Keywords
production flow
critical machine
simulation model
log length
How to Cite
##article.highlights##
- The production of a Pinus radiata sawmill was optimized through the analysis of critical machines.
- Two simulation models were developed for the sawing of 2.5 and 5.0 m long logs.
- The number of logs processed per shift was the response variable of the model.
- The modification of four factors in the process flow improved the production level.
- Production increased by 13 and 18 % when 2.5 and 5.0 m logs, respectively, were processed.
Abstract
Introduction: When sawing logs without diameter sorting, the cutting program generates decisions and routes depending on the log’s appearance and machine load. Objective: To optimize the production flow of a custom-cut Pinus radiata D. Don sawmill, through detection and analysis of critical machines, using discrete event simulation and experimental design. Materials and methods: Two simulation models were developed for sawing logs with lengths of 2.5 and 5.0 m, and diameters between 34 and 44 cm to produce sawnwood with a thickness of 5/4" with variable width. The number of logs processed per shift was the response variable of the model. Heavily-used machines and transports with long queues were candidates for critical machines. The impact on production was determined by means of experimental design, where the factors assessed were: (A) delivery from bandsaw carriage 1 to band saw 3, (B) increased debarker capacity, (C) failures eliminated from bandsaw 1 and 2, and (D) direct delivery from bandsaw 2 to edger 2. Results and discussion: Modifications to the production flow were proposed because factors B, C and D significantly increased (P = 0.1) the production level (logs per shift); the increases over the sawmill’s initial condition were 13 and 18 % for lengths of 2.5 and 5.0 m, respectively. Conclusion: Simulation and experimental design can be applied in small and medium-sized sawmills to improve production when processing logs without diameter sorting.References
Álvarez-Lazo, D., Andrade-Fernando, E., Quintin-Cuador, G., & Domínguez-Goizueta, A. (2004). Importancia del control de las dimensiones de la madera aserrada. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 10(2), 105–110. Retrieved from https://www.chapingo.mx/revistas/forestales/contenido.php?seccion=numero&id_revista_numero=30
Arena. (2007). Arena® simulation software. Version 3.5 para Windows. Milwaukee, WI, USA: Rockwell Automation. Retrieved from https://www.arenasimulation.com/what-is-simulation/discrete-event-simulation-software
Azadeh, A., & Maghsoudi, A. (2010). Optimization of production systems through integration of computer simulation, design of experiment, and Tabu search: The case of a large steelmaking workshop. The International Journal of Advanced Manufacturing Technology, 48(5), 785−800. doi: https://doi.org/10.1007/s00170-009-2305-3
Baesler, F., Araya. E., Ramis, F., & Sepúlveda, J. (2004). The use of simulation and design of experiments for productivity improvement in the sawmill industry. In R. G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters, & W. Hilton (Eds.), Proceedings of the 2004 winter simulation conference (vol. 2, pp. 1218−1221). USA: IEEE Xplore. doi: https://doi.org/10.1109/WSC.2004.1371452
Cown, D., McConchie, D., & Treolar, C. (1984). Timber recovery from pruned Pinus radiata butt logs at mangatu: Effect of log sweep. New Zealand Journal of Forestry Science, 14(1), 109−123. Retrieved from https://www.scionresearch.com/__data/assets/pdf_file/0019/30916/NZJFS1411984COWN109_123.pdf
Design Expert. (2007). Design Expert 6.0 para Windows. Minneapolis, MN, USA: Stat-Easy Inc.
