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Milat, M. Resilient Scheduling and Construction Projects. Encyclopedia. Available online: (accessed on 05 December 2023).
Milat M. Resilient Scheduling and Construction Projects. Encyclopedia. Available at: Accessed December 05, 2023.
Milat, Martina. "Resilient Scheduling and Construction Projects" Encyclopedia, (accessed December 05, 2023).
Milat, M.(2021, July 26). Resilient Scheduling and Construction Projects. In Encyclopedia.
Milat, Martina. "Resilient Scheduling and Construction Projects." Encyclopedia. Web. 26 July, 2021.
Resilient Scheduling and Construction Projects

Resilient schedules are defined by their multidimensionality: they tend to be robust, flexible, and adaptable. Previous studies in construction scheduling have predominantly focused on the robustness aspect, which is already a complex concept. On the one hand, robustness depicts the insensitivity of objective function in the optimization model, may it be project minimization or NPV maximization, for example; on the other hand, it tends to minimize the deviations between baseline schedule and realized state.

resilience baseline schedule uncertainty taxonomy construction project

1. Overview

Complex construction projects are developed in a dynamic environment, where uncertainty conditions have a great potential to affect project deliverables. In an attempt to efficiently deal with the negative impacts of uncertainty, resilient baseline schedules are produced to improve the probability of reaching project goals, such as respecting the due date and reaching the expected profit. Prior to introducing the resilient scheduling procedure, a taxonomy model was built to account for uncertainty sources in construction projects. Thence, a multi-objective optimization model is presented to manage the impact of uncertainty. This approach can be described as a complex trade-off analysis between three important features of a construction project: duration, stability, and profit. The result of the suggested procedure is presented in a form of a resilient baseline schedule, so the ability of a schedule to absorb uncertain perturbations is improved. The proposed optimization problem is illustrated on the example project network, along which the probabilistic simulation method was used to validate the results of the scheduling process in uncertain conditions. The proposed resilient scheduling approach leads to more accurate forecasting, so the project planning calculations are accepted with increased confidence levels. 

2. Resilient Schedules

Large construction projects are characterized by their complexity in terms of organization, as they consist of hundreds of activities and require numerous resources. To successfully manage important project objectives, scheduling efforts must be applied to ensure that a project is completed within the contract requirements [1]. Schedule management concentrates on the processes that are essential to appropriately deliver critical project aspects, such as time, cost, resources, etc. [2] Scheduling methods which are used to establish reliable construction plans can be broadly classified into exact [3][4][5][6], heuristic [7][8][9], or metaheuristic approaches [10][11][12][13][14]. Current practices in the domain of construction planning and scheduling are oriented towards automatic schedule development and the application of specialized optimization techniques [15]. Although modern technologies such as BIM have already been applied to the optimization problems in the realm of construction scheduling [16][17][18][19][20][21], additional development and possible extensions are still needed to effectively automatize scheduling practices and improve both customizability and user-friendliness of emerging technologies for practical use [22][23].
As construction projects take place in a dynamic environment, they are consequently prone to the negative impacts of internal and external sources of uncertainty. Due to the fact that uncertainty has been recognized as a cause of risks that can influence the final outcome of a project [24], there is an indisputable need to manage the uncertainty which is present in almost all of the construction activities [25]. However, there is still a lack of mechanisms that would leverage state-of-the-art technologies such as BIM with the uncertainty management frameworks in complex construction projects [26]. BIM-based uncertainty management frameworks are currently emerging at the theoretical level, where the automatization and practical integration of existing frameworks with new technologies remain the main challenges thus far [27].
To diminish unfavorable impacts stemming from the construction project environment, a relevant strategy is to prepare for uncertainty from the early stages of the project life cycle. To account for uncertainty as early as in the project planning phase, a resilient scheduling approach has been developed recently [28][29][30]. Resilient scheduling is a procedure to develop the optimal baseline makespan for a project, considering the trade-off between schedule stability or robustness, and other important objectives, such as makespan minimization [29]. Moreover, additional trade-offs can be considered when defining the equilibrium state of the project, such as the limited budget of the project, expected net present value, as well as adequate risk management, among other equilibrium facets [28].
Resilient schedules are defined by their multidimensionality: they tend to be robust, flexible, and adaptable. Previous studies in construction scheduling have predominantly focused on the robustness aspect, which is already a complex concept. On the one hand, robustness depicts the insensitivity of objective function in the optimization model, may it be project minimization or NPV maximization, for example; on the other hand, it tends to minimize the deviations between baseline schedule and realized state. For instance, Zhao et al. [31] propose the framework for integrated robustness evaluation, considering composite robustness measure. Authors have been using improved subjective and objective weights in order to evaluate the schedule robustness. In another research, Zhao et al. [32] investigated the importance of schedule robustness by use of metaheuristic optimization techniques, considering both activities’ starting-time deviation, as well as a structural deviation in a schedule.
On the other hand, information about the uncertainty in a construction project is usually scattered, as it arises from various sources, and due to the dynamics of complex construction projects, it is extremely demanding to organize, collect, and reuse that knowledge. Lack of information or ambiguous data can have undesirable consequences on project success and cause a negative impact to project objectives. According to Reference [33], significant efforts have been undertaken to consider more general sources of uncertainty in the project management domain. Because of the inherent complexity in large construction projects, there is a need to manage a considerable amount of information [34].
In the realm of construction management, different researchers have attempted to integrate knowledge about uncertainty into formal conceptualization. This way, domain knowledge can be accessed and reused by users in a form of computer-readable data [35]. For example, Tah and Carr [36] proposed a knowledge-based approach to facilitate effective risk management procedures in a construction project. Ping Tserng et al. [37] developed an ontology-based risk management framework to improve the overall effectiveness of risk management practices for a construction contractor. The study of Ding et al. [38] coupled ontology and semantic web technology in a BIM environment to manage construction risk knowledge. Apart from enhancing general risk management procedures, other practical applications of ontology in the construction domain include knowledge sharing [39][40][41], information extraction [42][43][44], and performance analysis [45][46][47][48].
So far, however, there has been little discussion about modeling a comprehensive knowledge base by considering general sources of uncertainty in construction projects. Therefore, the first aim of this research was to structure uncertainty sources related to complex construction projects in a faceted taxonomy, as a basis for the analysis and uncertainty management in construction projects from the early planning stages. Comprehensive identification and characterization of uncertainties in the construction domain is a first step towards increasing the probability of reaching project goals during the execution phase.
Considering the nature of complex construction projects, which are financially extremely demanding undertakings, appropriate cash procurement is of vital importance. The major source of financing for construction projects is the establishment of the bank overdraft [49][50]. If the cash deficit occurs during the project realization period, contractors will encounter difficulties related to the implementation of the project activities in accordance with a baseline plan [51]. Therefore, the development of a schedule where the cash flows will be suitable for the established bank overdraft is an important subject, since large construction projects require extensive investments and rarely depend solely on the savings of the contractor [49][50].

