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An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes

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  • Lova, Antonio
  • Tormos, Pilar
  • Cervantes, Mariamar
  • Barber, Federico

Abstract

Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) aims at finding the start times and execution modes for the activities of a project that optimize a given objective function while verifying a set of precedence and resource constraints. In this paper, we focus on this problem and develop a hybrid Genetic Algorithm (MM-HGA) to solve it. Its main contributions are the mode assignment procedure, the fitness function and the use of a very efficient improving method. Its performance is demonstrated by extensive computational results obtained on a set of standard instances and against the best currently available algorithms.

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  • Lova, Antonio & Tormos, Pilar & Cervantes, Mariamar & Barber, Federico, 2009. "An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes," International Journal of Production Economics, Elsevier, vol. 117(2), pages 302-316, February.
    • Handle: RePEc:eee:proeco:v:117:y:2009:i:2:p:302-316
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    Cited by:

    1. V. Van Peteghem & M. Vanhoucke, 2009. "Using Resource Scarceness Characteristics to Solve the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/595, Ghent University, Faculty of Economics and Business Administration.
    2. Van Peteghem, Vincent & Vanhoucke, Mario, 2014. "An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances," European Journal of Operational Research, Elsevier, vol. 235(1), pages 62-72.
    3. Kellenbrink, Carolin & Helber, Stefan, 2015. "Scheduling resource-constrained projects with a flexible project structure," European Journal of Operational Research, Elsevier, vol. 246(2), pages 379-391.
    4. Leyman, Pieter & Vanhoucke, Mario, 2017. "Capital- and resource-constrained project scheduling with net present value optimization," European Journal of Operational Research, Elsevier, vol. 256(3), pages 757-776.
    5. HazIr, Öncü & Erel, Erdal & Günalay, Yavuz, 2011. "Robust optimization models for the discrete time/cost trade-off problem," International Journal of Production Economics, Elsevier, vol. 130(1), pages 87-95, March.
    6. Ramírez Palencia, Alberto E. & Mejía Delgadillo, Gonzalo E., 2012. "A computer application for a bus body assembly line using Genetic Algorithms," International Journal of Production Economics, Elsevier, vol. 140(1), pages 431-438.
    7. Kadri, Roubila Lilia & Boctor, Fayez F., 2018. "An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case," European Journal of Operational Research, Elsevier, vol. 265(2), pages 454-462.
    8. Weglarz, Jan & Józefowska, Joanna & Mika, Marek & Waligóra, Grzegorz, 2011. "Project scheduling with finite or infinite number of activity processing modes - A survey," European Journal of Operational Research, Elsevier, vol. 208(3), pages 177-205, February.
    9. Coelho, José & Vanhoucke, Mario, 2011. "Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers," European Journal of Operational Research, Elsevier, vol. 213(1), pages 73-82, August.
    10. T Wauters & K Verbeeck & G Vanden Berghe & P De Causmaecker, 2011. "Learning agents for the multi-mode project scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 281-290, February.
    11. Ilkyeong Moon & Sanghyup Lee & Moonsoo Shin & Kwangyeol Ryu, 2016. "Evolutionary resource assignment for workload-based production scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 375-388, April.
    12. Cédric Verbeeck & Vincent Peteghem & Mario Vanhoucke & Pieter Vansteenwegen & El-Houssaine Aghezzaf, 2017. "A metaheuristic solution approach for the time-constrained project scheduling problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 353-371, March.
    13. Ho, William & Ji, Ping, 2010. "Integrated component scheduling models for chip shooter machines," International Journal of Production Economics, Elsevier, vol. 123(1), pages 31-41, January.
    14. Geiger, Martin Josef, 2017. "A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 256(3), pages 729-741.
    15. Alfredo S. Ramos & Pablo A. Miranda-Gonzalez & Samuel Nucamendi-Guillén & Elias Olivares-Benitez, 2023. "A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    16. He, Naihui & Zhang, David Z. & Yuce, Baris, 2022. "Integrated multi-project planning and scheduling - a multiagent approach," European Journal of Operational Research, Elsevier, vol. 302(2), pages 688-699.
    17. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    18. Siqing Shan & Zhongjun Hu & Zhilian Liu & Jihong Shi & Li Wang & Zhuming Bi, 2017. "An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly," Information Technology and Management, Springer, vol. 18(1), pages 41-53, March.
    19. Abdollah Arasteh, 2020. "Considering Project Management Activities for Engineering Design Groups," SN Operations Research Forum, Springer, vol. 1(4), pages 1-29, December.
    20. Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustin Barrios-Sarmiento, 2021. "A memetic algorithm to address the multi-node resource-constrained project scheduling problem," Journal of Scheduling, Springer, vol. 24(4), pages 413-429, August.
    21. Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustín Barrios-Sarmiento, 2022. "An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 221-248, July.
    22. Len Vandenheede & Mario Vanhoucke & Broos Maenhout, 2016. "A scatter search for the extended resource renting problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4723-4743, August.
    23. Peteghem, Vincent Van & Vanhoucke, Mario, 2010. "A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 201(2), pages 409-418, March.

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