A mathematical model for a hybrid first/second generation of biodiesel supply chain design with limited and reliable multimodal transport

Document Type : Research Paper

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Designing a biofuel supply chain plays an important role in the reduction of biomass transportation costs. This study aims to present a comprehensive decision support tool (DST) for designing of the integrated biodiesel supply chain (BSC). In addition, so far no research has been found that examined hybrid first/second generation of biodiesel with considering all economic, environmental and social costs. In achieving this goal, we developed a new optimization model using mixed integer linear programming with the objective of maximizing the total profits of BSC incorporating environmental and social costs.  To do so, practical constraints including the limit of biomass, the capacity of technologies, the land availability, and especially limited capacity of each transportation vehicles are applied to this mathematical model. The main purpose of this study is to develop a DST to evaluate the commercial feasibility of BSC with focusing on multimodal and reliable transport.  To illustrate the capability of the proposed model, Iran is considered as a real application. The findings of this study indicate that some factors such as biomass availability, transportation reliability, and biofuel price can play as a pivotal role in this supply chain design and optimization. All in all, 31% increase in amount of produced biodiesel leads a marginal increase in environmental-related costs. 

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Main Subjects


Ageron, B., Gunasekaran, A., & Spalanzani, A. (2012). Sustainable  supply management: An empirical study. International Journal of Production Economics, 140(1), 168-182.
Akhtari, S., Sowlati, T., & Day, K. (2014). Economic feasibility of utilizing forest biomass in district energy systems–a review. Renewable and Sustainable Energy Reviews, 33, 117-127.
An, H., Wilhelm, W. E., & Searcy, S. W. (2011). Biofuel and petroleum-based fuel supply chain research: A literature review. Biomass and Bioenergy, 35(9), 3763-3774.
Botard, S., Aguilar, F., Stelzer, H., Gallagher, T., & Dwyer, J. (2015). Operational Costs and Sensitivity Analyses of an Integrated Harvest of Solid Hardwood Products and Woody Biomass: Case Study in Central Missouri (Vol. 61).
Carriquiry, M. A., Du, X., & Timilsina, G. R. (2011). Second generation biofuels: Economics and policies. Energy Policy, 39(7), 4222-4234.
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International journal of physical distribution & logistics management, 38(5), 360-387.
Castillo-Villar, K. K. (2014). Metaheuristic algorithms applied to bioenergy supply chain problems: theory, review, challenges, and future. Energies, 7(11), 7640-7672.
Ekşioğlu, S. D., Acharya, A., Leightley, L. E., & Arora, S. (2009). Analyzing the design and management of biomass-to-biorefinery supply chain. Computers & Industrial Engineering, 57(4), 1342-1352.
Ekşioğlu, S. D., Acharya, A., Leightley, L. E., & Arora, S. (2009). Analyzing the design and management of biomass-to-biorefinery supply chain. Computers & Industrial Engineering, 57(4), 1342-1352.
Eriksson, L. O., & Björheden, R. (1989). Optimal storing, transport and processing for a forest-fuel supplier. European Journal of Operational Research, 43(1), 26-33.
Ghelichi, Z., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study. Energy, 156, 661-687.
Giarola, S., Zamboni, A., & Bezzo, F. (2011). Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries. Computers & Chemical Engineering, 35(9), 1782-1797.
Gonela, V., Zhang, J., & Osmani, A. (2015). Stochastic optimization of sustainable industrial symbiosis based hybrid generation bioethanol supply chains. Computers & Industrial Engineering, 87, 40-65.
Li, Q., & Hu, G. (2014). Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification. Energy, 74, 576-584.
Light, A. R. (1976). Federalism and the Energy Crisis: A View from the States. Publius, 6(1), 81-96.
López-González, D., Fernandez-Lopez, M., Valverde, J. L., & Sanchez-Silva, L. (2014). Gasification of lignocellulosic biomass char obtained from pyrolysis: Kinetic and evolved gas analyses. Energy, 71, 456-467.
Maheshwari, P., Singla, S., & Shastri, Y. (2017). Resiliency optimization of biomass to biofuel supply chain incorporating regional biomass pre-processing depots. Biomass and Bioenergy, 97, 116-131.
Mele, F. D., Guillén-Gosálbez, G., & Jiménez, L. (2009). Optimal Planning of Supply Chains for Bioethanol and Sugar Production with Economic and Environmental Concerns. In J. Jeżowski & J. Thullie (Eds.), Computer Aided Chemical Engineering (Vol. 26, pp. 997-1002): Elsevier.
Miret, C., Chazara, P., Montastruc, L., Negny, S., & Domenech, S. (2016). Design of bioethanol green supply chain: Comparison between first and second generation biomass concerning economic, environmental and social criteria. Computers & Chemical Engineering, 85, 16-35.
Mohamed Abdul Ghani, N. M. A., Vogiatzis, C., & Szmerekovsky, J. (2018). Biomass feedstock supply chain network design with biomass conversion incentives. Energy Policy, 116, 39-49.
Nixon, J. D., Dey, P. K., Davies, P. A., Sagi, S., & Berry, R. F. (2014). Supply chain optimisation of pyrolysis plant deployment using goal programming. Energy, 68, 262-271.
ohansson, J., Liss, J.-E., Gullberg, T., & Björheden, R. (2006). Transport and handling of forest energy bundles—advantages and problems. Biomass and Bioenergy, 30(4), 334-341.
Perimenis, A., Walimwipi, H., Zinoviev, S., Müller-Langer, F., & Miertus, S. (2011). Development of a decision support tool for the assessment of biofuels. Energy Policy, 39(3), 1782-1793.
Rabbani, M., Saravi, N. A., Farrokhi-Asl, H., Lim, S. F. W., & Tahaei, Z. (2018). Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study. Journal of Cleaner Production, 200, 827-843.
R., C. C., & S., R. D. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387.
Searcy, E., Flynn, P., Ghafoori, E., & Kumar, A. (2007). The relative cost of biomass energy transport. Appl Biochem Biotechnol, 137-140(1-12), 639-652.
Timilsina, G. R., & Shrestha, A. (2011). How much hope should we have for biofuels. Energy, 36(4), 2055-2069.
Weber, A. (1929). Uber den standort der industrien (alfred weber's theory of the location of industries). University of Chicago.
Yazan, D. M., van Duren, I., Mes, M., Kersten, S., Clancy, J., & Zijm, H. (2016). Design of sustainable second-generation biomass supply chains. Biomass and Bioenergy, 94, 173-186.
You, F., & Wang, B. (2011). Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed–Centralized Processing Networks (Vol. 50).
Zhong, J., Yu, T. E., Larson, J. A., English, B. C., Fu, J. S., & Calcagno, J. (2016). Analysis of environmental and economic tradeoffs in switchgrass supply chains for biofuel production. Energy, 107, 791-803.-