Abstract
Resource-constrained project scheduling for ship refit and maintenance is a major challenge for planners. A smart scheduling solution proposed herein relies on a combination of optimization methods including constraint programming and mixed-integer linear programming. The method employs model-based AI, heuristic methods and discrete-event simulation to efficiently schedule project tasks while handling precedence constraints, resource constraints (labor, equipment) and capacity constraints. The present study investigated the key challenge of managing geospatial constraints. A new solution is presented that captures in a generic fashion the geospatial relationships of work areas and that analyses schedules to detect proximity and path-based conflicts occurring over the course of the project plan. A visualization support tool was designed that generates an abstract 3D model to help intuitively understand and contextualize detected conflicts. Solution effectiveness was assessed and validated using a test scenario. The resulting method is deemed applicable to a broad range of domains.
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Acknowledgments
Thanks are due to the many members of the Refit Optimizer project team and collaborators. Special thanks to Rob Scott (Genoa Design), Prof. Claver Diallo (Dalhousie U.), LCdr. Eric Bertrand (Royal Canadian Navy), Prof. Claude-Guy Quimper (Université Laval), Prof. Robert Pellerin, Prof. Issmail El Hallaoui, Prof. François Soumis and Hugues Delmaire (Polytechnique Montreal), André Jacques (Simwell), Cynthia Pouliot, Wayne Brewster and Rod McMullin (Thales Canada). We are very grateful to the many domain experts consulted and to Seaspan Victoria Shipyards for their invaluable feedback. This project has received financial support from the Scale AI Canadian Innovation Supercluster and from the Mitacs Accelerate program.
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Lafond, D., Couture, D., Delaney, J., Cahill, J., Corbett, C., Lamontagne, G. (2021). Multi-objective Schedule Optimization for Ship Refit Projects: Toward Geospatial Constraints Management. In: Ahram, T., Taiar, R., Groff, F. (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIET-AI 2021. Advances in Intelligent Systems and Computing, vol 1378. Springer, Cham. https://doi.org/10.1007/978-3-030-74009-2_84
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DOI: https://doi.org/10.1007/978-3-030-74009-2_84
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