Blockchain of optimal multiple construction projects planning under probabilistic arrival and stochastic durations
Abstract
With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task’s start times. The project arrival date and the task’s execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task’s execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task’s execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task’s execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task’s execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning.
Keyword : blockchain, sequencing, scheduling, optimization, project management
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alladi, T., Chamola, V., Parizi, R. M., & Choo, K. K. R. (2019). Blockchain applications for industry 4.0 and industrial IoT: A review. IEEE Access, 7, 176935–176951. https://doi.org/10.1109/ACCESS.2019.2956748
Almeida, B. F., Correia, I, & Saldanha-da-Gama, F. (2016). Priority-based heuristics for the multi-skill resource constrained project scheduling problem. Expert Systems with Applications, 57, 91–103. https://doi.org/10.1016/j.eswa.2016.03.017
Al-Refaie, A., Qapaja, A., & Al-Hawadi, A. (2021). Optimal fuzzy scheduling and sequencing of work-intensive multiple projects under normal and unexpected events. International Journal of Information Technology Project Management, 12(3), 64–89. https://doi.org/10.4018/IJITPM.2021070105
Ansari, R., Khalilzadeh, M., & Hosseini, M. R. (2022). A multi-objective dynamic optimization approach to project schedule management: A case study of a gas field construction. KSCE Journal of Civil Engineering, 26(3), 1005–1013. https://doi.org/10.1007/s12205-021-0410-5
Ballestin, F., & Leus, R. (2009). Resource‐constrained project scheduling for timely project completion with stochastic activity durations. Production and Operations Management, 18(4), 459–474. https://doi.org/10.1111/j.1937-5956.2009.01023.x
Bruni, M. E., Beraldi, P., & Guerriero, F. (2015). The stochastic resource-constrained project scheduling problem. In C. Schwindt, & J. Zimmermann (Eds.), Handbook on project management and scheduling: Vol. 2. International handbooks on information systems (pp. 811–835). Springer. https://doi.org/10.1007/978-3-319-05915-0_7
Christodoulou, P., Christodoulou, K., & Andreou, A. (2018). A decentralised application for logistics: Using blockchain in real-world applications. The Cyprus Review, 30(2), 181–193.
Creemers, S. (2015). Minimizing the expected makespan of a project with stochastic activity durations under resource constraints. Journal of Scheduling, 18(3), 263–273. https://doi.org/10.1007/s10951-015-0421-5
de Melo, L. V., & de Queiroz, T. A. (2021). Integer linear programming formulations for the RCPSP considering multi-skill, multi-mode, and minimum and maximum time lags. IEEE Latin America Transactions, 19(1), 5–16. https://doi.org/10.1109/TLA.2021.9423821
Deblaere, F., Demeulemeester, E., & Herroelen, W. (2011). Proactive policies for the stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 214(2), 308–316. https://doi.org/10.1016/j.ejor.2011.04.019
Delgoshaei, A., Rabczuk, T., Ali, A., & Ariffin, M. K. A. (2017). An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources. Annals of Operations Research, 259(1), 85–117. https://doi.org/10.1007/s10479-016-2336-8
Delgoshaei, A., Aram, A., Mantegh, V., Hanjani, S., Nasiri, A. H., & Shirmohamadi, F. (2019). A multi-objectives weighting genetic algorithm for scheduling resource-constraint project problem in the presence of resource uncertainty. International Journal of Supply and Operations Management, 6(3), 213–230. https://doi.org/10.22034/2019.3.3
Guo, Y., & Liang, C. (2016). Blockchain application and outlook in the banking industry. Financial Innovation, 2(1), 24. https://doi.org/10.1186/s40854-016-0034-9
Hong, Y., Choi, B., & Kim, Y. (2019). Two-stage stochastic programming based on particle swarm optimization for aircraft sequencing and scheduling. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1365–1377. https://doi.org/10.1109/TITS.2018.2850000
Huang, S., Li, G., Ben-Awuah, E., Afum, B. O., & Hu, N. (2020). A stochastic mixed integer programming framework for underground mining production scheduling optimization considering grade uncertainty. IEEE Access, 8, 24495–24505. https://doi.org/10.1109/ACCESS.2020.2970480
Issaoui, Y., Khiat, A., Bahnasse, A., & Ouajji, H. (2019). Smart logistics: Study of the application of blockchain technology. Procedia Computer Science, 160, 266–271. https://doi.org/10.1016/j.procs.2019.09.467
Kadri, R. L., & Boctor, F. 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, 265(2), 454–462. https://doi.org/10.1016/j.ejor.2017.07.027
Kawaguchi, N. (2019). Application of blockchain to supply chain: Flexible blockchain technology. Procedia Computer Science, 164, 143–148. https://doi.org/10.1016/j.procs.2019.12.166
Lee, J., Azamfar, M., & Singh, J. (2019). A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems. Manufacturing Letters, 20, 34–39. https://doi.org/10.1016/j.mfglet.2019.05.003
Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021). Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Automation in Construction, 127, 103688. https://doi.org/10.1016/j.autcon.2021.103688
Lohmer, J., & Lasch, R. (2020). Blockchain in operations management and manufacturing: Potential and barriers. Computers & Industrial Engineering, 149, 106789. https://doi.org/10.1016/j.cie.2020.106789
Marinho, A., Couto, J. P., & Teixeira, J. M. C. (2021). Relational contracting and its combination with the BIM methodology in mitigating asymmetric information problems in construction projects. Journal of Civil Engineering and Management, 27(4), 217–229. https://doi.org/10.3846/jcem.2021.14742
Muzylyov, D., & Shramenko, N. (2019). Blockchain technology in transportation as a part of the efficiency in Industry 4.0 strategy. In Lecture notes in mechanical engineering. Advanced manufacturing processes. InterPartner 2019 (pp. 216–225). Springer. https://doi.org/10.1007/978-3-030-40724-7_22
Ning, M., He, Z., Jia, T., & Wang, N. (2017). Metaheuristics for multi-mode cash flow balanced project scheduling with stochastic duration of activities. Automation in Construction, 81, 224–233. https://doi.org/10.1016/j.autcon.2017.06.011
Olawumi, T. O., Chan, D. W., Ojo, S., & Yam, M. C. (2021). Automating the modular construction process: A review of digital technologies and future directions with blockchain technology. Journal of Building Engineering, 46, 103720. https://doi.org/10.1016/j.jobe.2021.103720
Ortiz-Pimiento, N., & Diaz-Serna, F. (2018). The project scheduling problem with non-deterministic activities duration: A literature review. Journal of Industrial Engineering and Management, 11(1), 116–134. https://doi.org/10.3926/jiem.2492
Pham, A., Bui, A., Nguyen, T., Nguyen, T., Pashchenko, F., & Pashchenko, F. (2021). Optimization of model parameters by complex probabilistic criteria. In 2021 International Siberian Conference on Control and Communications (SIBCON), Kazan, Russia. IEEE. https://doi.org/10.1109/SIBCON50419.2021.9438856
Project Management Institute. (2021). Beyond agility. PMI’s pulse of the profession report. https://www.pmi.org/learning/library/beyond-agility-gymnastic-enterprises-12973
Quoc, H., The, L., Doan, C., & Thanh, T. (2019). Solving resource constrained project scheduling problem by a discrete version of Cuckoo search algorithm. In NAFOSTED Conference on Information and Computer Science (pp. 73–76), Hanoi, Vietnam. IEEE. https://doi.org/10.1109/NICS48868.2019.9023867
Shu, X., Su, Q., Wang, Q., & Wang, Q. (2018). Optimization of resource-constrained multi-project scheduling problem based on the genetic algorithm. In 2018 15th International Conference on Service Systems and Service Management (ICSSSM), Hangzhou, China. IEEE. https://doi.org/10.1109/ICSSSM.2018.8465086
Singh, M. (2020). Blockchain technology for data management in Industry 4.0. In R. Rosa Righi, A. Alberti, & M. Singh (Eds.), Blockchain technology for industry 4.0 (pp. 59–72). Springer, Singapore. https://doi.org/10.1007/978-981-15-1137-0_3
Swan, M. (2015). Blockchain blueprint for a new economy. O’Reilly Media Inc.
Su, Z., Wang, H., Wang, H., & Shi, X. (2020). A financial data security sharing solution based on blockchain technology and proxy re-encryption technology. In 2020 IEEE 3rd International Conference of Safe Production and Informatization (pp. 462–465), Chongqing City, China. https://doi.org/10.1109/IICSPI51290.2020.9332363
Tanwar, S., Parekh, K., & Evans, R. (2020). Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications, 50, 102407. https://doi.org/10.1016/j.jisa.2019.102407
Tian, Y., Xiong, T., Liu, Z., Mei, Y., & Wan, L. (2022). Multi-objective multi-skill resource-constrained project scheduling problem with skill switches: Model and evolutionary approaches. Computers & Industrial Engineering, 167, 107897. https://doi.org/10.1016/j.cie.2021.107897
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(6), 547–570. https://doi.org/10.1007/s00607-018-00693-1
Ullah, F., & Al-Turjman, F. (2021). A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-05800-6
Ulusoy, G., & Hazır, Ö. (2021). Stochastic project scheduling with no resource constraints. In An introduction to project modeling and planning. Springer texts in business and economics (pp. 167–198). Springer, Cham. https://doi.org/10.1007/978-3-030-61423-2_6
Zhao, G., Liu, S., Lopez, C., Lu, H., Elgueta, S., Chen, H., & Boshkoska, B. M. (2019). Blockchain technology in agri-food value chain management: A synthesis of applications, challenges, and future research directions. Computers in Industry, 109, 83–99. https://doi.org/10.1016/j.compind.2019.04.002
Zhong, P., Zhong, Q., Mi, H., Zhang, S., & Xiang, Y. (2019). Privacy-protected blockchain system. In 2019 20th IEEE International Conference on Mobile Data Management (pp. 457–461), Hong Kong, China. IEEE. https://doi.org/10.1109/MDM.2019.000-2