Document Type : Research Paper
Authors
1 Ph.D. Candidate in Industrial engineering Technology Management, Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.
2 Assistant Prof., Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.
3 Associate Prof, Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.
4 Associate Prof., Department of Industrial Engineering, Bon.C., Islamic Azad University, Bonab, Iran.
Abstract
Keywords
Abdolshah, M. (2014). A review of resource-constrained project scheduling problems (RCPSP) approaches and solutions. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 5(4), 253–286. http://tuengr.com/V05/0253.pdf
Agarwal, A., Colak, S., & Erenguc, S. (2015). Metaheuristic methods. Handbook on Project Management and Scheduling, 1, 57–74. https://doi.org/10.1007/978-3-319-05443-8_4
Aghileh, M., Tereso, A., Alvelos, F., & Lopes, M. O. M. (2025). Multi-Project Scheduling with Uncertainty and Resource Flexibility: A Narrative Review and Exploration of Future Landscapes. Algorithms, 18(6), 314. https://doi.org/10.3390/a18060314
Baharum, Z., Venkatesan, Y. R., Raidzuan, S. N. M., & Qureshi, M. I. (2018). The development of simulation model on environmental uncertainty factors for interval project completion. International Journal of Engineering & Technology, 7(2.29), 62–66. https://doi.org/10.14419/ijet.v7i2.29.13130
Bakhshi, R., Moradinia, S. F., Jani, R., & Poor, R. V. (2022). Presenting a hybrid scheme of machine learning combined with metaheuristic optimizers for predicting final cost and time of project. KSCE Journal of Civil Engineering, 26(8), 3188–3203. https://doi.org/10.1007/s12205-022-1424-3
Bakry, I., Moselhi, O., & Zayed, T. (2016). Optimized scheduling and buffering of repetitive construction projects under uncertainty. Engineering, Construction and Architectural Management, 23(6), 782–800. https://doi.org/10.1108/ECAM-05-2014-0069
Ballesteros-Pérez, P., Cerezo-Narváez, A., Otero-Mateo, M., Pastor-Fernández, A., & Vanhoucke, M. (2019). Performance comparison of activity sensitivity metrics in schedule risk analysis. Automation in Construction, 106, 102906. https://doi.org/10.1016/j.autcon.2019.102906
Barbalho, T.J., Laredo, J.L.J. & Santos, A.C. (2025). The resource-constrained project scheduling problem for risk reduction after industrial disasters involving dangerous substances. OR Spectrum. https://doi.org/10.1007/s00291-025-00822-1
Brucker, P., Knust, S., Schoo, A., & Thiele, O. (1998). A branch and bound algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 107(2), 272–288.https://doi.org/10.1016/S0377-2217(97)00335-4
Chakrabortty, R. K., Sarker, R. A., & Essam, D. L. (2016). Multi-mode resource constrained project scheduling under resource disruptions. Computers & Chemical Engineering, 88, 13–29. https://doi.org/10.1016/j.compchemeng.2016.01.004
Challa, S., & Koks, D. (2004). Bayesian and Dempster-Shafer fusion. Sadhana, 29, 145–174. https://doi.org/10.1007/BF02703729
Chand, S., Singh, H., & Ray, T. (2019). Evolving heuristics for the resource constrained project scheduling problem with dynamic resource disruptions. Swarm and evolutionary computation, 44, 897–912. https://doi.org/10.1016/j.swevo.2018.09.007
Chao, Y., Zhuang, C., Guo, H., & Liu, J. (2026). A genetic programming hyper-heuristic with whale optimization algorithm for the dynamic resource-constrained multi-project scheduling problems. Expert Systems with Applications, 295, 128881. https://doi.org/10.1016/j.eswa.2025.128881
Chen, S. M., Chen, P. H., & Chang, L. M. (2013). A framework for an automated and integrated project scheduling and management system. Automation in Construction, 35, 89–110. https://doi.org/10.1016/j.autcon.2013.04.002
Cheng, J. R., & Gen, M. (2019). Accelerating genetic algorithms with GPU computing: A selective overview. Computers & Industrial Engineering, 128, 514–525. https://doi.org/10.1016/j.cie.2018.12.067
Choi, H., Katake, A., Choi, S., Kang, Y., & Choe, Y. (2009). Probabilistic combination of multiple evidence. In Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part I 16 (pp. 302–311). Springer Berlin Heidelberg.. https://doi.org/10.1007/978-3-642-10677-4_34
