Scheduling Optimization Using an Adapted Genetic Algorithm with Due Regard for Random Project Interruptions
Abstract
:1. Introduction
1.1. Review of the Literature
1.2. Purpose, Objective and Summary of the Study Outlined in This Article
2. Materials and Methods
2.1. Scheduling Optimization Problem Formulation
2.1.1. General Provisions
2.1.2. Formulation of Goal Optimization Criteria
2.1.3. The Estimated Cost of Risks, Associated with Failure to Meet Scheduled Deadlines
2.1.4. Evaluation of Reliability of Organizational and Technological Solutions
2.1.5. Modeling the Introduction of Random Interruptions into the Schedule
2.2. The Optimal Schedule Option: The Search Algorithm
- 1.
- The basic (deterministic) topology of (i) a schedule model and (ii) a set of interruption values related to a set of emergence factors , is developed.
- 2.
- During the first iteration, a set of schedules is made randomly by selecting values of interruptions from the sets and assigning them to the work items where they can emerge. The set of best schedule options is not completed yet: .
- 3.
- Timing is computed for each schedule.
- 4.
- Then the iterative process is launched in which the current extreme value of time T0 is found for each schedule option. After that conditions are verified:
- 5.
- If condition (7) is not met, schedule option is replaced by a new one:
- 6.
- Set is edited to save the best solution that meets the optimality criterion. For this purpose, the elitism strategy is used, which can be formulated as follows:
- 7.
- Genetic operators of adjustable multipoint mutation [56] are applied to set .
- 8.
- The computation stopping criterion is verified. The criterion, empirically derived from a solution to a number of optimization problems related to genetic algorithms, is used. Iterations stop after number if there are no changes in set . This number can be identified as follows:
3. Results
3.1. Characteristic Case of Interval Estimation of Construction Time
3.2. Sample Risk Assessment (3) for One Critical Path Work Item
3.3. Evaluation of the Organizational and Technological Reliability of the Schedule
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zalmai, M.L.; Akcay, C.; Manisali, E. Time-cost optimization using harmony search algorithm in construction projects. Optimización del costo del tiempo, utilizando el algoritmo de búsqueda de armonía en proyectos de construcción. Rev. Construc. 2019, 18, 226–237. [Google Scholar] [CrossRef]
- Anysz, H. Managing Delays in Construction Projects Aiming at Cost Overrun Minimization. IOP Conf. Ser. Mater. Sci. Eng. 2019, 603, 032004. [Google Scholar] [CrossRef]
- Monghasemi, S.; Abdallah, M. Linear Optimization Model to Minimize Total Cost of Repetitive Construction Projects and Identify Order of Units. J. Manag. Eng. 2021, 37, 936. [Google Scholar] [CrossRef]
- Kermanshachi, S.; Rouhanizadeh, B. Sensitivity analysis of construction schedule performance due to increased change orders and decreased labor productivity. In Proceedings of the Annual Conference—Canadian Society for Civil Engineering, Montreal, QC, Canada, 11–15 June 2019. [Google Scholar]
- Leoni, L.; BahooToroody, A.; De Carlo, F.; Paltrinieri, N. Developing a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Network. J. Loss Prev. Process Ind. 2019, 57, 17–24. [Google Scholar] [CrossRef]
- Francis, A. Chronographical spatiotemporal scheduling optimization for building projects. Front. Built Env. 2019, 5, 36. [Google Scholar] [CrossRef]
- Lim, A.W.P.; Latief, Y. The development of safety plan using Work Breakdown Structure (WBS) for Building Information Modeling (BIM)-based building structure work. J. Comput. Theor. Nanosci. 2020, 17, 1402–1413. [Google Scholar] [CrossRef]
