Recent Developments of Computational Intelligence for Resource Constrained Project Scheduling Problems: A Taxonomy and Review
نویسنده
چکیده
This article presents a broad overview of applications of Computational Intelligence (CI) paradigms in resource constrained project scheduling problems (RCPSP) including Fuzzy system (FS), Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Tabu Search (TS), Genetic Algorithms (GA), Simulated Annealing (SA) and other metaheuristics techniques. Recent developments of computational intelligence techniques and its implementations to project scheduling problems are reviewed. Various types of RCPSP and its extensions as a single objective and multiobjective models are introduced. Applications of CI techniques to RCPSP are classified and analyzed on various dimensional including CI paradigms, publication years and numbers, type of RCPSP models as single objective optimization model and multiobjective model as well as RCPSP with renewable, nonrenewable, partially renewable resource constrained. In addition to, a discussion of how these CI paradigms could be applied to solve RCPSP problems and how RCPSP could be analyzed, processed, and optimized using CI paradigms. Challenges and promising research directions in the field of CI and RCPSP are addressed. Keywords— Resource constrained project scheduling, Multimode, Combinatorial Optimization, Computational intelligence, Hybrid Computational Intelligence.
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