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Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling

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  • Antonio Lova
  • Pilar Tormos

Abstract

Frequently, the availability of resources assigned to a project is limited and not sufficient to execute all the concurrent activities. In this situation, decision making about their schedule is necessary. Many times this schedule supposes an increase in the project completion time. Additionally, companies commonly manage various projects simultaneously, sharing a pool of renewable resources. Given these resource constraints, we often can only apply heuristic methods to solve the scheduling problem. In this work the effect of the schedule generation schemes – serial or parallel – and priority rules – MINLFT, MINSLK, MAXTWK, SASP or FCFS – with two approaches – multi-project and single-project – are analysed. The time criteria considered are the mean project delay and the multiproject duration increase. Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multiproject duration increase. New heuristics – based on priority rules with a two-phase approach – that outperform classical ones are proposed to minimise mean project delay with a multi-project approach. Finally, the best heuristics analysed are evaluated together with a representative sample of commercial project management software. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Antonio Lova & Pilar Tormos, 2001. "Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling," Annals of Operations Research, Springer, vol. 102(1), pages 263-286, February.
  • Handle: RePEc:spr:annopr:v:102:y:2001:i:1:p:263-286:10.1023/a:1010966401888
    DOI: 10.1023/A:1010966401888
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    Citations

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    Cited by:

    1. Ben Issa, Samer & Patterson, Raymond A. & Tu, Yiliu, 2021. "Solving resource-constrained multi-project environment under different activity assumptions," International Journal of Production Economics, Elsevier, vol. 232(C).
    2. Feifei Li & Zhe Xu, 2018. "A multi-agent system for distributed multi-project scheduling with two-stage decomposition," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-24, October.
    3. Krüger, Doreen & Scholl, Armin, 2009. "A heuristic solution framework for the resource constrained (multi-)project scheduling problem with sequence-dependent transfer times," European Journal of Operational Research, Elsevier, vol. 197(2), pages 492-508, September.
    4. Gómez Sánchez, Mariam & Lalla-Ruiz, Eduardo & Fernández Gil, Alejandro & Castro, Carlos & Voß, Stefan, 2023. "Resource-constrained multi-project scheduling problem: A survey," European Journal of Operational Research, Elsevier, vol. 309(3), pages 958-976.
    5. Wuliang Peng & Jiali lin & Jingwen Zhang & Liangwei Chen, 2022. "A bi-objective hierarchical program scheduling problem and its solution based on NSGA-III," Annals of Operations Research, Springer, vol. 308(1), pages 389-414, January.
    6. Anıl Can & Gündüz Ulusoy, 2014. "Multi-project scheduling with two-stage decomposition," Annals of Operations Research, Springer, vol. 217(1), pages 95-116, June.
    7. 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.
    8. Browning, Tyson R. & Yassine, Ali A., 2010. "Resource-constrained multi-project scheduling: Priority rule performance revisited," International Journal of Production Economics, Elsevier, vol. 126(2), pages 212-228, August.
    9. M. Suresh & Pankaj Dutta & Karuna Jain, 2015. "Resource Constrained Multi-Project Scheduling Problem with Resource Transfer Times," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(06), pages 1-30, December.
    10. Bredael, Dries & Vanhoucke, Mario, 2023. "Multi-project scheduling: A benchmark analysis of metaheuristic algorithms on various optimisation criteria and due dates," European Journal of Operational Research, Elsevier, vol. 308(1), pages 54-75.
    11. Rob Eynde & Mario Vanhoucke, 2020. "Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling," Journal of Scheduling, Springer, vol. 23(3), pages 301-325, June.
    12. Esmaeil MEHDIZADEH & Hamidreza AKBARI, 2017. "A Novel Vibration Damping Optimization Algorithm for Resource Constrained Multi- Project Scheduling Problem," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(2), pages 291-309.
    13. Yaghoubi, Saeed & Noori, Siamak & Azaron, Amir & Fynes, Brian, 2015. "Resource allocation in multi-class dynamic PERT networks with finite capacity," European Journal of Operational Research, Elsevier, vol. 247(3), pages 879-894.
    14. Yaghoubi, Saeed & Noori, Siamak & Azaron, Amir & Tavakkoli-Moghaddam, Reza, 2011. "Resource allocation in dynamic PERT networks with finite capacity," European Journal of Operational Research, Elsevier, vol. 215(3), pages 670-678, December.
    15. Aidin Delgoshaei & Timon Rabczuk & Ahad Ali & Mohd Khairol Anuar Ariffin, 2017. "An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources," Annals of Operations Research, Springer, vol. 259(1), pages 85-117, December.
    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. Giuseppe Confessore & Stefano Giordani & Silvia Rismondo, 2007. "A market-based multi-agent system model for decentralized multi-project scheduling," Annals of Operations Research, Springer, vol. 150(1), pages 115-135, March.

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