Русский
English
The objective of this project is to enhance the technicues for creating informational models of alternative scenarios for the execution of the schedule and to expand the timeframe for predicting the progress of construction activities in the face of unpredictable factors. As a result of the study, the structure of a cellular automaton with memory, the cells of which quantitatively describe the states of objects of construction production, and the rules of transition between them were optimized. This paper introduces a comprehensive model framework for analyzing technologically and organizationally intertwined processes inherent in construction production. The model incorporates cellular automata to simulate spatial-temporal dynamics, vectors of complex resources to quantify heterogeneous inputs, and intricate process representations to capture the nuanced interdependencies within the cosnstruction system. A meticulously designed methodology has been developed to quantitatively evaluate technological and organizational capabilities, as well as the efficiency of implementing complex processes under constraints on both elemental and aggregated non-storage resources. This approach integrates advanced analytical techniques to assess performance metrics and identify optimization opportunities, ensuring alignment with strategic objectives and resource limitations. The proposed approach provides a robust analytical tool for optimizing construction workflows and enhancing overall project performance, leveraging advanced systems theory and resource optimization techniques.Methods for intensive and extensive optimization of complex process efficiency are formulated. Methods for optimal software implementation of the obtained algorithms are determined. In the shell of the relational database management system, a software package for forming basic and complex structures of a cellular automaton with memory is implemented.
1. Gorbaneva E.P., Bukhtoyarov A.V. Optimization of construction project implementation methods in calendar planning. Real estate: economics, management. 2024. 3. Р. 152 – 157.
2. Brucker P., Drexl A., Möhring R., Neumann K., and Pesch E. Resource-constrained project scheduling: Notation, classification, models, and methods. European Journal of Operational Research. 1999. 112 (1). Р. 3 – 41. DOI: 10.1016/S0377-2217(98)00204-5
3. Chauhan S.S., Eremeev A.V., Kolokolov A.A., Servakh V.V. Concave cost supply management for single manufacturing unit. In book: Supply Chain Optimisation. 2005. P. 167 – 174. DOI: 10.1007/0-387-23581-7_12
4. Ayad A.R., Awad H., Yassin A. Parametric analysis for genetic algorithms handling parameters. Alexandria Engineering Journal. 2013. 52 (1). P. 99. DOI: 10.1016/j.aej.2012.10.007
5. Christodoulou S. Scheduling Resource-constrained projects with ant colony optimization artificial agents. Journal of Computing in Civil Engineering. 2010. 24 (l). P. 45 – 55. DOI: 10.1061/(ASCE)0887-3801(2010)24:1(45)
6. Kianfar K. Branch‐and‐Bound Algorithms. In book: Wiley Encyclopedia of Operations Research and Management Science. 2011. P. 1 – 9. DOI: 10.1002/9780470400531.eorms0116
7. Ballesteros-Pérez P., Cerezo-Narváez A., Otero-Mateo M., Pastor A., Vanhoucke M. Performance comparison of activity sensitivity metrics in Schedule Risk Analysis. Automation in Construction. 2019. 106. P. 102906. DOI: 10.1016/j.autcon.2019.102906
8. Batselier J., Vanhoucke M. Construction and evaluation framework for a real-life project database. International Journal of Project Management. 2014. 33 (3). P 697 – 710. DOI: 10.1016/j.ijproman.2014.09.004
9. Lapidus A., Abramov I., Ali Z. Assessment of the impact of destabilizing factors on implementation of investment and construction projects. IOP Conference Series Materials Science and Engineering. 2020. 951 (1). P. 012028. DOI: 10.1088/1757-899X/951/1/012028
10. Lapidus A., Abramov I. Systemic integrated method for assessing factors affecting construction timelines. MATEC Web of Conferences. 2018. 193 (3). P. 05033. DOI: 10.1051/matecconf/201819305033
11. Dobrosotskikh M.G., Mishchenko V.Ya., Preobrazhenskii M.A. Improvement the scheduling based on the reducing of the dimensionality of the system. IOP Conference Series: Materials Science and Engineering. 2019. 481 (1). P.012029. DOI: 10.1088/1757-899X/481/1/012029
12. Lapidus A., Abramov I., Kuzmina T., Abramova A. Study of the Sustainable Functioning of Construction Companies in the Conditions of Risk Factors. Buildings. 2023. 13 (9). P. 2282. DOI: 10.3390/buildings13092282
13. Mishchenko A.V., Gorbaneva E.P., Preobrazhensky M.A. Reduction of the BIM dimension of the full life cycle of building and facilities. Russian Journal of Building Construction and Architecture. 2021. 4 (52). P. 95 – 105. DOI: 10.36622/VSTU.2021.52.4.009
14. Xie X., Lam J., Fan C. Robust time-weighted guaranteed cost control of uncertain periodic piecewise linear systems. Information Sciences. 2018. 460. P. 23. DOI: 10.1016/j.ins.2018.05.052
15. Ballestín F., Valls V., Quintanilla S., Quintanilla S. Scheduling projects with limited number of preemptions. Computers & Operations Research. 2009. 36 (11). P. 2913 – 2925. DOI: 10.1016/j.cor.2009.01.006
16. Xiong J., Chen Y., Liu J., Abbass H.A. An evolutionary multi-objective scenario-based approach for stochastic resource investment project scheduling. Conference: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). 2011. P. 2767 – 2774. DOI: 10.1109/CEC.2011.5949965
17. Andryushkevich S.K., Kovalev S. Distributed plants intelligent monitoring using information models of states. Bulletin of Tomsk Polytechnic University. Georesources engineering. 2010. 317 (5). P. 35 – 41.
