The Effect of Environmental Criteria on Locating a Biorefinery : a Green Facility Location Problem
Underestimating facility location decisions may penalize business performance over the time. These penalties have usually been studied from the economic point of view, analyzing its impact on profitability. Additionally, the concern about obtaining sustainability is gaining importance, leading to a search for renewable energy sources to reduce greenhouse gas emissions. However, little attention has been paid to choosing a location considering environmental criteria. Thus, this work aims at determining a biorefinery location considering its impacts on natural resources. Therefore, a mixed integer linear programming (MILP) model has been developed, taking into account crop location and biomass production seasonality to obtain a proper location that minimizes environmental impact. The initial version of this paper was presented at the ICIL Conference in 2016.(original abstract)
- Aytug, H., Saydam, C., 2002. Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research, 141(3), pp. 480-494.
- Bashiri, M., Rezanezhad, M., 2015. A reliable multi-objective p-hub covering location problem considering of hubs capabilities. International Journal of Engineering, Transactions B: Applications, 28(5), pp. 717-729.
- Beliën, J., De Boeck, L., Colpaert, J., Devesse, S., Bossche, F., 2013. Optimizing the facility location design of organ transplant centers. Decision Support Systems, 54(4), pp. 1568- 1579.
- Berman, O., Krass, D., Menezes, M., 2007. Facility reliability issues in network p-median problems: Strategic centralization and co-location effects. Operations Research, 55(2), pp. 332-350.
- Bieniek, M., 2015. A note on the facility location problem with stochastic demands. Omega, 55, pp. 53-60.
- Börjesson, M., Ahlgren, E., Lundmark, R., Athanassiadis, D., 2014. Biofuel futures in road transport - A modeling analysis for Sweden. Transportation Research Part D: Transport and Environment, 32, pp. 239-252.
- Chatterjee, D., Mukherjee, B., 2013. Potential Hospital Location Selection using AHP: A Study in Rural India. International Journal of Computer Applications, 71(17), pp. 1-17.
- Cherubini, F., Jungmeier, G., Wellisch, M., Willke, C., Skiadas, I., Ree, R., Jong, E., 2009. Toward a common classification approach for biorefinery systems. Biofuels, Bioproducts and Biorefining, 3(5), pp. 534-546.
- Daskin, M., 2013. Network and Discrete Location: Models, Algorithms, and Applications. Second Edition, John Wiley & Sons, New York.
- Demir, E., Bektas, T., Laporte, G., 2014. A review of recent research on green road freight transportation. European Journal of Operational Research, 237, pp. 775-793.
- Department of Agriculture of Navarre, 2016. Encuesta Agraria. URL: http://www.navarra.es/home_es/Temas/Ambito+rural/Vida+rural/Observatorio+ agrario/Agricola/Informacion+estadistica/produccion+agricola.htm (in Spanish, accessed on: 12 January 2017).
- European Environment Agency, 2015. EU fuel quality monitoring - 2014. Summary report http://www.eea.europa.eu/publications/eu-fuel-quality-monitoring-2014 (accessed on: 12 January 2017)
- Gutjahr, W., Dzubur, N., 2016. Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 85, pp. 1-22.
- Harris, I., Mumford, C.L., Naim, M.M., 2014. A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling. Transportation Research Part E: Logistics and Transportation Review, 66, pp. 1-22.
- Juan, A., Mendez, C., Faulin, J., de Armas, J., Grasman, S., 2016. Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges. Energies, 9(2), pp. 1-21.
- Koç, Ç., Bektaş, T., Jabali, O., Laporte, G., 2016. The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B: Methodological, 84, pp. 81-102.
- Lee, J., Lee, Y., 2010. Tabu based heuristics for the generalized hierarchical covering location problem. Computers and Industrial Engineering, 58(4), pp. 638-645.
- Lera-López, F., Faulin, J., Sánchez, M., Serrano-Hernandez, A., 2014. Evaluating factors of the willingness to pay to mitigate the environmental effects of freight transportation crossing the Pyrenees. Transportation Research Procedia, 3, pp. 423-432.
- Liu, Z., Qiu, T., Chen, B., 2014. A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China. Applied Energy, 126, pp. 221-234.
- Luo, L., Voet, E., Huppes, G., 2010. Biorefining of lignocellulosic feedstock - Technical, economic and environmental considerations. Bioresource Technology, 101(13), pp. 5023-5032.
- Martínez, J.C.V., Fransoo, J.C., 2017. Green Facility Location. In: Bouchery, Y., Corbett, C.J., Fransoo, J.C., Tan, T. (eds.), Sustainable Supply Chains: A Research-Based Textbook on Operations and Strategy, Springer International Publishing, Cham, pp. 219-234.
- Marvin, W., Schmidt, L., Daoutidis, P., 2012. Biorefinery location and technology selection through supply chain optimization. Industrial and Engineering Chemistry Research, 52(9), pp. 3192-3208.
- Melo, M.T., Nickel, S., Saldanha-Da-Gama, F., 2009. Facility location and supply chain management - A review. European Journal of Operational Research, 196(2), pp. 401-412.
- Memişoğlu, G., Üster, H., 2015. Integrated Bioenergy Supply Chain Network Planning Problem. Transportation Science, 50, pp. 35-36.
- Montoya, A., Vélez-Gallego, M., Villegas, J., 2016. Multi-product capacitated facility location problem with general production and building costs. NETNOMICS: Economic Research and Electronic Networking, 1, pp. 1-24.
- Ortiz-Astorquiza, C., Contreras, I., Laporte, G., 2015. Multi-level facility location as the maximization of a submodular set function. European Journal of Operational Research, 247, pp. 1013-1016.
- Papendiek, F., Tartiu, V., Morone, P., Venus, J., Hönig, A., 2016. Assessing the economic profitability of fodder legume production for Green Biorefineries: A cost-benefit analysis to evaluate farmers profitability. Journal of Cleaner Production, 112, pp. 3643-3656.
- Sakakibara, K., Tian, Y., Nishikawa, I., 2012. An Incremental Approach for Storage and Delivery Planning Problems. Decision Making in Manufacturing and Services, 6(1), pp. 5-23.
- Serrano-Hernandez, A., Faulin, J., Astiz, P., Sánchez, M., Belloso, J., 2015. Locating and Designing a Biorefinery Supply Chain under Uncertainty in Navarre: A Stochastic Facility Location Problem Case. Transportation Research Procedia, 10, pp. 704-713.
- Shavandi, H., Mahlooji, H., 2006. A fuzzy queuing model with a genetic algorithm for congested systems. Applied Mathematics and Computation, 181(1), pp. 440-456.
- You, F., Tao, L., Graziano, D., Snyder, S., 2012. Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input-output analysis. American Institute of Chemical Engineers Journal, 58, pp. 1157-1180.
- Yu, E., Lixia, H., English, B., Larson, J., 2014. GIS-based optimization for advanced biofuels supply chains: A case study in Tennessee, Lecture Notes in Management Science, 6, pp. 217-227.
- Zanjirani, R., Hekmatfar, M., 2009. Facility Location Concepts, Models, Algorithms and Case Studies, Physica Verlag.
- Zhao, J., Verter, V., 2015. A bi-objective model for the used oil location-routing problem. Computers and Operations Research, 62, pp. 157-168.