Optimization model for remanufacturing in a real sawmill
محورهای موضوعی : Mathematical Optimization
Lorena Pradenas
1
(Departamento de Ingeniería Industrial
Universidad de Concepción, Chile)
German Bravo
2
(Departamento de Ingeniería Industrial
Universidad de Concepción, Chile)
Rodrigo Linfati
3
(Departamento de Ingeniería Industrial
Universidad del Bío-Bío, Chile)
کلید واژه: Mixed Integer Programming, Forest industry, Planning in sawmills, Tactical planning, Forest optimization,
چکیده مقاله :
Sawmills are an important part of the forest supply chain, and as at any company, their production planning is highly complex. Planning in the remanufacturing area, in terms of its economic contribution to the sawmill and the supply chain, has not been studied in the scientific literature. The goal of this study was to develop and solve a mixed-integer linear programming model by employing an efficient allocation of cutting patterns on in-stock logs to maximize profits. To quantify the impact of an appropriate use of raw materials in the remanufacturing area in a sawmill, real and generated data were used. The model considers fixed and variable production costs, the availability of raw material, the capacity of the processes, the sale price of the products and the demand, for a process period of one month. The proposed compact mixed-integer linear programming model was solved using the commercial solver IBM ILGO CPLEX12.8. It was determined that the additional margin in USD earned in the remanufacturing area for the considered scenarios amounted to an average of 21.6%. The proposed method facilitates evaluating the economic contribution of remanufacturing while identifying bottlenecks and assessing proposed scenarios.
Engineering International, 767-782, https://doi.org/10.1007/s40092-018-0259-5 (0123456789().,-volV)(0123456789().,
[3] D’Amours, S., Ouhimmou, M., Audy, J.-F., & Feng, Y. (2016). Forest Value Chain Optimization and Sustainability. CRC Press.ISBN 9781498704861
[4] Dao S D, Abhary K, Marian R (2017). An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling. Journal of Industrial Engineering International, 13(2), 143–159.
[5] Fuentealba S., Pradenas L., Linfati R., and Ferland J. (2019). Forest harvest and sawmills: an integrated tactical planning model. Computers and Electronics in Agriculture, 156, 275-281,
doi.org/10.1016/j.compag.2018.11.011
[6] Hamidi, M., Shahanaghi, K., Jabbarzadeh, A., Jahani, E., & Pousti, Z. (2017). Determining production level under uncertainty using fuzzy simulation and bootstrap technique, a case study. Journal of Industrial Engineering International, 13, 487-497, DOI10.1007/s40092-017-0194-x
[7] Hafezalkotob, A., Chaharbaghi, S. & Moradi Laketh, T. (2019). Cooperative aggregate production planning: game theory approach. Journal of Industrial Engineering International,
https://doi.org/10.1007/s40092-019-0303-0
[8] Kumar, V. M., Murthy, A., & Chandrashekara, K. (2012). A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system. Journal of Industrial Engineering International, 8(1), 3. https://doi.org/10.1186/2251-712X-8-3
[9] Linfati-Medina R, Pradenas-Rojas L, Ferland J (2016). Planificación agregada en la cosecha forestal. Un Modelo de programación matemática y solución. Maderas-Cienc Tecnol 18(4),
555-566 DOI 10.4067/S0718-221X2016005000048.
[10] Mariajayaprakash A., Senthilvelan T., and Vivekananthan K. (2013). Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm. Journal of Industrial Engineering International, 9(18), 1-10. https://doi.org/10.1186/2251-712X-9-18
[11] Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges, and opportunities. Computers & Chemical Engineering, 33(12), 1919–1930. DOI:10.1016/j.compchemeng.2009.06.007
[12] Mehrjoo, S., & Bashiri, M. (2013). An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case.Journal of Industrial Engineering International, 9(1), 14. DOI https://doi.org/10.1186/2251-712X-9-14
[13] Noori-Darvish, S., & Tavakkoli-Moghaddam, R. (2012). Minimizing the total tardiness and makespan in an open shop scheduling problem with sequence-dependent setup times. Journal
of Industrial Engineering International, 8(1), 25. https://doi.org/10.1186/2251-712X-8-25.
[14] Ouhimmou, M., Rönnqvist, R., Lapointe, L.,A., (2021). Assessment of sustainable integration of new products into value chain through a generic decision support model: An application to the forest value chain. The International Journal of Management Science, 99. https://doi.org/10.1016/j.omega.2019.102173.
[15] Pereira, D., F., Oliveira, J., F., Carravilla, M.,A., (2020). Tactical sales and operations planning: A holistic framework and a literature review of decision-making models. International Journal of
Production Economics, 228. https://doi.org/10.1016/j.ijpe.2020.107695.
[16] Pradenas L, Alvarez C, Ferland J (2009) A solution for the aggregate production planning problem in a multi-plant, multi-period and multi-product environment. Acta Mathematica Vietnam,34 (1), 11-17.
[17] Pradenas L, Garcés J, Parada V, Ferland J (2013). Genotype-phenotype heuristic approaches for a cutting stock problem with circular patterns. Engineering applications of Artificial Intelligence, 26, 2340-2355. https://doi.org/10.1016/j.engappai.2013.08.003
[18] Da Silva L., E., Vieira, A., S., Lopes, S., G., Guimarães, A., C., Campos, T., M., Paula, T., P., (2020). Challenges of the lumber production in the Amazon region: relation between sustainability of
sawmills, process yield and logs quality. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-020-00797-9.
[19] Rahimi, A., Rönnqvist, M., Lebel, L., Audy, J., F., (2019). An Optimization Model for Selecting Wood Supply Contracts. Canadian Journal of Forest Research, 50 (4).https://doi.org/10.1139/cjfr-2019-0281.
[20] Rahimi, A., Rönnqvist, M., Lebel, L., Audy, J., F., (2020).Selecting wood supply contracts under uncertainty using stochastic programming. INFOR: Information Systems and Operational Research. https://doi.org/10.1080/03155986.2020.1800975.
[21] Rabbani, M., Ramezankhani, F., Giahi, R., & Farshbaf-Geranmayeh, A. (2016). Biofuel supply chain considering depreciation cost of installed plants. Journal of Industrial Engineering International, 12(2), 221–235.https://doi.org/10.1007/s40092-015-0139-1.
[22] Rönnqvist, M. (2003). Optimization in forestry. Mathematical Programming, 97(1-2), 267–284. http://doi.org/10.1007/s10107-003-0444-0.
[23] Troncoso, J., D’Amours, S., Flisberg, P., Rönnqvist, M., & Weintraub, A. (2015). A mixed integer programming model to evaluate integrating strategies in the forest value chain — a case study in the Chilean forest industry. Canadian Journal of Forest Research, 45(7), 937–949. https://doi.org/10.1139/cjfr-2014-0315.
[24] Vanzetti N., Broz D., Corsano G., & Montagna, J. M. (2018). No optimization approach for multiperiod production planning in a sawmill. Forest Policy and Economics, 97, 1-8.
[25] https://doi.org/10.1016/j.forpol.2018.09.001
[26] Weintraub, A., Romero, C., Bjørndal, T., & Epstein, R. (2007). Handbook of Operations Research in Natural Resources. Springer Science & Business Media. ISBN 978-0-387-71815-6
[27] Zanjani MK, Nourelfath M, Ait-Kadi D (2013). A scenario decomposition approach for stochastic production planning in sawmills. Journal of the Operational Research Society 64 (1), 48–59, https://doi.org/10.1057/jors.2012.28I. Column Format Instructions.
