A Bi-Objective Vehicle-Routing Problem for Optimization of a Bioenergy Supply Chain by Using NSGA-II Algorithm

Document Type : 15th IIEC conference selected papers

Authors

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

2 School of industrial engineering, College of Engineering, University of Tehran, Tehran, Iran

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

10.22070/jqepo.2020.3650.1079

Abstract

In this paper, we develop a multi-period mathematical model involving economic and environmental considerations. A vehicle-routing problem is considered an essential matter due to decreasing the routing cost, especially in the concerned bioenergy supply chain. A few of the optimization model recognized the vehicle routing to design the bioenergy supply chain. In this study, a bi-objective mixed-integer linear programming (MILP) model is presented.  The economic objective function minimizes the transportation, capacity expanding, fixed and variable costs, and the locating routing cost in this problem. The proposed bi-objective model is solved through a Non-Dominated Genetic Algorithm (NSGA- II).  Furthermore, the small-sized problem is solved by the CPLEX solver and augmented ɛ-constraint method.

Keywords

References

Ba, B. H., Prins, C.,  Prodhon, C.,2016. Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective. Renewable Energy, 87, 977-989.
Babazadeh, R., Razmi, J., Pishvaee, M. S.,  Rabbani, M. ,2017. A sustainable second-generation biodiesel supply chain network design problem under risk. Omega, 66, 258-277.
Babazadeh, R., Razmi, J., Rabbani, M.,  Pishvaee, M. S. ,2015. An integrated data envelopment analysis–mathematical programming approach to strategic biodiesel supply chain network design problem. Journal of Cleaner Production.
Cobuloglu, H. I., & Büyüktahtakın, İ. E. (2014). A mixed-integer optimization model for the economic and environmental analysis of biomass production. Biomass and Bioenergy, 67, 8-23.
Claudia, Cambero, et al. "Strategic optimization of forest residues to bioenergy and biofuel supply chain." International Journal of Energy Research 39.4 (2015): 439-452.
Cambero, C., Sowlati, T.,  Pavel, M. ,2016. Economic and life cycle environmental optimization of forest-based biorefinery supply chains for bioenergy and biofuel production. Chemical Engineering Research and Design, 107, 218-235.
Chambost, V.,  Stuart, P. R. ,2007. Selecting the most appropriate products for the forest biorefinery. Industrial Biotechnology, 3(2), 112-119.
Čuček, L., Klemeš, J. J., Varbanov, P. S.,  Kravanja, Z. ,2011. Life cycle assessment and multi-criteria optimization of regional biomass and bioenergy supply chains. Chemical Engineering Transactions, 25, 575-580.
Ćosić, B., Stanić, Z., Duić, N. ,2011. Geographic distribution of economic potential of agricultural and forest biomass residual for energy use: case study Croatia. Energy, 36(4), 2017-2028.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 2002. 6(2): p. 182-197.
Heydari, A.,  Askarzadeh, A. ,2016. Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept. Applied Energy, 165, 601-611.
Iakovou, E., Karagiannidis, A., Vlachos, D., Toka, A.,  Malamakis, A. ,2010. Waste biomass-to-energy supply chain management: a critical synthesis. Waste management, 30(10), 1860-1870.
Keeling, C. D. ,1973. Industrial production of carbon dioxide from fossil fuels and limestone. Tellus, 25(2), 174-198.
Marufuzzaman, M., Eksioglu, S. D.,  Huang, Y. E. ,2014. Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment. Computers & Operations Research, 49, 1-17.
Ochoa, P., & Van Ackere, A. (2009). Policy changes and the dynamics of capacity expansion in the Swiss electricity market. Energy policy, 37(5), 1983-1998.
Paolotti L., G Martino, A Marchini, R Pascolini, A Boggia (2015).Economic and environmental evaluation of transporting imported pellet: A case study. Biomass and Bioenergy, 83, 340-353.
Paulo, H., Azcue, X., Barbosa-Póvoa, A. P., Relvas, S. ,2015. Supply chain optimization of residual forestry biomass for bioenergy production: the case study of Portugal. Biomass and Bioenergy, 83, 245-256.
Perl, J. and Daskin, M.S., A warehouse location-routing problem. Transportation Research Part B: Methodological, 1985, 19(5), 381-396.
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.
Rabbani, M., Momen, S., Akbarian-Saravi, N., Farrokhi-Asl, H., & Ghelichi, Z. (2020). Optimal design for sustainable bioethanol supply chain considering the bioethanol production strategies: A case study. Computers & Chemical Engineering, 134, 106720.
Roghanian, E. and Pazhoheshfar, P.,  An optimization model for reverse logistics network under stochastic environment by using genetic algorithm. Journal of Manufacturing Systems, 2014. 33(3): p. 348-356.
Santibañez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., El-Halwagi, M. M. ,2011. Optimal planning of a biomass conversion system considering economic and environmental aspects. Industrial & Engineering Chemistry Research, 50(14), 8558-8570.
Woo, Y.-b., Cho, S., Kim, J., & Kim, B. S. (2016). Optimization-based approach for strategic design and operation of a biomass-to-hydrogen supply chain. International Journal of Hydrogen Energy, 41(12), 5405-5418.
Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: overview, key issues and challenges. Computers & Chemical Engineering, 66, 36-56.
Zhu, X., Li, X., Yao, Q., Chen, Y. ,2011. Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry. Bioresource technology, 102(2), 1344-1351.
Journal of Quality Engineering and Production Optimization
Volume 5, Issue 1
June 2020
Pages 87-102

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