Di Gironimo, G., Balsamo, A., Esposito, G., Lanzotti, A., Melemez, K., & Spinelli, R. (2015). Simulation of forest harvesting alternative processes and concept design of an innovative skidding winch focused on productivity improvement. Turkish Journal of Agriculture and Forestry, 39(2), 350–359. doi: https://doi.org/10.3906/tar-1408-64
Flores-Velázquez, R., Serrano-Gálvez, E., Palacio-Muñoz, V., & Chapela, G. (2007). Análisis de la industria de la madera aserrada en México. Madera y Bosques, 13(1), 47–59. Retrieved from http://www.redalyc.org/pdf/617/61713105.pdf
Grigolato, S., Bietresato, M., Asson, D., & Cavalli, R. (2011). Evaluation of the manufacturing of desk and stringer boards for wood pallets production by discrete event simulation. Biosystems Engineering, 109(4), 288–296. doi: https://doi.org/10.1016/j.biosystemseng.2011.04.009
Heshmat, M., El-Sharief, M. A., & El-Sebaie, M. G. (2013). Simulation modeling of automatic production lines with intermediate buffers. International Journal of Scientific & Engineering Research, 4(7), 2528−2535. Retrieved from https://www.researchgate.net/publication/303876043_SIMULATION_MODELING_OF_AUTOMATIC_PRODUCTION_LINES_WITH_INTERMEDIATE_BUFFERS
Hogg, G. A., Pulkki, R. E., & Ackerman, P. A. (2010). Multi-stem mechanized harvesting operation analysis- application of Arena 9 discrete event simulation software in Zululand, South Africa. International Journal of Forest Engineering, 21(2), 14–22. doi: https://doi.org/10.1080/14942119.2010.10702594
Instituto Forestal (INFOR). (2016). Anuario forestal 2015. Chile: Author. Retrieved from https://wef.infor.cl/publicaciones/anuario/2015/Anuario2015.pdf
Lohmander, P. (2007). Adaptative optimization of forest management in a stochastic world. In A. Weintraub, C. Romero, T. Bjørndal, R. Epstein, & J. Miranda (Eds.), Handbook of operations research in natural resources (525–543). Boston, MA, USA: Springer. doi: https://doi.org/10.1007/978-0-387-71815-6_28
Marques, A. F., De Sousa, J. P., Rönnqvist, M., & Jafe, R. (2014). Combining optimization and simulation tools for short-term planning of forest operations. Scandinavian Journal of Forest Research, 29(1), 166–177. doi: https://doi.org/10.1080/02827581.2013.856937
Meimban, R., Mendoza, G., Araman, P., & Luppold, W. (1991). A simulation model for a hardwood decision support system. Journal of Forest Engineering, 4(1), 39–47. Retrieved from https://journals.lib.unb.ca/index.php/IJFE/article/view/10059/10315
Memari, A., Zahraee, S. M., Anjomanshoae, A., & Rahim, A. R. B. A. (2013). Performance assessment in a production-distribution network using simulation. Caspian Journal of Applied Sciences Research, 2(5), 48–56. Retrieved from https://www.researchgate.net/publication/259532370_Performance_Assessment_in_a_Production-Distribution_Network_Using_Simulation
Montgomery, D. C., Runger, G. C., & Medal, E. G. U. (1996). Probabilidad y estadística aplicada a la ingeniería. México: McGraw-Hill.
Opacic, L., & Sowlati, T. (2017). Applications of discrete-event simulation in the forest products sector: A review. Forest Products Journal, 67(3-4), 219−229. doi: https://doi.org/10.13073/FPJ-D-16-00015
Opacic, L., Sowlati, T., & Mobini, M. (2018). Design and development of a simulation-based decision support tool to improve the production process at an engineering wood products mill. International Journal of Production Economics, 199, 209–219. doi: https://doi.org/10.1016/j.ijpe.2018.03.010
Parthanadee, P., & Buddhakulsomsiri, J. (2014). Production efficiency improvement in batch production system using value stream mapping and simulation: A case study of the roasted and ground coffee industry. Production Planning & Control, 25(5), 425–446. doi: https://doi.org/10.1080/09537287.2012.702866
Spinelli, R., Di Gironimo, G., Esposito, G., Magagnotti, N. (2014). Alternative supply chain for logging residues under access constraints. Scandinavian Journal of Forest Research, 29(3), 266–274. doi: https://doi.org/10.1080/02827581.2014.896939
Steele, P. (1984). Factors determining lumber recovery in sawmilling. Retrieved from https://www.fpl.fs.fed.us/documnts/fplgtr/fplgtr39.pdf
Thoews, S. E., Maness, T. C., & Ristea, C. (2008). Using flow simulation as a decision tool for improvements in sawmill productivity. Maderas. Ciencia y Tecnología, 10(3), 229−242. doi: https://doi.org/10.4067/S0718-221X2008000300006
Quintero, M., & Rosso, F. (2001). Propuesta de un sistema simulador de aserraderos para la industria forestal venezolana. Revista Forestal Venezolana, 45(1), 95–101. Retrieved from http://www.saber.ula.ve/handle/123456789/24377
Walker, J. F. C. (2006). Primary wood processing, principles and practices (2nd ed.). Dordrech, Netherlands: Springer Science & Business Media.
Wolfsmayr, U. J., Merenda, R., Rauch, P., Longo, F., & Gronalt, M. (2016). Evaluating primary forest fuel rail terminals with discrete event simulation: A case study from Austria. Annals of Forest Research, 59(1), 145–164. doi: https://doi.org/10.15287/afr.2015.428
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2019 Revista Chapingo Serie Ciencias Forestales y del Ambiente