- Nishigaki, S.; Saibara, K.; Ootsuki, T.; Morikawa, H. Scheduling Simulator by Ensemble Forecasting of Construction Duration. In Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; pp. 441–448. [Google Scholar]
- Tirunagari, H.V.; Kone, V. Simulation of construction sequence using BIM 4D techniques. Int. J. Recent Technol. Eng. 2019, 7, 877–881. [Google Scholar]
- Kim, S.; Peavy, M.; Huang, P.-C.; Kim, K. Development of BIM-integrated construction robot task planning and simulation system. Autom. Constr. 2021, 127, 3720. [Google Scholar] [CrossRef]
- Yan, X.; Zhang, H. Computer vision-based disruption management for prefabricated building construction schedule. J. Comput. Civ. Eng. 2021, 35, 990. [Google Scholar] [CrossRef]
- Chang, H.-Y.; Chiu, C.-K. Uncertainty assessment of field weld connections and the related effects on service life of steel buildings. Struct. Infrastruct. Eng. 2019, 15, 1333–1345. [Google Scholar] [CrossRef]
- Chen, L.; Lu, Q.; Li, S.; He, W.; Yang, J. Bayesian Monte Carlo Simulation-Driven Approach for Construction Schedule Risk Inference. J. Manag. Eng. 2021, 37, 884. [Google Scholar] [CrossRef]
- Fitzsimmonsa, J.; Hong, Y.; Brilakis, I. Improving construction project schedules before execution. In Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; pp. 1144–1151. [Google Scholar]
- Hu, Z.; Luo, J.; Fang, X.; Xiao, K.; Hu, B.; Chen, L. Real-time Schedule Algorithm with Temporal and Spatial Isolation Feature for Mixed Criticality System. In Proceedings of the 7th International Symposium on System and Software Reliability, ISSSR 2021, Chongqing, China, 23–24 September 2021; pp. 99–108. [Google Scholar] [CrossRef]
- Singh, J.; Anumba, C.J. Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniques. J. Inf. Technol. Constr. 2022, 27, 173–190. [Google Scholar] [CrossRef]
- Turk, A.; Wu, Q.; Zhang, M. Model predictive control based real-time scheduling for balancing multiple uncertainties in integrated energy system with power-to-x. Int. J. Electr. Power Energy Syst. 2021, 130, 7015. [Google Scholar] [CrossRef]
- Sobieraj, J.; Metelski, D. Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex. Appl. Sci. 2022, 12, 1044. [Google Scholar] [CrossRef]
- Abdallah, M.; Alshahri, A. Optimizing planning of build–operate–transfer projects to maximize investor profit. Can. J. Civ. Eng. 2019, 46, 26–37. [Google Scholar] [CrossRef]
- Liu, D.; Li, H.; Wang, H.; Qi, C.; Rose, T. Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling. Expert Syst. Appl. 2020, 148, 3230. [Google Scholar] [CrossRef]
- Biruk, S.; Jaśkowski, P. Selection of the optimal actions for crashing processes duration to increase the robustness of construction schedules. Appl. Sci. 2020, 10, 8028. [Google Scholar] [CrossRef]
- Liu, D.; Li, X.; Chen, J.; Jin, R. Real-Time Optimization of Precast Concrete Component Transportation and Storage. Adv. Civ. Eng. 2020, 2020, 4910. [Google Scholar] [CrossRef]
- Plebankiewicz, E.; Zima, K.; Wieczorek, D. Modelling of time, cost and risk of construction with using fuzzy logic. J. Civ. Eng. Manag. 2021, 27, 412–426. [Google Scholar] [CrossRef]
- Maremi, A.; Ben-Awuah, E.; Askari-Nasab, H. Multi-objective Mathematical Programming Framework for Integrated Oil Sands Mine Planning and Tailings Disposal Optimization. Min. Metall. Explor. 2021, 38, 1355–1374. [Google Scholar] [CrossRef]
- Atef, S.; Ismail, N.; Eltawil, A.B. A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction. Adv. Build. Energy Res. 2022, 16, 262–280. [Google Scholar] [CrossRef]
- Xue, X.; Ai, X.; Fang, J.; Yao, W.; Wen, J. Real-time schedule of integrated heat and power system: A multi-dimensional stochastic approximate dynamic programming approach. Int. J. Electr. Power Energy Syst. 2022, 134, 7427. [Google Scholar] [CrossRef]
- Isah, M.A.; Kim, B.-S. Integrating schedule risk analysis with multi-skilled resource scheduling to improve resource-constrained project scheduling problems. Appl. Sci. 2021, 11, 650. [Google Scholar] [CrossRef]