Chou, J. S., & Yang, J. G. (2012). Project management knowledge and effects on construction project outcomes: An empirical study. Project Management Journal, 43(5), 47–67. https://doi.org/10.1002/pmj.21293
Colin, J., & Vanhoucke, M. (2015). A comparison of the performance of various project control methods using earned value management systems. Expert Systems with Applications, 42, 3159–3175. https://doi.org/10.1016/j.eswa.2014.12.007
Davis, K. R., Stam, A., & Grzybowski, R. A. (1992). Resource constrained project scheduling with multiple objectives: A decision support approach. Computers & operations research, 19(7), 657–669. https://doi.org/10.1016/0305-0548(92)90035-4
Demeulemeester, E., & Herroelen, W. (2010). Robust project scheduling. Foundations and Trends® in Technology, Information and Operations Management, 3(3–4), 201–376.http://dx.doi.org/10.1561/0200000021
Ding, H., Zhuang, C., & Liu, J. (2023). Extensions of the resource-constrained project scheduling problem. Automation in Construction, 153, 104958. https://doi.org/10.1016/j.autcon.2023.104958
Farahmand-Mehr, M., & Mousavi, S. M. (2025). Resource-constrained multi-project scheduling problems considering time-dependent reliability of resources: a new immune genetic local search algorithm. Kybernetes. https://doi.org/10.1108/K-04-2024-0895
Geibinger, T., Mischek, F. & Musliu, N. (2024). Investigating constraint programming and hybrid methods for real-world industrial test laboratory scheduling. J Sched 27, 607–622 https://doi.org/10.1007/s10951-024-00821-0
Ghoroqi, M., Ghoddousi, P., Makui, A., Shirzadi Javid, A. A., & Talebi, S. (2023). An integrated model for multi-mode resource-constrained multi-project scheduling problems considering supply management with sustainable approach in the construction industry under uncertainty using evidence theory and optimization algorithms. Buildings, 13(8), 2023. https://doi.org/10.3390/buildings13082023
Han, S. H., Yun, S., Kim, H., Kwak, Y. H., Park, H. K., & Lee, S. H. (2009). Analyzing schedule delay of mega project: Lessons learned from Korea train express. IEEE Transactions on Engineering Management, 56(2), 243–256.https://doi.org/10.1109/TEM.2009.2016042
Hazır, Ö. (2015). A review of analytical models, approaches and decision support tools in project monitoring and control. International Journal of Project Management, 33(4), 808–815. https://doi.org/10.1016/j.ijproman.2014.09.005
Hazır, Ö., & Ulusoy, G. (2020). A classification and review of approaches and methods for modeling uncertainty in projects. International Journal of Production Economics, 223, 107522. https://doi.org/10.1016/j.ijpe.2019.107522
He, N., Zhang, D. Z., & Yuce, B. (2022). Integrated multi-project planning and scheduling-a multiagent approach. European Journal of Operational Research, 302(2), 688-699. https://doi.org/10.1016/j.ejor.2022.01.018
Herroelen, W., & Leus, R. (2004). The construction of stable project baseline schedules. European journal of operational research, 156(3), 550–565. https://doi.org/10.1016/S0377-2217(03)00130-9
Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306. https://doi.org/10.1016/j.ejor.2004.04.002
Huynh, V. N. (2009, November). Discounting and combination scheme in evidence theory for dealing with conflict in information fusion. In International Conference on Modeling Decisions for Artificial Intelligence. 217–230. https://doi.org/10.1007/978-3-642-04820-3_20
Jin, S. (2024). Measuring complexity in mega construction projects: fuzzy comprehensive evaluation and grey relational analysis. Engineering, Construction, and Architectural Management. https://doi.org/10.1108/ecam-07-2024-0951
Kannimuthu, M., Ekambaram, P., Raphael, B., & Kuppuswamy, A. (2018). Resource unconstrained and constrained project scheduling problems and practices in a multiproject environment. Advances in Civil Engineering, 1, 9579273. https://doi.org/10.1155/2018/9579273
Ke, H., Wang, L., & Huang, H. (2015). An uncertain model for RCPSP with solution robustness focusing on logistics project schedule. International Journal of e-Navigation and Maritime Economy, 3, 71–83. https://doi.org/10.1016/j.enavi.2015.12.007
