Abstract
The performance of methods to manage projects depends heavily on the features of their project networks. This is particularly true for methods devoted to project scheduling, risk analysis and resources allocation. Therefore, a long line of research has been developed to generate benchmark sets of project networks and several sets have been proposed in the literature. Unfortunately, no comparative analyses of their features were published and hence serious doubts about the comparability of results using different benchmark sets can be raised. In this paper, a multi-dimensional taxonomy for the morphology of project networks is used and four benchmark sets are evaluated: Patterson collection of problems (Patterson JH. A comparison of exact approaches for solving the multiple constrained resource, project scheduling problem. Management Science 1984;30:854-867) and the sets produced by the generators due to Agrawal et al. Agrawal MK, Elmaghraby SE, Herroelen WS. DAGEN a generator of testsets for project activity nets. European Journal of Operational Research 1996;90:376-382. Kolisch R, Sprecher A, Drexl A. Characterization and generation of a general class of resource - constrained project scheduling problems. Management Science 1995;41:1693-1703. Tavares Tavares LV. Advanced models in project management. Kluwer, 1999 and Tavares et al. Tavares LV, Antunes Ferreira JA. Coelho JS. The risk of delay of a project in terms of the morphology of its network. European Journal of Operational Research 1999;119:510-537. Original results about the lack of representativeness of these sets are obtained showing that misleading conclusions can be deduced. The last set is, by far, that one covering most extensively the morphologic space of instances which could be foreseen because the generation of networks is carried out in terms of an wider range of parameters. This conclusion is quite useful for project managers willing to assess alternative methods to solve their problems based on project networks.

Abstract
This paper reports on a new solution approach for the well-known multi-mode resource-constrained project scheduling problem (MRCPSP). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and non-renewable) resource feasible project schedule with a minimal makespan. The problem type is known to be NP-hard and has been solved using various exact as well as (meta-)heuristic procedures. The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resource-constrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean non-renewable resource constraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time.

Abstract
This paper evaluates and compares different network generators to generate project scheduling problem instances based on indicators measuring the topological network structure. We review six topological network indicators in order to describe the detailed structure of a project network. These indicators were originally developed by [L.V. Tavares, J.A. Ferreira and J.S. Coelho, The risk of delay of a project in terms of the morphology of its network, European Journal of Operational Research 119 (1999), 510-537] and have been modified, or sometimes completely replaced, by alternative indicators to describe the network topology. The contribution of this paper is twofold. Firstly, we generate a large amount of different networks with four project network generators. Our general conclusions are that none of the network generators are able to capture the complete feasible domain of all networks. Additionally, each network generator covers its own network-specific domain and, consequently, contributes to the generation of data sets. Secondly, we perform computational results on the well-known resource-constrained project scheduling problem to prove that our indicators are reliable and have significant, predictive power to serve as complexity indicators.

Abstract
The resource-constrained project scheduling problem is a widely studied problem in the literature. The goal is to construct a schedule for a set of activities, such that precedence and resource constraints are respected and that an objective function is optimized. In project scheduling literature, summary measures are often used as a tool to evaluate the performance of algorithms and to analyze instances and datasets. They can be classified in two groups, network measures describe the precedence constraints of a project, while resource measures focus on the resource constraints of the instance. In this manuscript we make an exhaustive evaluation of the summary measures for project scheduling. We provide an overview of the most prevalent measures and also introduce some new ones. For our tests we combine different datasets from the literature and generate a new set with diverse characteristics. We evaluate the performance of the summary measures on three dimensions: consistency, instance complexity and algorithm selection. We conclude by providing an overview of which measures are best suited for each of the three investigated dimensions.

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.

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.