Abstract-In this paper novel project scheduling difficulty estimations are proposed for Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). The main goal of introducing the complexity estimations is an attempt of estimation the project complexity before launching the optimization process. What is more, the dataset instance generator is also presented as a tool to create new instances for extending the research area. Furthermore, the dataset proposed in previous works is extended by new instances, described thoroughly and released as a benchmark dataset. The dataset instances are also scheduled using simple heuristic and greedy algorithm in duration-and cost-oriented optimization modes. Finally, a brief summary of investigated methods and potential further research directions is presented.

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

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This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional 'small' 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 'small' instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage.