Biofuel supply chain considering depreciation cost of installed plants
Tóm tắt
Due to the depletion of the fossil fuels and major concerns about the security of energy in the future to produce fuels, the importance of utilizing the renewable energies is distinguished. Nowadays there has been a growing interest for biofuels. Thus, this paper reveals a general optimization model which
enables the selection of preprocessing centers for the biomass, biofuel plants, and warehouses to store the biofuels. The objective of this model is to maximize the total benefits. Costs of the model consist of setup cost of preprocessing centers, plants and warehouses, transportation costs, production costs, emission cost and the depreciation cost. At first, the deprecation cost of the centers is calculated by means of three methods. The model chooses the best depreciation method in each period by switching between them. A numerical example is presented and solved by CPLEX solver in GAMS software and finally, sensitivity analyses are accomplished.
Tài liệu tham khảo
Akgul O, Zamboni A, Bezzo F, Shah N, Papageorgiou LG (2011) Optimization-based approaches for bioethanol supply chains. Ind Eng Chem Res 50(9):4927–4938. doi:10.1021/ie101392y
Akgul O, Shah N, Papageorgiou LG (2012) Economic optimisation of a UK advanced biofuel supply chain. Biomass Bioenergy 41:57–72. doi:10.1016/j.biombioe.2012.01.040
Alimardani M, Jolai F, Rafiei H (2013) Bi-product inventory planning in a three-echelon supply chain with backordering, Poisson demand, and limited warehouse space. J Ind Eng Int 9(1):1–13. doi:10.1186/2251-712X-9-22
AriaNezhad MG, Makuie A, Khayatmoghadam S (2013) Developing and solving two-echelon inventory system for perishable items in a supply chain: case study (Mashhad Behrouz Company). J Ind Eng Int 9(1):1–10. doi:10.1186/2251-712X-9-39
Ayoub N, Yuji N (2012) Demand-driven optimization approach for biomass utilization networks. Comput Chem Eng 36:129–139. doi:10.1016/j.compchemeng.2011.09.005
Ayoub N, Martins R, Wang K, Seki H, Naka Y (2007) Two levels decision system for efficient planning and implementation of bioenergy production. Energy Convers Manage 48(3):709–723. doi:10.1016/j.enconman.2006.09.012
Ayoub N, Elmoshi E, Seki H, Naka Y (2009) Evolutionary algorithms approach for integrated bioenergy supply chains optimization. Energy Convers Manag 50(12):2944–2955. doi:10.1016/j.enconman.2009.07.010
Chen C-W, Fan Y (2012) Bioethanol supply chain system planning under supply and demand uncertainties. Transp Res Part E Logist Transp Rev 48(1):150–164. doi:10.1016/j.tre.2011.08.004
Čuček L, Varbanov PS, Klemeš JJ, Kravanja Z (2012) Total footprints-based multi-criteria optimisation of regional biomass energy supply chains. Energy 44(1):135–145. doi:10.1016/j.energy.2012.01.040
Fazlollahi S, Maréchal F (2013) Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP). Appl Therm Eng 50(2):1504–1513. doi:10.1016/j.applthermaleng.2011.11.035
Jackson SB, Liu XK, Cecchini M (2009) Economic consequences of firms’ depreciation method choice: evidence from capital investments. J Acc Econ 48(1):54–68. doi:10.1016/j.jacceco.2009.06.001
Judd JD, Sarin SC, Cundiff JS (2012) Design, modeling, and analysis of a feedstock logistics system. Bioresour Technol 103(1):209–218. doi:10.1016/j.biortech.2011.09.111
Keirstead J, Samsatli N, Pantaleo AM, Shah N (2012) Evaluating biomass energy strategies for a UK eco-town with an MILP optimization model. Biomass Bioenergy 39:306–316. doi:10.1016/j.biombioe.2012.01.022