Khamooshi, H., & Golafshani, H. (2014). EDM: Earned Duration Management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019–1041. https://doi.org/10.1016/j.ijproman.2013.11.002
Lamas, P., & Demeulemeester, E. (2016). A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations. Journal of Scheduling, 19, 409–428. https://doi.org/10.1007/s10951-015-0423-3
Leus, R., Rostami, S., & Creemers, S. (2015, December). New benchmark results for the stochastic resource-constrained project scheduling problem. In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 204–208. IEEE. https://doi.org/ 10.1109/IEEM.2015.7385637
Li, H., & Demeulemeester, E. (2016). A genetic algorithm for the robust resource leveling problem. Journal of Scheduling, 19, 43–60. https://doi.org/10.1007/s10951-015-0457-6
Li, H., Xu, Z., & Demeulemeester, E. (2015). Scheduling policies for the stochastic resource leveling problem. Journal of Construction Engineering and Management, 141(2), 04014072. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000936
Liu, W., Zhang, J., Vanhoucke, M., & Guo, W. (2025). Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints. Computers & Industrial Engineering, 200, 110843. https://doi.org/10.1016/j.cie.2024.110843
Liu, Z., & Wang, H. (2006). Heuristic algorithm for RCPSP with the objective of minimizing activities' cost. Journal of Systems Engineering and Electronics, 17(1), 96–102. https://doi.org/10.1016/S1004-4132(06)60018-2
Lova, A., & Tormos, P. (2001). Analysis of scheduling schemes and heuristic rules performance in resource-constrained multiproject scheduling. Annals of Operations Research, 102, 263–286. http://dx.doi.org/10.1023/A:1010966401888
Ma, W., Che, Y., Huang, H., & Ke, H. (2016). Resource-constrained project scheduling problem with uncertain durations and renewable resources. International journal of machine learning and cybernetics, 7, 613–621. https://doi.org/10.1007/s13042-015-0444-4
Martens, A., & Vanhoucke, M. (2017). A buffer control method for top-down project control. European Journal of Operational Research, 262(1), 274–286. https://doi.org/10.1016/j.ejor.2017.03.034
Martens, A., & Vanhoucke, M. (2017). The integration of constrained resources into top-down project control. Computers & Industrial Engineering, 110, 277–288. https://doi.org/10.1016/j.cie.2017.05.020
Martin, X. A., Herrero, R., Juan, A. A., & Panadero, J. (2024). An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem. Mathematics, 12(12), 1873. https://doi.org/10.3390/math12121873
Mittal, M. L., & Kanda, A. (2009). Scheduling of multiple projects with resource transfers. International Journal of Mathematics in Operational Research, 1(3), 303–325. http://dx.doi.org/10.1504/IJMOR.2009.024288.
Moradi, M., Hafezalkotob, A., & Ghezavati, V. (2019). Robust resource-constrained project scheduling problem of the project’s subcontractors in a cooperative environment under uncertainty: Social complex construction case study. Computers & Industrial Engineering, 133, 19–28. https://doi.org/10.1016/j.cie.2019.04.046
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264–281. https://doi.org/10.1287/opre.43.2.264
Nassreddine, G., Abdallah, F., & Denoux, T. (2009). State estimation using interval analysis and belief-function theory: application to dynamic vehicle localization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(5), 1205–1218. https://doi.org/ 10.1109/TSMCB.2009.2035707
Nie, X., Li, M., Lu, J., & Wang, B. (2023). Research on Buffer Calculation Model of Critical Chain Based on Adjacency Information Entropy. Buildings, 13(4), 942. https://doi.org/10.3390/buildings13040942
Nonobe, K., Ibaraki, T. (2002). Formulation and Tabu Search Algorithm for the Resource Constrained Project Scheduling Problem. In: Essays and Surveys in Metaheuristics. Operations Research/Computer Science Interfaces Series Springer Boston MA, 15. https://doi.org/10.1007/978-1-4615-1507-4_25
Nudtasomboon, N., & Randhawa, S. U. (1997). Resource-constrained project scheduling with renewable and non-renewable resources and time-resource tradeoffs. Computers & Industrial Engineering, 32(1), 227–242. https://doi.org/10.1016/S0360-8352(96)00212-4
