Effective allocation of manpower in the production of precast concrete elements with the use of metaheuristics
Abstract
Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered – the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms.
Keyword : scheduling, hybrid flow shop, precast concrete production, optimization, management, metaheuristics
How to Cite
Podolski, M. (2022). Effective allocation of manpower in the production of precast concrete elements with the use of metaheuristics. Journal of Civil Engineering and Management, 28(4), 247–260. https://doi.org/10.3846/jcem.2022.16383
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Lumdsen, P. (1968). The line of balance method. Pergamon Press.
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Ogbu, F., & Smith, D. (1990). The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem. Computers & Operations Research, 17(3), 243–253. https://doi.org/10.1016/0305-0548(90)90001-N
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Podolski, M., & Sroka, B. (2019). Cost optimization of multiunit construction projects using linear programming and metaheuristic-based simulated annealing algorithm. Journal of Civil Engineering and Management, 25(8), 848–857. https://doi.org/10.3846/jcem.2019.11308
Podolski, M., & Rejment, M. (2019). Scheduling the production of precast concrete elements using the simulated annealing metaheuristic algorithm. IOP Conference Series: Materials Science and Engineering, 471, 112083. https://doi.org/10.1088/1757-899X/471/11/112083
Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18. https://doi.org/10.1016/j.ejor.2009.09.024
Su, Y., & Lucko, G. (2016). Linear scheduling with multiple crews based on line-of-balance and productivity scheduling method with singularity functions. Automation of Construction, 70, 38–50. https://doi.org/10.1016/j.autcon.2016.05.011
Tharmmaphornphilas, W., & Sareinpithak, N. (2013). Formula selection and scheduling for precast concrete production. International Journal of Production Research, 51(17), 5195–5209. https://doi.org/10.1080/00207543.2013.795250
Wang, Z., Hu, H., & Gong, J. (2018). Framework for modelling operational uncertainty to optimize offsite production scheduling of precast components. Automation in Construction, 86, 69–80. https://doi.org/10.1016/j.autcon.2017.10.026
Warszawski, A. (1984). Production planning in prefabrication plant. Building and Environment, 19(2), 139–147. https://doi.org/10.1016/0360-1323(84)90039-8
Wittrock, R. J. (1988). An adaptable scheduling algorithm for flexible flow lines. Operational Research, 36(3), 445–453. https://doi.org/10.1287/opre.36.3.445
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321–329. https://doi.org/10.1016/j.autcon.2016.08.021
Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021b). Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887–5958. https://doi.org/10.1002/int.22535
Addo-Tenkorang, R., & Helo, P. (2011, October). Enterprise resource planning (ERP): A review literature report. In Proceedings of the World Congress on Engineering and Computer Science (WCECS 2011) (Vol. 2), San Francisco, USA.
Anvari, B., Angeloudis, P., & Ochieng, W. (2016). A multi-objective GA-based optimisation for holistic manufacturing, transportation and assembly of precast construction. Automation in Construction, 71, 226–241. https://doi.org/10.1016/j.autcon.2016.08.007
Bennett, D. (2005). Precast concrete: Tone texture form. Birkhaeuser.
Chan, W. T., & Hu, H. (2002a). Constraint programming approach to precast production scheduling. Journal of Construction Engineering and Management, 128(6), 513–521. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:6(513)
Chan, W. T., & Hu, H. (2002b). Production scheduling for precast plants using a flowshop sequencing model. Journal of Computing in Civil Engineering, 16(3), 165–174. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(165)
Cochran, J. K., Horng, S. M., & Fowler, J. W. (2003). A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Computers and Operations Research, 30(7), 1087–1102. https://doi.org/10.1016/S0305-0548(02)00059-X
Coello, C. A. C., Lamont, G. B., & Veldhuizen, D. A. V. (2007). Evolutionary algorithms for solving multi-objective problems. Springer.
