Abstract
This paper solves the resource-constrained project scheduling problem (RCPSP) with a satisfiability problem (SAT) solver. This paper builds further on various existing SAT models for this well-known project scheduling problem and extends them with two methods to satisfy the resource constraints. Specifically, we use the wellknown minimal forbidden sets and compare them with the so-called covers that are traditionally used in SAT implementations. Moreover, we also implement an existing binary decision trees approach under various settings and extend the model with networks with adders, so far never used for solving the RCPSP, to guarantee that resource constraints are satisfied. The algorithms are tested under different settings on a set of 13,413 project instances with diverse network and resource structures, and the experiments demonstrate that a combination of these approaches help in finding better solutions within a reasonable time. Moreover, 393 new lower bounds, 62 new upper bounds, and 290 optimally solved instances (including 18 from the PSPLIB) have been discovered, which, to the best of our knowledge, had not been found before. The strong performance of the new algorithm motivated additional experiments, and the preliminary results suggest several promising directions for future research.

Abstract
The branch-and-bound (B&B) procedure is one of the most frequently used methods for solving the resource-constrained project scheduling problem (RCPSP) to obtain optimal solutions and has a rich history in the academic literature. Over the past decades, various variants of this procedure have been proposed, each using slightly different configurations to search for the optimal solution. While most of the configurations perform relatively well for many problem instances, there is, however, no known universal best B&B configuration that works well for all problem instances. In this work, we propose two problem transformation-based machine learning classification methods (binary relevance and classifier chains) to automatically detect the best-performing branch-and-bound configuration for the resource-constrained project scheduling problem. The proposed novel learning models aim to find the relationship between the project characteristics and the performance of a specific B&B configuration. With this obtained knowledge, the best-performing B&B configurations can be predicted, resulting in a better solution. A comprehensive computational experiment is conducted to demonstrate the effectiveness of the proposed classification models and the performance improvements over three categories of methods from the literature, including the latest branch-and-bound configurations, the state-of-the-art classification models in project scheduling, and commonly used clustering algorithms in machine learning. The results show that the proposed classification models can enhance solution quality for the RCPSP without changing the core components of existing algorithms. More specifically, the classifier chains method, when combined with the Back-Propagation Neural Network algorithm, achieves the best performance, outperforming binary relevance, which demonstrates the impact of label correlation on the performance. The experiments also demonstrate the merits of the proposed model in improving the robustness of the solutions. Furthermore, these findings not only highlight the potential of the classification models in detecting best-performing B&B configurations, but also emphasize the need for future work and development to further improve the performance and applicability of these models.

Abstract
This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community.

Abstract
This paper presents a matheuristic solution algorithm to solve the well-known resource-constrained project scheduling problem (RCPSP). The problem makes use of a restricted neighbourhood method using an activity selection and a search space restriction module and implements them as two alternative search algorithms. The first algorithm makes use of the best-performing components of the branch-and-bound procedures from the literature, and embeds them into a greedy neighbourhood search. The second matheuristic implements the exact branch-and-bound procedures into a known and well-performing meta-heuristic search algorithm. Computational experiments have been carried out on seven different datasets consisting of 10,000+ project instances. Experiments reveal that the choice of exact algorithm is key in finding high-quality solutions, and illustrate that the trade-off between selecting an activity set size and search space restriction depends on the specific implementation. The computational tests demonstrate that the matheuristic discovered 24 new best known solutions that could not be found by either a meta-heuristic or an exact method individually. Moreover, a new benchmark dataset has been proposed that can be used to develop new matheuristic search procedures to solve the problem consisting of 461 instances from the literature.

Abstract
This study evaluates a new solution approach for the Resource -Constrained Project Scheduling with Alternative Subgraphs (RCPSP-AS) in case that complex relations (i.e. nested and linked alternatives) are considered. In the RCPSP-AS, the project activity structure is extended with alternative activity sequences. This implies that only a subset of all activities should be scheduled, which corresponds with a set of activities in the project network that model an alternative execution mode for a work package. Since only the selected activities should be scheduled, the RCPSP-AS comes down to a traditional RCPSP problem when the selection subproblem is solved. It is known that the RCPSP and, hence, its extension to the RCPSP-AS is NP -hard. Since similar scheduling and selection subproblems have already been successfully solved by satisfiability (SAT) solvers in the existing literature, we aim to test the performance of a GA -SAT approach that is derived from the literature and adjusted to be able to deal with the problem -specific constraints of the RCPSP-AS. Computational results on smalland large-scale instances (both artificial and empirical) show that the algorithm can compete with existing metaheuristic algorithms from the literature. Also, the performance is compared with an exact mathematical solver and learning behaviour is observed and analysed. This research again validates the broad applicability of SAT solvers as well as the need to search for better and more suited algorithms for the RCPSP-AS and its extensions.