3. Conclusions

Validation results suggest that resilience of a baseline solution improves with SM value maximization. However, the scope of the research was limited to a small project network, so caution must be applied when examining larger problem instances, since the findings might not be unconditionally transferable to a construction projects based on the more complex precedence networks. Although the research has laid the theoretical foundations for resilient scheduling procedure, the study has certain limitations. For example, the resilience framework should be tested on a larger set of project data collected from real construction projects to analyze the systems’ behavior. Moreover, further research might explore different surrogate measures to interpret the resilience capacity of the baseline schedules for various types of construction projects. Finally, the development of new metaheuristic algorithms will enable detailed analysis and validation of the proposed resilience framework on a larger set of problems.
The present research introduces the financing aspect into the process of resilient scheduling, so the comprehensiveness and feasibility of the initial schedule are significantly improved. The advantage of the proposed resilient project planning is enhanced stability of the baseline schedule in comparison with the simulated state of project execution. This leads to more accurate forecasting, so the project-planning calculations are accepted with higher confidence levels.


  1. Derbe, G.; Li, Y.; Wu, D.; Zhao, Q. Scientometric review of construction project schedule studies: Trends, gaps and potential research areas. J. Civ. Eng. Manag. 2020, 26, 343–363.
  2. Faghihi, V.; Nejat, A.; Reinschmidt, K.F.; Kang, J.H. Automation in construction scheduling: A review of the literature. Int. J. Adv. Manuf. Technol. 2015, 81, 1845–1856.
  3. Cajzek, R.; Klanšek, U. Cost optimization of project schedules under constrained resources and alternative production processes by mixed-integer nonlinear programming. Eng. Constr. Archit. Manag. 2019, 26, 2474–2508.
  4. García-Nieves, J.; Ponz-Tienda, J.; Ospina-Alvarado, A.; Bonilla-Palacios, M. Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects. Autom. Constr. 2019, 105, 102799.
  5. Zou, X.; Fang, S.; Huang, Y.; Zhang, L. Mixed-Integer Linear Programming Approach for Scheduling Repetitive Projects with Time-Cost Trade-Off Consideration. J. Comput. Civ. Eng. 2017, 31, 06016003.
  6. Klanšek, U. Mixed-Integer Nonlinear Programming Model for Nonlinear Discrete Optimization of Project Schedules under Restricted Costs. J. Constr. Eng. Manag. 2016, 142, 04015088.
  7. Liu, Z.; Zhang, Y.; Yu, M.; Zhou, X. Heuristic algorithm for ready-mixed concrete plant scheduling with multiple mixers. Autom. Constr. 2017, 84, 1–13.
  8. Sonmez, R.; Iranagh, M.; Uysal, F. Critical Sequence Crashing Heuristic for Resource-Constrained Discrete Time–Cost Trade-Off Problem. J. Constr. Eng. Manag. 2016, 142, 04015090.
  9. Li, H.; Xu, Z.; Demeulemeester, E. Scheduling Policies for the Stochastic Resource Leveling Problem. J. Constr. Eng. Manag. 2015, 141, 04014072.
  10. Tran, D.; Chou, J.; Luong, D. Multi-objective symbiotic organisms optimization for making time-cost tradeoffs in repetitive project scheduling problem. J. Civ. Eng. Manag. 2019, 25, 322–339.
  11. Agdas, D.; Warne, D.; Osio-Norgaard, J.; Masters, F. Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems. J. Comput. Civ. Eng. 2018, 32, 04017072.
