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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahluwalia, R., Pinha, D.: Decision support system for production planning in the ship repair industry. Ind. Syst. Eng. Rev. 2(1), 52–61 (2014)
Bertrand, E.L.J.: Optimization of the naval surface ship resource-constrained project scheduling problem. Doctoral thesis. Dalhousie University, Halifax (2020)
Kelley, Jr. J.E., Walker, M.R.: Critical-path planning and scheduling. In: Papers Presented at the 1–3 December 1959, Eastern Joint IRE-AIEE-ACM Computer Conference, pp. 160–173 (1959)
Malcolm, D.G., Roseboom, J.H., Clark, C.E., Fazar, W.: Application of a technique for research and development program evaluation. Oper. Res. 7(5), 646–669 (1959)
Brucker, P., Drexl, A., Möhring, R., Neumann, K., Pesch, E.: Resource-constrained project scheduling: Notation, classification, models, and methods. Eur. J. Oper. Res. 112(1), 3–41 (1999)
Oddi, A., Rasconi, R.: Solving resource-constrained project scheduling problems with time-windows using iterative improvement algorithms. In: Proceedings of the Nineteenth International Conference on International Conference on Automated Planning and Scheduling, pp. 378–381 (2009)
Bhaskar, T., Pal, M.N., Pal, A.K.: A heuristic method for RCPSP with fuzzy activity times. Eur. J. Oper. Res. 208(1), 57–66 (2011)
Deblaere, F., Demeulemeester, E., Herroelen, W.: Proactive policies for the stochastic resource-constrained project scheduling problem. Eur. J. Oper. Res. 214(2), 308–316 (2011)
Koné, O., Artigues, C., Lopez, P., Mongeau, M.: Comparison of mixed integer linear programming models for the resource-constrained project scheduling problem with consumption and production of resources. Flex. Serv. Manuf. J. 25(1-2), 25–47 (2013)
Mattioli, J., Robic, P.-O., Reydellet, T.: L’intelligence artificielle au service de la maintenance previsionnelle. In: APIA: Application Pratique en Intelligence Artificielle, hal-01830917 (2018)
Pellerin, R., Perrier, N., Berthaut, F.: A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 280(2), 395–416 (2020)
Erl, T.: SOA Design Patterns. Pearson Education, London (2008)
Caprace, J., Petcu, C., Velarde, M., Rigo, P.: Optimization of shipyard space allocation and scheduling using a heuristic algorithm. J. Mar. Sci. Technol. 18(3), 404–417 (2013)
Kleppmann, M.: Designing Data-intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O’Reilly Media, Sebastopol (2017)
Silverston, L., Agnew, P.: The Data Model Resource Book: Universal Patterns for Data Modeling. Wiley, New York (2009)
Le Huede, F., Grabisch, M., Labreuche, C., Saveant, P.: Integration and propagation of a multi-criteria decision making model in constraint programming. J. Heuristics 12(4–5), 329–346 (2006)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-74009-2_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73270-7
Online ISBN: 978-3-030-74009-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)