Ock, J. H., & Han, S. H. (2010). Measuring risk-associated activity’s duration: A fuzzy set theory application. KSCE Journal of Civil Engineering, 14(5), 663-671. https://doi.org/10.1007/s12205-010-1003-x
Pritsker, A. A. B., Waiters, L. J., & Wolfe, P. M. (1969). Multiproject scheduling with limited resources: A zero-one programming approach. Management science, 16(1), 93–108. https://doi.org/10.1287/mnsc.16.1.93
Qiu, K., Chen, L., & Dauzère-Pérès, S. (2025). Robust optimization approach for the resource-constrained project scheduling problem with uncertain activity release times. Computers & Operations Research, 107215. https://doi.org/10.1016/j.cor.2025.107215
Rahman, M. H. F., Chakrabortty, R. K., & Ryan, M. J. (2021). Managing uncertainty and disruptions in resource constrained project scheduling problems: A real-time reactive approach. IEEE Access, 9, 45562–45586. https://doi.org/ 10.1109/ACCESS.2021.3063766
Rezaeian, J., Soleimani, F., Mohaselafshary, S., & Arab, A. (2015). Using a meta-heuristic algorithm for solving the multi-mode resource-constrained project scheduling problem. International Journal of Operational Research, 24(1), 1–16. https://doi.org/10.1504/IJOR.2015.070859
RezaHoseini, A., Noori, S., & Ghannadpour, S. F. (2021). Integrated scheduling of suppliers and multi-project activities for green construction supply chains under uncertainty. Automation in Construction, 122, 103485. https://doi.org/10.1016/j.autcon.2020.103485
Rodrigues, S. B., & Yamashita, D. S. (2010). An exact algorithm for minimizing resource availability costs in project scheduling. European Journal of Operational Research, 206(3), 562–568. https://doi.org/10.1016/j.ejor.2010.03.008
Sadeghloo, M., Emami, S., & Divsalar, A. (2024). A Benders decomposition algorithm for the multi-mode resource-constrained multi-project scheduling problem with uncertainty. Annals of Operations Research, 339(3), 1637–1677. https://doi.org/10.1007/s10479-023-05403-5
Salah, A., & Moselhi, O. (2016). Risk identification and assessment for engineering procurement construction management projects using fuzzy set theory. Canadian Journal of Civil Engineering, 43(5), 429–442. https://doi.org/10.1139/cjce-2015-0154
Salama, T., & Moselhi, O. (2019). Multi-objective optimization for repetitive scheduling under uncertainty. Engineering, Construction and Architectural Management, 26(7), 1294–1320. https://doi.org/10.1108/ECAM-05-2018-0217
Salvadori, I., Agnetis, A. (2025). The impact of the number of preemptions in resource-constrained project scheduling problems with time-varying resources: computational experiments and a case study. Ann Oper Res. https://doi.org/10.1007/s10479-025-06892-2
Schutt, A., Chu, G., Stuckey, P. J., & Wallace, M. G. (2012). Maximising the net present value for resource-constrained project scheduling. In International conference on integration of artificial intelligence (AI) and Operations research (OR) techniques in constraint programming , Berlin, Heidelberg: Springer Berlin Heidelberg. 362–378. https://doi.org/10.1007/978-3-642-29828-8_24
Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press. 42. https://doi.org/10.2307/j.ctv10vm1qb
Shrivastava, A., & Pandey, M. (2024). Integrating quality in resource-constrained time-cost trade-off optimization for civil construction projects using NSGA-III technique. Asian Journal of Civil Engineering, 25(6), 4619–4632. https://doi.org/10.1007/s42107-024-01068-y
Soares, L. C. R., & Carvalho, M. A. M. (2020). Biased random-key genetic algorithm for scheduling identical parallel machines with tooling constraints. European Journal of Operational Research, 285(3), 955–964.https://doi.org/10.1016/j.ejor.2020.02.047
Song, J., Martens, A., & Vanhoucke, M. (2021). Using schedule risk analysis with resource constraints for project control. European Journal of Operational Research, 288, 736–752. https://doi.org/10.1016/j.ejor.2020.06.015
Song, J., Martens, A., & Vanhoucke, M. (2022). Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control. European Journal of Operational Research, 297(2), 451–466. https://doi.org/10.1016/j.ejor.2021.05.036