  12. Aminbakhsh, S.; Sonmez, R. Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem. J. Comput. Civ. Eng. 2017, 31, 04016040.
  13. Sroka, B.; Rosłon, J.; Podolski, M.; Bożejko, W.; Burduk, A.; Wodecki, M. Profit optimization for multi-mode repetitive construction project with cash flows using metaheuristics. Arch. Civ. Mech. Eng. 2021, 21, 1–17.
  14. Tao, S.; Wu, C.; Hu, S.; Xu, F. Construction project scheduling under workspace interference. Comput.-Aided Civ. Infrastruct. Eng. 2020, 35, 923–946.
  15. Amer, F.; Koh, H.; Golparvar-Fard, M. Automated Methods and Systems for Construction Planning and Scheduling: Critical Review of Three Decades of Research. J. Constr. Eng. Manag. 2021, 147, 03121002.
  16. ElMenshawy, M.; Marzouk, M. Automated BIM schedule generation approach for solving time–cost trade-off problems. Eng. Constr. Archit. Manag. 2021. Epub ahead of printing.
  17. Wang, Z.; Azar, E.R. BIM-based draft schedule generation in reinforced concrete-framed buildings. Constr. Innov. 2019, 19, 280–294.
  18. Abbasi, S.; Taghizade, K.; Noorzai, E. BIM-Based Combination of Takt Time and Discrete Event Simulation for Implementing Just in Time in Construction Scheduling under Constraints. J. Constr. Eng. Manag. 2020, 146, 04020143.
  19. Dasović, B.; Galić, M.; Klanšek, U. A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling. Sustainability 2020, 12, 3405.
  20. Nusen, P.; Boonyung, W.; Nusen, S.; Panuwatwanich, K.; Champrasert, P.; Kaewmoracharoen, M. Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm. Appl. Sci. 2021, 11, 4716.
  21. Xie, L.; Chen, Y.; Chang, R. Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm. Appl. Sci. 2021, 11, 5531.
  22. Wang, H.; Lin, J.; Zhang, J. Work package-based information modeling for resource-constrained scheduling of construction projects. Autom. Constr. 2020, 109, 102958.
  23. Sbiti, M.; Beddiar, K.; Beladjine, D.; Perrault, R.; Mazari, B. Toward BIM and LPS Data Integration for Lean Site Project Management: A State-of-the-Art Review and Recommendations. Buildings 2021, 11, 196.
  24. Perminova, O.; Gustafsson, M.; Wikström, K. Defining uncertainty in projects—A new perspective. Int. J. Proj. Manag. 2008, 26, 73–79.
  25. Project Management Institute. Construction Extension to the PMBOK Guide; Project Management Institute, Inc.: Newtown Square, PA, USA, 2016; pp. 29–30.
  26. Ahmad, Z.; Thaheem, M.; Maqsoom, A. Building information modeling as a risk transformer: An evolutionary insight into the project uncertainty. Autom. Constr. 2018, 92, 103–119.
  27. Badran, D.; AlZubaidi, R.; Venkatachalam, S. BIM based risk management for design bid build (DBB) design process in the United Arab Emirates: A conceptual framework. Int. J. Syst. Assur. Eng. Manag. 2020, 11, 1339–1361.
  28. Xiong, J.; Chen, Y.; Zhou, Z. Resilience analysis for project scheduling with renewable resource constraint and uncertain activity durations. J. Ind. Manag. Optim. 2016, 12, 719–737.
  29. Yeganeh, F.T.; Zegordi, S.H. A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration. Ann. Oper. Res. 2020, 285, 161–196.
  30. Milat, M.; Knezic, S.; Sedlar, J. A new surrogate measure for resilient approach to construction scheduling. Proc. Comp. Sci. 2021, 181, 468–476.
  31. Zhao, M.; Wang, X.; Yu, J.; Xue, L.; Yang, S. A construction schedule robustness measure based on improved prospect theory and the Copula-CRITIC method. Appl. Sci. 2020, 10, 2013.
  32. Zhao, M.; Wang, X.; Yu, J.; Bi, L.; Xiao, Y.; Zhang, J. Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm. Energies 2020, 13, 2015.
