, obtaining high-quality solutions for instances of even moderate size remains a difficult task. To address this challenge, a wide range of heuristic and metaheuristic approaches have been proposed in the literature. A promising direction involves parallelizing computations and combining multiple heuristic or metaheuristic methods to enhance performance and solution quality. This paper presents a set of heuristic algorithms and a parallelized, population-based multi-heuristic system implemented in the Apache Spark environment. The proposed approach offers a solution method for RCPSP/max instances and has been validated through computational experiments using benchmark datasets from the PSPLIB library.