18. Wu J., Liao F., Deng J. Optimal Preview Control for a Class of Linear Continuous Stochastic Control Systems in the Infinite Horizon. Mathematical Problems in Engineering. 2016. 3. P. 1 – 9. DOI: 10.1155/2016/7679165
19. Gorbaneva E.P., Mishchenko A.V. BIM technologies to optimize the catch-up schedule for the implementation of the construction schedule plan, taking into account external stochastic impacts. Real Estate: Economics, Management. 2022. 1. P. 58 – 67. DOI: 10.22337/2073-8412-2022-1-58-67
20. Gorbaneva E.P., Mishchenko A.V. BIM technologies for monitoring and dynamic adjustment of the implementation of the construction calendar plan. Russian Journal of Building Construction and Architecture. 2022. 4 (68). P. 83 – 95. DOI: 10.36622/VSTU.2022.56.4.006
21. Shvedovskiy V.A. Cellular automaton with percolation as a dynamic system: Entropy approach. Computational Mathematics and Information Technologies. 2021. 1 (2). P. 61 – 71. DOI: 10.23947/2587-8999-2021-1-2-61-71
22. Jun D.H., El-Rayes K. Multiobjective optimization of resource leveling and allocation during construction scheduling. Journal of Construction Engineering and Management. 2011. 137 (12). P. 1080 – 1088. DOI: 10.1061/(ASCE)CO.1943-7862.0000368
23. Khrustalev B., Grabovy P., Grabovy K., Kargin A. Features of the information modeling use of real estate objects in the housing market. E3S Web of Conferences. 2022. 363 (58). P. 02038. DOI: 10.1051/e3sconf/202236302038
24. Mishchenko V.Ya., Gorbaneva Ye.P., Preobrazhensky M.A., Bukhtoyarov A.V. Dynamics of Technical and Technological Processes in Construction under Conditions Of Stochastic Impacts. Russian Journal of Building Construction and Architecture. 2024. 2 (62). P. 70 – 82. DOI: 10.36622/2542-0526.2024.62.2.007
25. Saeed A. Role of Database Management Systems (DBMS) in Supporting Information Technology in Sector of Education. 2015. P. 1462 – 1466. DOI: 10.21275/ART20173499
26. Mishchenko V.Ya., Gorbaneva E.P., Bukhtoyarov A.V. Scenario method of forecasting and dynamic adjustment of the construction calendar plan in conditions of stochastic impacts. News of higher educational institutions. Construction. 2025. 3 (795). P. 87 – 101. DOI: 10.32683/0536-1052-2025-795-3-87-101 (In Russ.)
2. Brucker P., Drexl A., Möhring R., Neumann K., and Pesch E. Resource-constrained project scheduling: Notation, classification, models, and methods. European Journal of Operational Research. 1999. 112 (1). Р. 3 – 41. DOI: 10.1016/S0377-2217(98)00204-5
3. Chauhan S.S., Eremeev A.V., Kolokolov A.A., Servakh V.V. Concave cost supply management for single manufacturing unit. In book: Supply Chain Optimisation. 2005. P. 167 – 174. DOI: 10.1007/0-387-23581-7_12
4. Ayad A.R., Awad H., Yassin A. Parametric analysis for genetic algorithms handling parameters. Alexandria Engineering Journal. 2013. 52 (1). P. 99. DOI: 10.1016/j.aej.2012.10.007
5. Christodoulou S. Scheduling Resource-constrained projects with ant colony optimization artificial agents. Journal of Computing in Civil Engineering. 2010. 24 (l). P. 45 – 55. DOI: 10.1061/(ASCE)0887-3801(2010)24:1(45)
6. Kianfar K. Branch‐and‐Bound Algorithms. In book: Wiley Encyclopedia of Operations Research and Management Science. 2011. P. 1 – 9. DOI: 10.1002/9780470400531.eorms0116
7. Ballesteros-Pérez P., Cerezo-Narváez A., Otero-Mateo M., Pastor A., Vanhoucke M. Performance comparison of activity sensitivity metrics in Schedule Risk Analysis. Automation in Construction. 2019. 106. P. 102906. DOI: 10.1016/j.autcon.2019.102906
8. Batselier J., Vanhoucke M. Construction and evaluation framework for a real-life project database. International Journal of Project Management. 2014. 33 (3). P 697 – 710. DOI: 10.1016/j.ijproman.2014.09.004
9. Lapidus A., Abramov I., Ali Z. Assessment of the impact of destabilizing factors on implementation of investment and construction projects. IOP Conference Series Materials Science and Engineering. 2020. 951 (1). P. 012028. DOI: 10.1088/1757-899X/951/1/012028
10. Lapidus A., Abramov I. Systemic integrated method for assessing factors affecting construction timelines. MATEC Web of Conferences. 2018. 193 (3). P. 05033. DOI: 10.1051/matecconf/201819305033