Tirkolaee, E. B., Goli, A., Hematian, M., Sangaiah, A. K., & Han, T. (2019). Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms. Computing, 101, 547-570. https://doi.org/10.1007/s00607-018-00693-1
Van de Vonder, S., Demeulemeester, E., Leus, R., & Herroelen, W. (2006). Proactive-reactive project scheduling trade-offs and procedures. Perspectives in modern project scheduling, 25–51. https://doi.org/10.1007/978-0-387-33768-5_2
Van Eynde, R., & Vanhoucke, M. (2020). Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling. Journal of Scheduling, 23, 301–325. http://dx.doi.org/10.1007/s10951-020-00651-w
Van Eynde, R., & Vanhoucke, M. (2022). New summary measures and datasets for the multi-project scheduling problem. European Journal of Operational Research, 299(3),853–868. http://dx.doi.org/10.1016/j.ejor.2021.10.006
Vandenheede, L., Vanhoucke, M., & Maenhout, B. (2016). A scatter search for the extended resource renting problem. International Journal of Production Research, 54(16), 4723-4743. https://doi.org/10.1080/00207543.2015.1064177
Vanhoucke, M. (2011). On the dynamic use of project performance and schedule risk information during project tracking. Omega The International Journal of Management Science, 39, 416–42. https://doi.org/10.1016/j.omega.2010.09.006
Vanhoucke, M. (2012). Measuring the efficiency of project control using fictitious and empirical project data. International journal of project management, 30(2), 252–263. https://doi.org/10.1016/j.ijproman.2011.05.006
Vanhoucke, M. , & Batselier, J. (2019). A statistical method for estimating activity uncertainty parameters to improve project forecasting. Entropy, 21(10), 952. https://doi.org/10.3390/e21100952
Villafáñez, F., Poza, D., López-Paredes, A., Pajares, J., & Olmo, R. D. (2019). A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP). Soft Computing, 23(10), 3465–3479. https://doi.org/10.1007/s00500-017-3003-y
Wang, H., Wang, W., Sun, H., Cui, Z., Rahnamayan, S., & Zeng, S. (2017). A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 21, 4297–4307 .https://doi.org/1 0.1007/s00500-016-2062-9
Wang, L., Huang, H., & Ke, H. (2015). Chance-constrained model for RCPSP with uncertain durations. Journal of uncertainty analysis and applications, 3, 1–10. https://doi.org/10.1186/s40467-015-0034-8
Wang, Y., Xu, D., Zhou, L., & Li, Z. (2025). Standard Revision Project Scheduling Problem Considering Coordination Degree of Standards Systems. Systems, 13(8), 685. https://doi.org/10.3390/systems13080685
Wen, M., Lin, J., Qian, Y., & Huang, W. (2021). Scheduling interrelated activities in complex projects under high-order rework: A DSM-based approach. Computers & Operations Research, 130, 105246. https://doi.org/10.1016/j.cor.2021.105246
Wiesemann, W., Kuhn, D., & Rustem, B. (2010). Maximizing the net present value of a project under uncertainty. European Journal of Operational Research, 202(2), 356–367. https://doi.org/10.1016/j.ejor.2009.05.045
Yager, R. R. (1986). Arithmetic and other operations on Dempster-Shafer structures. International Journal of Man-Machine Studies, 25(4), 357–366. https://doi.org/10.1016/S0020-7373(86)80066-9
Zaman, F., Elsayed, S. M., Saker, R., & Essam, D. (2020). Resource constrained project scheduling with dynamic disruption recovery. IEEE Access, 8, 144866–144879.https://doi.org/ 10.1109/ACCESS.2020.3014940
Zhang, L., Zhou, L., Yao, Z., & Li, Y. (2024). Reactive scheduling for repetitive projects based on integrated crew interruption and soft logic strategies. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2024.3394852
Zhang, Y., Chang, R., Omrany, H., Zuo, J., Burry, J., & Gu, N. (2025). Policy-gradient scheduling optimisation under multi-skill constraints: A comparative study on computational algorithms. Journal of Building Design and Environment, 3(3), 202571–202571. http://dx.doi.org/10.70401/jbde.2025.0017
Zhang, Y., Li, X., Teng, Y., Bai, S., & Chen, Z. (2024). Two-list genetic algorithm for optimizing work package schemes to minimize project costs. Automation in Construction, 165, 105595. https://doi.org/10.1016/j.autcon.2024.105595