  33. Chapman, C.; Ward, S. Project Risk Management: Processes, Techniques and Insights, 2nd ed.; John Wiley & Sons Ltd.: Chichester, UK, 2003; pp. 1–15.
  34. Zhang, J.; El-Diraby, T.E. Social semantic approach to support communication in AEC. Int. J. Proj. Manag. 2012, 26, 90–104.
  35. Elghamrawy, T.; Boukamp, F.; Kim, H.S. Ontology-based, semi-automatic framework for storing and retrieving on-site construction problem information—An RFID-based case study. In Proceedings of the Construction Research Congress 2009: Building a Sustainable Future, Seattle, WA, USA, 5–7 April 2009.
  36. Tah, J.H.M.; Carr, V. Knowledge-based approach to construction project risk management. J. Comput. Civ. Eng. 2001, 15, 170–177.
  37. Tserng, H.P.; Yin, Y.L.S.; Dzeng, R.J.; Wou, B.; Tsai, M.D.; Chen, W.Y. A study of ontology-based risk management framework of construction projects through project life cycle. Autom. Constr. 2009, 18, 994–1008.
  38. Ding, L.Y.; Zhong, B.T.; Wu, S.; Luo, H.B. Construction risk knowledge management in BIM using ontology and semantic web technology. Saf. Sci. 2016, 87, 202–213.
  39. El-Diraby, T.A.; Lima, C.; Feis, B. Domain taxonomy for construction concepts: Toward a formal ontology for construction knowledge. J. Comput. Civ. Eng. 2005, 19, 394–406.
  40. Costa, R.; Lima, C.; Sarraipa, J. Facilitating knowledge sharing and reuse in building and construction domain: An ontology-based approach. J. Intell. Manuf. 2016, 27, 263–282.
  41. Niu, J.; Issa, R.R.A. Developing taxonomy for the domain ontology of construction contractual semantics: A case study on the AIA A201 document. Adv. Eng. Inform. 2015, 29, 472–482.
  42. Fidan, G.; Dikmen, I.; Tanyer, M.A.; Birgonul, T.M. Ontology for relating risk and vulnerability to cost overrun in international projects. J. Comput. Civ. Eng. 2011, 25, 302–315.
  43. Zhang, L.; Issa, R.R.A. Ontology-based partial building information model extraction. J. Comput. Civ. Eng. 2013, 27, 576–584.
  44. Baudrit, C.; Taillandier, F.; Tran, T.T.P.; Breysse, D. Uncertainty processing and risk monitoring in construction projects using hierarchical probabilistic relational models. Comp. Aid. Civ. Inf. Eng. 2019, 34, 97–115.
  45. Jiang, S.; Wang, N.; Wu, J. Combining BIM and ontology to facilitate intelligent green building evaluation. J. Comput. Civ. Eng. 2018, 32.
  46. Xing, X.; Zhong, B.; Luo, H.; Lic, H.; Wu, H. Ontology for safety risk identification in metro construction. Comp. Ind. 2019, 109, 14–30.
  47. Zhong, B.; Li, H.; Luo, H.; Zhou, J.; Fang, W.; Xing, X. Ontology-based semantic modeling of knowledge in construction: Classification and identification of hazards implied in images. J. Constr. Eng. Manag. 2020, 146, 04020013.
  48. Zhong, B.; Gan, C.; Luo, H.; Xing, X. Ontology-based framework for building environmental monitoring and compliance checking under BIM environment. Build. Environ. 2018, 141, 127–142.
  49. Elazouni, A.M.; Metwally, F.G. Finance-Based Scheduling: Tool to Maximize Project Profit Using Improved Genetic Algorithms. J. Constr. Eng. Manag. 2005, 131, 400–412.
  50. Fathi, H.; Afshar, A. GA-based multi-objective optimization of finance-based construction project scheduling. KSCE J. Civ. Eng. 2010, 14, 627–638.
  51. El-Abbasy, M.; Elazouni, A.; Zayed, T. Finance-based scheduling multi-objective optimization: Benchmarking of evolutionary algorithms. Autom. Constr. 2020, 120, 103392.
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