- García-Nieves, J.D.; Ponz-Tienda, J.L.; Ospina-Alvarado, A.; Bonilla-Palacios, M. Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects. Autom. Constr. 2019, 105, 20. [Google Scholar] [CrossRef]
- Sami Ur Rehman, M.; Thaheem, M.J.; Nasir, A.R.; Khan, K.I.A. Project schedule risk management through building information modelling. Int. J. Constr. Manag. 2020, 22, 1489–1499. [Google Scholar] [CrossRef]
- Kim, T.; Kim, Y.-W.; Cho, H. Dynamic production scheduling model under due date uncertainty in precast concrete construction. J. Clean. Prod. 2020, 257, 527. [Google Scholar] [CrossRef]
- Taraziya, R.F.; Ali, R.S.A. Survey on the most significant factors affecting the delivery process of construction activities during execution phase in Iraq. International Rev. Civ. Eng. 2020, 11, 114–120. [Google Scholar] [CrossRef]
- Kim, T.; Kim, Y.-W.; Cho, H. A simulation-based dynamic scheduling model for curtain wall production considering construction planning reliability. J. Clean. Prod. 2021, 286, 4922. [Google Scholar] [CrossRef]
- Nolz, P.C. Optimizing construction schedules and material deliveries in city logistics: A case study from the building industry. Flex. Serv. Manuf. J. 2021, 33, 846–878. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Jaśkowski, P.; Biruk, S.; Krzemiński, M. Planning repetitive construction processes to improve robustness of schedules in risk environment. Metoda harmonogramowanie powtarzalnych procesów budowlanych zwiekszajaca odporność harmonogramów w warunkach ryzyka. Arch. Civ. Eng. 2020, 66, 643–657. [Google Scholar] [CrossRef]
- Kubečková, D.; Smugala, S. Statistical methods applied to construction process management. Asian J. Civ. Eng. 2020, 21, 479–494. [Google Scholar] [CrossRef]
- Ibrahim, M.W.; Hanna, A.S.; Russell, J.S.; Abotaleb, I.S.; El-Adaway, I.H. Quantitative Analysis of the Impacts of Out-of-Sequence Work on Project Performance. J. Constr. Eng. Manag. 2020, 146, 1876. [Google Scholar] [CrossRef]
- Jakkula, B.; Govinda Raj, M.; Murthy, C.S.N. Maintenance management of load haul dumper using reliability analysis. J. Qual. Maint. Eng. 2020, 26, 290–310. [Google Scholar] [CrossRef]
- Liu, B.; Pan, Z.; Tan, Z.; Wang, D.; Yu, T. A Real-Time Schedule Optimization of Massive Electric Vehicles and Energy Storage System Based on Grey Wolf Optimizer. In Proceedings of the IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER 2018), Tianjin, China, 19–23 July 2018; pp. 1160–1165. [Google Scholar] [CrossRef]
- Kalugin Yu., B. Universal method for calculation of reliable completion times. Mag. Civ. Eng. 2016, 7, 70–80. [Google Scholar] [CrossRef] [Green Version]
- Petrochenko, M.V.; Velichkin, V.Z.; Kazakov, Y.N.; Zavodnova, Y.B. Reliability assessment of the construction schedule by the critical chain method. Mag. Civ. Eng. 2018, 81, 25–31. [Google Scholar] [CrossRef]
- Alvanchi, A.; Rahimi, M.; Mousavi, M.; Alikhani, H. Construction schedule, an influential factor on air pollution in urban infrastructure projects. J. Clean. Prod. 2020, 255, 222. [Google Scholar] [CrossRef]
- Senthil, J.; Muthukannan, M.; Robin Sham, S.H. Prediction of climate risk management in infrastructure projects. International J. Innov. Technol. Explor. Eng. 2019, 8, 268–272. [Google Scholar] [CrossRef]
- Xiao, B.; Kang, S.-C. Deep learning detection for real-time construction machine checking. In Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff Alberta, AB, Canada, 21–24 May 2019; pp. 1136–1141. [Google Scholar]
- Linlin, X.; Chen, Y.; Chang, R. Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm. Appl. Sci. 2021, 11, 5531. [Google Scholar] [CrossRef]
