The sustainable design of the supply chain of bioethanol production of sugarcane
Subject Areas :
1 - University of Science and Technology
2 -
Keywords: Supply chain Sustainable supply chain Bioethanol Scenario based programming, Distruption,
Abstract :
High cost, the risk of ending fossil fuels and pollution severely threatens metropolitan areas today and therefore paying attention to renewable energies is inevitable. This paper presents a three-level supply chain of bioethanol production with the three objectives of maximizing profit, reducing environmental impacts and maximizing social impacts, using the Epsilon-constraint method for optimization. On the third level, there are several consumer markets that satisfy end-customer demand. The type of feedstock intended is sugarcane, which can be used to produce bioethanol. The planning horizon of the model is multi-period, and the relationship of chain levels to each other may be disrupted. Finally, a case study in the Southwest region of Iran demonstrates the function of the model.
1.Shabani, N. and T. Sowlati, A hybrid multi-stage stochastic programming-robust optimization model for maximizing the supply chain of a forest-based biomass power plant considering uncertainties. Journal of Cleaner Production, 2016. 112: p. 3285-3293.
2. Andersen, F., et al., Optimal design and planning of biodiesel supply chain with land competition. Computers & Chemical Engineering, 2012. 47: p. 170-182.
3. Huang, Y., Y. Fan, and C.-W. Chen, An integrated biofuel supply chain to cope with feedstock seasonality and uncertainty. Transportation Science, 2014. 48(4): p. 540-554.
4. Sharma, B., et al., Scenario optimization modeling approach for design and management of biomass-to-biorefinery supply chain system. Bioresource technology, 2013. 150: p. 163-171.
5. Ng, W.P.Q., H.L. Lam, and S. Yusup, Supply network synthesis on rubber seed oil utilisation as potential biofuel feedstock. Energy, 2013. 55: p. 82-88.
6. Leduc, S., et al., Location of a biomass based methanol production plant: a dynamic problem in northern Sweden. Applied Energy, 2010. 87(1): p. 68-75.
7. Duarte, A., W. Sarache, and Y. Costa, Biofuel supply chain design from Coffee Cut Stem under environmental analysis. Energy, 2016. 100: p. 321-331.
8. Marufuzzaman, M., S.D. Ekşioğlu, and R. Hernandez, Environmentally friendly supply chain planning and design for biodiesel production via wastewater sludge. Transportation Science, 2014. 48(4): p. 555-574.
9. Sharifzadeh, M., M.C. Garcia, and N. Shah, Supply chain network design and operation: Systematic decision-making for centralized, distributed, and mobile biofuel production using mixed integer linear programming (MILP) under uncertainty. Biomass and Bioenergy, 2015. 81: p. 401-414.
10. Zhang, Y. and M.M. Wright, Product selection and supply chain optimization for fast pyrolysis and biorefinery system. Industrial & Engineering Chemistry Research, 2014. 53(51): p. 19987-19999.
11. You, F. and B. Wang, Life cycle optimization of biomass-to-liquid supply chains with distributed–centralized processing networks. Industrial & Engineering Chemistry Research, 2011. 50(17): p. 10102-10127.
12. Marufuzzaman, M., et al., Supply chain design and management for syngas production. ACS Sustainable Chemistry & Engineering, 2016. 4(3): p. 890-900.
13. Li, Q. and G. Hu, Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification. Energy, 2014. 74: p. 576-584.
14. Marvin, W.A., et al., Economic optimization of a lignocellulosic biomass-to-ethanol supply chain. Chemical Engineering Science, 2012. 67(1): p. 68-79.
15. Corsano, G., A.R. Vecchietti, and J.M. Montagna, Optimal design for sustainable bioethanol supply chain considering detailed plant performance model. Computers & Chemical Engineering, 2011. 35(8): p. 1384-1398.
16. Osmani, A. and J. Zhang, Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 2013. 59: p. 157-172.
17. Gonela, V., J. Zhang, and A. Osmani, Stochastic optimization of sustainable industrial symbiosis based hybrid generation bioethanol supply chains. Computers & Industrial Engineering, 2015. 87: p. 40-65.
18. Kostin, A., et al., Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. chemical engineering research and design, 2012. 90(3): p. 359-376.
19. Zhang, J., et al., An integrated optimization model for switchgrass-based bioethanol supply chain. Applied Energy, 2013. 102: p. 1205-1217.
20. Ahn, Y.-C., et al., Strategic planning design of microalgae biomass-to-biodiesel supply chain network: multi-period deterministic model. Applied Energy, 2015. 154: p. 528-542.
21. Dal-Mas, M., et al., Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass and bioenergy, 2011. 35(5): p. 2059-2071.
22. Chen, C.-W. and Y. Fan, Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E: Logistics and Transportation Review, 2012. 48(1): p. 150-164.
23. Ghaderia, H., M. Asadia, and S. Shavvalpour, A Switchgrass-based Bioethanol Supply Chain Network Design Model under Auto-Regressive Moving Average Demand.
24. Akgul, O., et al., Optimization-based approaches for bioethanol supply chains. Industrial & Engineering Chemistry Research, 2010. 50(9): p. 4927-4938.
25. Bai, Y., et al., Biofuel refinery location and supply chain planning under traffic congestion. Transportation Research Part B: Methodological, 2011. 45(1): p. 162-175.
26. Ehrgott, M. and X. Gandibleux, Multiobjective combinatorial optimization—theory, methodology, and applications, in Multiple criteria optimization: State of the art annotated bibliographic surveys. 2003, Springer. p. 369-444.
27. Bérubé, J.-F., M. Gendreau, and J.-Y. Potvin, An exact ϵ-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits. European Journal of Operational Research, 2009. 194(1): p. 39-50.
28. Mele, F.D., et al., Multiobjective model for more sustainable fuel supply chains. A case study of the sugar cane industry in Argentina. Industrial & Engineering Chemistry Research, 2011. 50(9): p. 4939-4958.