Kim J, Realff MJ, Lee JH, Whittaker C, Furtner L (2011) Design of biomass processing network for biofuel production using an MILP model. Biomass Bioenergy 35(2):853–871. doi:10.1016/j.biombioe.2010.11.008
Kostin AM, Guillén-Gosálbez G, Mele FD, Bagajewicz MJ, Jiménez L (2012) Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. Chem Eng Res Design 90(3):359–376. doi:10.1016/j.cherd.2011.07.013
Kumar R, Sharma AK, Tewari PC (2015) Cost analysis of a coal-fired power plant using the NPV method. J Ind Eng Int 11(4):495–504. doi:10.1007/s40092-015-0116-8
Leão RRDCC, Hamacher S, Oliveira F (2011) Optimization of biodiesel supply chains based on small farmers: a case study in Brazil. Bioresour Technol 102(19):8958–8963. doi:10.1016/j.biortech.2011.07.002
Leduc S, Schwab D, Dotzauer E, Schmid E, Obersteiner M (2008) Optimal location of wood gasification plants for methanol production with heat recovery. Int J Energy Res 32(12):1080–1091. doi:10.1002/er.1446
Mahmoudi R, Hafezalkotob A, Makui A (2014) Source selection problem of competitive power plants under government intervention: a game theory approach. J Ind Eng Int 10(3):59. doi:10.1007/s40092-014-0059-5
Meier A, Gremaud N, Steinfeld A (2005) Economic evaluation of the industrial solar production of lime. Energy Convers Manag 46(6):905–926. doi:10.1016/j.enconman.2004.06.005
Mele FD, Guillén-Gosálbez G, Jiménez L (2009) Optimal planning of supply chains for bioethanol and sugar production with economic and environmental concerns. Comput Aided Chem Eng 26:997–1002. doi:10.1016/S1570-7946(09)70166-X
Mobini M, Sowlati T, Sokhansanj S (2011) Forest biomass supply logistics for a power plant using the discrete-event simulation approach. Appl Energy 88(4):1241–1250. doi:10.1016/j.apenergy.2010.10.016
Mousavi SH, Nazemi A, Hafezalkotob A (2014) Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators. J Ind Eng Int 11(1):59–72. doi:10.1007/s40092-014-0094-2
Pérez-Fortes M, Laínez-Aguirre JM, Arranz-Piera P, Velo E, Puigjaner L (2012) Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy 44(1):79–95. doi:10.1016/j.energy.2012.01.033
Petroleum B (2015) BP statistical review of world energy. British Petroleum, London. Available at http://www.bp.com/statisticalreview
Rentizelas AA, Tatsiopoulos IP (2010) Locating a bioenergy facility using a hybrid optimization method. Int J Prod Econ 123(1):196–209. doi:10.1016/j.ijpe.2009.08.013
Seifbarghy M, Kalani MM, Hemmati M (2015) A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain. J Ind Eng Int. doi:10.1007/s40092-015-0126-6
Tumuluru JS, Sokhansanj S, Wright CT, Boardman RD, Yancey NA (2011) A review on biomass classification and composition, co-firing issues and pretreatment methods. In: Proceedings of the American society of agricultural and biological engineers annual international meeting, pp 2053–2083. doi:10.13031/2013.37191
Velazquez-Marti B, Fernandez-Gonzalez E (2010) Mathematical algorithms to locate factories to transform biomass in bioenergy focused on logistic network construction. Renew Energy 35(9):2136–2142. doi:10.1016/j.renene.2010.02.011
Zamboni A, Bezzo F, Shah N (2009) Supply chain optimization for bioethanol production system in Northern Italy: environmentally conscious strategic design. Comput Aided Chem Eng 27:2037–2042. doi:10.1016/S1570-7946(09)70730-8
Zhang J, Osmani A, Awudu I, Gonela V (2013) An integrated optimization model for switchgrass-based bioethanol supply chain. Appl Energy 102:1205–1217. doi:10.1016/j.apenergy.2012.06.054
Zhu X, Yao Q (2011) Logistics system design for biomass-to-bioenergy industry with multiple types of feedstocks. Bioresour Technol 102(23):10936–10945. doi:10.1016/j.biortech.2011.08.121