- Wei, H.; Li, W.; Wang, W. Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation. Sustainability 2021, 13, 7318. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Banihashemi, S.A.; Khalilzadeh, M. Time-Cost-Quality-Risk Trade-off Project Scheduling Problem in Oil and Gas Construction Projects: Fuzzy Logic and Genetic Algorithm. Jordan J. Civ. Eng. 2022, 16, 355–364. [Google Scholar]
- Wang, T.; Abdallah, M.; Clevenger, C.; Monghasemi, S. Time–Cost–Quality Trade-off Analysis for Planning Construction Projects. Eng. Constr. Archit. Manag. 2021, 28, 82–100. [Google Scholar] [CrossRef]
- Hua, Z.; Liu, Z.; Yang, L.; Yang, L. Improved Genetic Algorithm Based on Time Windows Decomposition for Solving Resource-Constrained Project Scheduling Problem. Autom. Constr. 2022, 142, 4503. [Google Scholar] [CrossRef]
- Eid, M.S.; Elbeltagi, E.E.; El-Adaway, H.I. Simultaneous Multi-Criteria Optimization for Scheduling Linear Infrastructure Projects. Int. J. Constr. Manag. 2021, 21, 41–55. [Google Scholar] [CrossRef]
- Abhilasha, P.; Jha, K.N. Integrating Quality and Safety in Construction Scheduling Time-Cost Trade-Off Model. J. Constr. Eng. Manag. 2021, 147, 1979. [Google Scholar] [CrossRef]
- Xie, F.; Li, H.; Xu, Z. Multi-Mode Resource-Constrained Project Scheduling with Uncertain Activity Cost. Expert Syst. Appl. 2021, 168, 4475. [Google Scholar] [CrossRef]
- Qin, M. Evolution of Labor Relations in the Development of Human Resources Based on Improved Genetic Algorithm. J. Circuits Syst. Comp. 2022, 31, 2723. [Google Scholar] [CrossRef]
- Tamrazyan, A.; Alekseytsev, A. Strategy for the evolutionary optimization of reinforced concrete frames based on parallel populations evolving. IOP Conf. Ser. Mater. Sci. Eng. 2020, 869, 052019. [Google Scholar] [CrossRef]
- Alekseytsev, A.V. Mechanical safety of reinforced concrete frames under complex emergency actions. Mag. Civ. Eng. 2021, 103. [Google Scholar] [CrossRef]
Schedules | Interruptions, Days | |||
---|---|---|---|---|
Criterion | OE1_Cages | OE2_Cages | OE1_Concrete | OE2_Concrete |
0 | 0 | 0, 1, 2, 3, 4, 5, 6 * | 2 | |
1 | 0 | 0, 1, 2, 3, 4, 5, 6 | 1 ** | |
2 | 0 | 0, 1, 2, 3, 4, 5, 6 | 2 | |
6 | 0, 1, 2, 3, 4, 5, 6 | 0, 1, 2, 3, 4, 5, 6 | 6 |
Schedule Model No. 1 (Figure 9) | Schedule Model No. 2 (Figure 10) | ||||||||
---|---|---|---|---|---|---|---|---|---|
No. of Critical Work Items | t, Days | ∆t, Days | Number of Workers a Day | ∆C, Thous. Conventional Units | No. of Critical Works | t, Days | ∆t, Days | Number of Workers a Day | ∆C, Thous. Conventional Units |
2 | 4 | 0.1 | 2(14); 2(12) * | 180 | 2 | 4 | 0,1 | 2(14); 2(12) | 180 |
5 | 4 | 0.1 | 2(14); 1(13); 1(12) | 180 | 5 | 4 | 0,1 | 2(14); 1(13); 1(12) | 180 |
11 | 3 | 0.1 | 2(10); 1(8) | 120 | 11 | 3 | 0,1 | 2(10); 1(8) | 120 |
18 | 4 | 0.1 | 1(14); 2(13); 1(12) | 180 | 15 | 4 | 0,1 | 2(14); 2(12) | 180 |
24 | 3 | 0.1 | 1(10); 1(9); 1(8) | 120 | 18 | 4 | 0,1 | 2(14); 1(13); 1(12) | 180 |
31 | 4 | 0.1 | 2(14); 2(12) | 180 | 24 | 3 | 0,1 | 2(10); 1(8) | 120 |
36 | 3 | 0.1 | 2(10); 1(8) | 120 | 28 | 4 | 0,1 | 2(14); 1(13); 1(12) | 180 |
- | - | - | - | - | 31 | 4 | 0,1 | 2(14); 2(12) | 180 |
- | - | - | - | - | 36 | 3 | 0,1 | 1(10); 1(9); 1(8) | 120 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alekseytsev, A.V.; Nadirov, S.H. Scheduling Optimization Using an Adapted Genetic Algorithm with Due Regard for Random Project Interruptions. Buildings 2022, 12, 2051. https://doi.org/10.3390/buildings12122051
Alekseytsev AV, Nadirov SH. Scheduling Optimization Using an Adapted Genetic Algorithm with Due Regard for Random Project Interruptions. Buildings. 2022; 12(12):2051. https://doi.org/10.3390/buildings12122051
Chicago/Turabian StyleAlekseytsev, Anatoly V., and Sodiqjon H. Nadirov. 2022. "Scheduling Optimization Using an Adapted Genetic Algorithm with Due Regard for Random Project Interruptions" Buildings 12, no. 12: 2051. https://doi.org/10.3390/buildings12122051