Advances in Modelling of the Integrated Production Logistics in Sugarcane Harvest
DOI:
https://doi.org/10.7307/ptt.v34i4.4012Keywords:
modelling, logistics, sugar-energy production, energy, optimisationAbstract
The sugar-energy sector is extremely important to the Brazilian economy, with several other production chains derived from it, generating some of the main products linked to food and energy sources. This study proposes an integration model for sugarcane harvesting logistics processes, focusing on optimisation of industrial plant production capacity. Dynamic modelling has been applied to study a broad range of the productive phases of the sugar-energy chain. This paper proposes indicators to evaluate the degree of efficiency of the production logistics processes. Preliminary results showed that phase times in the production logistics processes can be significantly reduced in the harvest phase. When analysed as a coordination-oriented flow having chained activities, the production logistics processes optimise the speeds and travel times during the harvest phase. The developed model uses data set of the production and logistics processes phases of a sugarcane industry. A future study will focus on more detailed and complex stakeholder behaviours based on the model proposed.
References
Nova C. Processos da fabricação do etanol. 2020. https://www.novacana.com/etanol/fabricacao [Accessed 19th Aug. 2020].
Rodrigue J, Slack B, Comtois C. The Handbook of Logistics and Supply-Chain Management. In: Brewer A, Button K, Hensher D. (eds.) Handbooks in Transport. 2nd Ed. London: Pergamon/Elsevier; 2001.
Yoshizaki H, Muscat A, Biazzi, J. Decentralizing ethanol distribution in southeastern Brazil. Brazil: Interfaces; 1996. p. 24-34.
Kawamura M, Ronconi D, Yoshizaki H. Optimizing transportation and storage of final products in the sugar and ethanol industry. International Transactions in Operational Research. 2006;13(425-439): 1475-3995. doi: 10.1111/j.1475-3995.2006.00556.x.
Iannoni A, Morabito R. Análise do Sistema Logístico de Recepção de Cana de Açúcar: Um estudo de caso utilizando simulação discreta. Brazil: Gestão e Produção; 2002. p. 107-128.
Higgins A, et al. A framework for integrating a complex harvesting and transport system for sugar production. Agricultural Systems. 2004;82(2): 99-115. Doi: 10.1016/j.agsy.2003.12.004.
Cock J, Luna C, Palma A. The trade-off between total harvestable production and concentration of the economically useful yield component: Cane tonnage and sugar content. Field Crops Research. 2000;67(3): 257-262. doi: 10.1016/S0378-4290(00)00100-3.
Milan E, Fernandez S, Aragones L. Sugar cane transportation in Cuba, a case study. https://www.sciencedirect.com/science/article/abs/pii/S0377221705001554 [Accessed 21st Jan. 2020].
Silva A, Dias E, Marins F, Luche J. Análise da incerteza no planejamento agregado da produção e cogeração de energia utilizando um modelo de programação por metas multiescolha revisado: uma aplicação em uma usina sucroenergética. In: XLVII Simpósio Brasileiro de Pesquisa Operacional, 2015, Porto de Galinhas - PE. Anais do XLVII SBPO. Vol. 1. Rio de Janeiro - RJ: Sobrapo; 2015. p. 1403-1415.
Marins F, Montevechi B, Arnaldo J, Silva A. Aplicação de programação por metas binária: Mista em uma empresa do setor sucroenergético. Gestão & Produção. 2013;3(321-336): 1806-9649. doi: 10.1590/S0104-530X2013000200006.
Bastos K. Modelos de simulação para análise e apoio à decisão nos processos de corte mecanizado, carregamento e transporte no agronegócio da cana-de-açúcar. Brazil: UFG; 2009. p. 1-54.
Becker T. Chankov M. Windt K. Synchronization measures in job shop manufacturing environments. Procedia CIRP. 2013. p. 157-162.
Manrubia S, Mikhailov S, Zannette D. Emergence of Dynamical Order: Synchronization Phenomena in Complex Systems. London: World Scientific; 2004.
Ching, H. Gestão de Estoques na Cadeia de Logística Integrada. São Paulo: Atlas; 2010. p. 254.
Almeida P, Fonseca A, Figueiredo R. Análise das relações de longo prazo no setor sucroenergético: Uma abordagem utilizando cointegração e causalidade de granger. Gestão Inovação e Negócios. 2018;4(60-71): 2447-8520. doi: 10.29246/2358-9868.2018v4i1.p60-71.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Paulo ALMEIDA, Reinaldo CRISPINIANO GARCIA, Adelayda PALLAVICINI FONSECA
This work is licensed under a Creative Commons Attribution 4.0 International License.