Investigation of benchmark dataset for many-objective Multi-Skill Resource Constrained Project Scheduling Problem

Abstract

The paper presents a redefinition of Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) as a many-objective optimization problem. In effect, it brings the problem closer to real-world applications. Five objectives have been defined: cost, duration, average cash flow, average usage of resources and skill overuse. To summarize MS-RCPSP usage, the paper presents a short survey (years 2015–2021) of multi- and many-objective MS–RCPSP solving methods. Moreover, results of investigation of many-objective MS–RCPSP for classic (greedy algorithm, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III)), single objective (decomposition-based) Differential Evolution And Greedy (DEGR) and state-of-the-art Non-dominated Tournament Genetic Algorithm (NTGA2) have been presented. Additionally, results are analysed using multi-objective quality measures (such as Pareto Front Size (PFS), Purity or Inverted Generational Distance (IGD)) to verify the efficiency of the used methods. The paper answers 4 research questions that investigate 5-objective MS-RCPSP domains, approximations of Pareto Fronts, relations between objectives and efficiency of NTGA2 compared to other methods. Finally, the paper is concluded with a summary and propositions for future work.