Dawood, N. N. (1995). Scheduling in the precast concrete industry using the simulation modelling approach. Building and Environment, 30, 197–207. https://doi.org/10.1016/0360-1323(94)00039-U
Dawood, N. N. (1996). A simulation model for eliciting scheduling knowledge: An application to the precast manufacturing process. Advances in Engineering Software, 25, 215–223. https://doi.org/10.1016/0965-9978(95)00096-8
Dawood, N. N. & Neale, R. H. (1993). Capacity planning model for precast concrete building products. Building and Environment, 28, 81–95. https://doi.org/10.1016/0360-1323(93)90009-R
Glover, F., & Laguna, M. (1995) Tabu search. Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-6089-0
Hayyolalam, V., & Kazem A. A. P. (2020). Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249. https://doi.org/10.1016/j.engappai.2019.103249
Huang, T., & Yasuda, K. (2016). Comprehensive review of literature survey articles on ERP. Business Process Management Journal, 22(1), 2–32. https://doi.org/10.1108/BPMJ-12-2014-0122
International Business Machines Corporation. (2021, November 10). IBM ILOG CPLEX Optimizer. https://www.ibm.com/products/ilog-cplex-optimization-studio
Ishibuchi, H., Misaki, S., & Tanaka, H. (1995). Modified simulated annealing algorithms for the flow shop sequencing problem. European Journal of Operational Research, 81, 388–398. https://doi.org/10.1016/0377-2217(93)E0235-P
Kirkpatrick, S., Gelatt, C. D., & Vecchi M. P. (1983). Optimization by simulated annealing. In M. Mezard, G. Parisi, & M. Virasoro (Eds), World Scientific lecture notes in physics: Vol. 9. Spin glass theory and beyond (pp. 339–348). https://doi.org/10.1142/9789812799371_0035
Ko, C. H., & Wang, S. F. (2010). GA-based decision support systems for precast production planning. Automation in Construction, 19(7), 907–916. https://doi.org/10.1016/j.autcon.2010.06.004
Ko, C. H., & Wang, S. F. (2011). Precast production scheduling using multi-objective genetic algorithms. Expert Systems with Applications, 38(7), 8293–8302. https://doi.org/10.1016/j.autcon.2016.08.021
Lumdsen, P. (1968). The line of balance method. Pergamon Press.
Ma, Z., Yang, Z., Liu, S., & Wu, S. (2018). Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. Automation in Construction, 95, 86–97. https://doi.org/10.1016/j.autcon.2018.08.002
Nowicki, E., & Smutnicki, C. (1998). The flow shop with parallel machines: A tabu search approach. European Journal of Operational Research, 106, 226–253. https://doi.org/10.1016/S0377-2217(97)00260-9
Ogbu, F., & Smith, D. (1990). The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem. Computers & Operations Research, 17(3), 243–253. https://doi.org/10.1016/0305-0548(90)90001-N
Podolski, M. (2016). Scheduling of job resources in multiunit projects with the use of time / cost criteria. Archives of Civil Engineering, 62(1), 143–158. https://doi.org/10.1515/ace-2015-0057
Podolski, M., & Sroka, B. (2019). Cost optimization of multiunit construction projects using linear programming and metaheuristic-based simulated annealing algorithm. Journal of Civil Engineering and Management, 25(8), 848–857. https://doi.org/10.3846/jcem.2019.11308
Podolski, M., & Rejment, M. (2019). Scheduling the production of precast concrete elements using the simulated annealing metaheuristic algorithm. IOP Conference Series: Materials Science and Engineering, 471, 112083. https://doi.org/10.1088/1757-899X/471/11/112083
Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18. https://doi.org/10.1016/j.ejor.2009.09.024
Su, Y., & Lucko, G. (2016). Linear scheduling with multiple crews based on line-of-balance and productivity scheduling method with singularity functions. Automation of Construction, 70, 38–50. https://doi.org/10.1016/j.autcon.2016.05.011
Tharmmaphornphilas, W., & Sareinpithak, N. (2013). Formula selection and scheduling for precast concrete production. International Journal of Production Research, 51(17), 5195–5209. https://doi.org/10.1080/00207543.2013.795250
Wang, Z., Hu, H., & Gong, J. (2018). Framework for modelling operational uncertainty to optimize offsite production scheduling of precast components. Automation in Construction, 86, 69–80. https://doi.org/10.1016/j.autcon.2017.10.026
Warszawski, A. (1984). Production planning in prefabrication plant. Building and Environment, 19(2), 139–147. https://doi.org/10.1016/0360-1323(84)90039-8
Wittrock, R. J. (1988). An adaptable scheduling algorithm for flexible flow lines. Operational Research, 36(3), 445–453. https://doi.org/10.1287/opre.36.3.445
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321–329. https://doi.org/10.1016/j.autcon.2016.08.021