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  • A genetic algorithm for the resource-constrained project scheduling problem with alternative subgraphs using a boolean satisfiability solver
    Publication . Servranckx, Tom; Coelho, José; Vanhoucke, Mario
    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 small and 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.
  • Project management and scheduling 2022
    Publication . Servranckx, Tom; Coelho, José; Vanhoucke, Mario
    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.
  • Various extensions in resource-constrained project scheduling with alternative subgraphs
    Publication . Servranckx, Tom; Coelho, José; Vanhoucke, Mario
    In this research, we present several extensions for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). First of all, we investigate more complex variants of the alternative project structure. More precisely, we consider nested alterative subgraphs, linked alternative branches, multiple selection, caused and closed choices, and split choices. Secondly, we introduce non-renewable resources in the RCPSP-AS in order to implicitly avoid certain combinations of alternatives given a limited availability of this resource over the complete project horizon. We formulate both the basic RCPSP-AS and its extensions as an ILP model and solve it using Gurobi. The computational experiments are conducted on a large set of artificial project instances as well as three case studies. The results show the impact of the different extensions on the project makespan and the computational complexity. We observe that combinations of the proposed extensions might imply complex alternative project structures, resulting in an increasing computational complexity or even infeasible solutions. The analysis of the three case studies shows that it is hard to find feasible solutions with a small time limit or optimal solutions with a larger time limit for projects with a realistic size in terms of the number of activities or alternatives.