Heuristic Optimization for the Resource Constrained Project Scheduling Problem: a Systematic Mapping
Aurelia Ciupe, Serban Meza, Bogdan Orza
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 619–626 (2016)
Abstract. Context: Heuristic optimization has been of strong focus in recent modeling of the Resource Constrained Project Scheduling Problem (RCPSP), but lack of evidence exists in providing a systematic assessment. New solution methods arise from random evaluation of existing studies. Objective: The current work conducts a secondary study, aiming to systemize existing primary studies in heuristic optimization techniques applied to solving classes of RCPSPs. Method: The systemizing framework consists of performing a systematic mapping study, following a 3-steped protocol. Results: 295 primary studies have been depicted from the multi-stage search and filtering process, to which inclusion and exclusion criteria have been applied. Results have been visually mapped under several distributions. Conclusions: Specific RCPSP classes have been grounded and therefore a rigorous classification is required before performing a systematic mapping. Focusing on recent developments of the RCPSP, between 2010-2015, a strong interest has been acknowledged on solution methods incorporating AI techniques, in meta- and hyper-heuristic algorithms.
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