11. Dobrosotskikh M.G., Mishchenko V.Ya., Preobrazhenskii M.A. Improvement the scheduling based on the reducing of the dimensionality of the system. IOP Conference Series: Materials Science and Engineering. 2019. 481 (1). P.012029. DOI: 10.1088/1757-899X/481/1/012029
12. Lapidus A., Abramov I., Kuzmina T., Abramova A. Study of the Sustainable Functioning of Construction Companies in the Conditions of Risk Factors. Buildings. 2023. 13 (9). P. 2282. DOI: 10.3390/buildings13092282
13. Mishchenko A.V., Gorbaneva E.P., Preobrazhensky M.A. Reduction of the BIM dimension of the full life cycle of building and facilities. Russian Journal of Building Construction and Architecture. 2021. 4 (52). P. 95 – 105. DOI: 10.36622/VSTU.2021.52.4.009
14. Xie X., Lam J., Fan C. Robust time-weighted guaranteed cost control of uncertain periodic piecewise linear systems. Information Sciences. 2018. 460. P. 23. DOI: 10.1016/j.ins.2018.05.052
15. Ballestín F., Valls V., Quintanilla S., Quintanilla S. Scheduling projects with limited number of preemptions. Computers & Operations Research. 2009. 36 (11). P. 2913 – 2925. DOI: 10.1016/j.cor.2009.01.006
16. Xiong J., Chen Y., Liu J., Abbass H.A. An evolutionary multi-objective scenario-based approach for stochastic resource investment project scheduling. Conference: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). 2011. P. 2767 – 2774. DOI: 10.1109/CEC.2011.5949965
17. Andryushkevich S.K., Kovalev S. Distributed plants intelligent monitoring using information models of states. Bulletin of Tomsk Polytechnic University. Georesources engineering. 2010. 317 (5). P. 35 – 41.
18. Wu J., Liao F., Deng J. Optimal Preview Control for a Class of Linear Continuous Stochastic Control Systems in the Infinite Horizon. Mathematical Problems in Engineering. 2016. 3. P. 1 – 9. DOI: 10.1155/2016/7679165
19. Gorbaneva E.P., Mishchenko A.V. BIM technologies to optimize the catch-up schedule for the implementation of the construction schedule plan, taking into account external stochastic impacts. Real Estate: Economics, Management. 2022. 1. P. 58 – 67. DOI: 10.22337/2073-8412-2022-1-58-67
20. Gorbaneva E.P., Mishchenko A.V. BIM technologies for monitoring and dynamic adjustment of the implementation of the construction calendar plan. Russian Journal of Building Construction and Architecture. 2022. 4 (68). P. 83 – 95. DOI: 10.36622/VSTU.2022.56.4.006
21. Shvedovskiy V.A. Cellular automaton with percolation as a dynamic system: Entropy approach. Computational Mathematics and Information Technologies. 2021. 1 (2). P. 61 – 71. DOI: 10.23947/2587-8999-2021-1-2-61-71
22. Jun D.H., El-Rayes K. Multiobjective optimization of resource leveling and allocation during construction scheduling. Journal of Construction Engineering and Management. 2011. 137 (12). P. 1080 – 1088. DOI: 10.1061/(ASCE)CO.1943-7862.0000368
23. Khrustalev B., Grabovy P., Grabovy K., Kargin A. Features of the information modeling use of real estate objects in the housing market. E3S Web of Conferences. 2022. 363 (58). P. 02038. DOI: 10.1051/e3sconf/202236302038
24. Mishchenko V.Ya., Gorbaneva Ye.P., Preobrazhensky M.A., Bukhtoyarov A.V. Dynamics of Technical and Technological Processes in Construction under Conditions Of Stochastic Impacts. Russian Journal of Building Construction and Architecture. 2024. 2 (62). P. 70 – 82. DOI: 10.36622/2542-0526.2024.62.2.007
25. Saeed A. Role of Database Management Systems (DBMS) in Supporting Information Technology in Sector of Education. 2015. P. 1462 – 1466. DOI: 10.21275/ART20173499
26. Mishchenko V.Ya., Gorbaneva E.P., Bukhtoyarov A.V. Scenario method of forecasting and dynamic adjustment of the construction calendar plan in conditions of stochastic impacts. News of higher educational institutions. Construction. 2025. 3 (795). P. 87 – 101. DOI: 10.32683/0536-1052-2025-795-3-87-101 (In Russ.)
Mishchenko V.Ya., Lapidus A.A., Topchiy D.V., Gorbaneva E.P. Calendar planning of construction production, taking into account stochastic impacts. Construction Materials and Products. 2025. 8 (4). 9. https://doi.org/10.58224/2618-7183-2025-8-4-9