The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem

Authors

  • Adrian Serrano-Hernandez Public University of Navarre
  • Javier Faulin Public University of Navarre
  • Javier Belloso Public University of Navarre
  • Bartosz Sawik AGH University of Science and Technology

DOI:

https://doi.org/10.7494/dmms.2017.11.1-2.19

Keywords:

biorefinery, logistics, supply chainmanagement, facility location problem, milp

Abstract

Underestimating facility location decisions may penalize business performance over the time. Those penalties usually have been studied from the economic point of view analyzing its impact on profitability. Additionally, the concern about the obtaining of sustainability is gaining importance leading to seek for renewable energy sources to reduce greenhouse gas emissions. However, little attention has been paid on 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 is developed taking into account the crop location and the biomass production seasonality to obtain an apposite location that minimizes environmental impact. The initial version of this paper was presented at ICIL 2016 Conference.

 

References

Aytug, H. and Saydam, C. (2012). Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research, 141 (3): 480–494

Bashiri, M. and 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): 717-729.

Beliën, J., De Boeck, L., Colpaert, J., Devesse, S. and Bossche, F. (2013). Optimizing the facility location design of organ transplant centers. Decision Support Systems, 54 (4): 1568-1579.

Berman, O., Krass, D. and Menezes, M. (2007). Facility reliability issues in network p-median problems: Strategic centralization and co-location effects. Operations Research, 55 (2): 332–350.

Bieniek, M. (2015). A note on the facility location problem with stochastic demands. Omega 55: 53-60.

Börjesson, M., Ahlgren, E., Lundmark, R. and Athanassiadis, D. (2014) Biofuel futures in road transport–A modeling analysis for Sweden. Transportation Research Part D: Transport and Environment, 32: 239-252.

Chatterjee, D. and Mukherjee, B. (2013). Potential Hospital Location Selection using AHP: A Study in Rural India. International Journal of Computer Applications, 71 (17): 1-17.

Cherubini, F., Jungmeier, G., Wellisch, M., Willke, C., Skiadas, I., Ree, R. and Jong, E. (2009). Toward a common classification approach for biorefinery systems. Biofuels, Bioproducts and Biorefining, 3(5): 534–546.

Daskin, M. (2014) Network and Discrete Location: Models, Algorithms, and Applications, John Wiley and Sons, Inc., New York.

Demir, E., Bektas, T. and Laporte, G. (2014) A review of recent research on green road freight transportation. European Journal of Operational Research, 237: 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, last access: January 2017).

European Environment Agency (2015) EU fuel quality monitoring — 2014. Summary report http://www.eea.europa.eu/publications/eu-fuel-quality-monitoring-2014 (Last access: January 2017)

Gutjahr, W. and Dzubur, N. (2016). Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 85: 1-22.

Juan, A., Mendez, C., Faulin, J., de Armas, J. and Grasman, S. (2016). Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges. Energies, 9(2): 1-21.

Koç, Ç., Bektaş, T., Jabali, O. and Laporte G. (2016). The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B: Methodological, 84: 81-102.

Kress, D and Pesch, E. (2012). Competitive location under proportional choice: 1-suboptimal points on networks. Decision Making in Manufacturing and Services, 6(2): 53–64

Lee, J. and Lee, Y. (2010). Tabu based heuristics for the generalized hierarchical covering location problem. Computers and Industrial Engineering, 58(4): 638–645.

Lera-López, F., Faulin, J., Sánchez, M. and 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: 423-432.

Liu, Z., Qiu, T. and 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: 221-234.

Luo, L., Voet, E. and Huppes, G. (2010) Biorefining of lignocellulosic feedstock - Technical, economic and environmental considerations. Bioresource Technology, 101 (13): 5023-5032.

Memişoğlu, G. and Üster, H. (2015). Integrated Bioenergy Supply Chain Network Planning Problem. Transportation Science, 50: 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: 1-24.

Ortiz-Astorquiza, C., Contreras, I. and Laporte, G. (2015). Multi-level facility location as the maximization of a submodular set function. European Journal of Operational Research, 247: 1013–1016.

Papendiek, F., Tartiu, V., Morone, P., Venus, J. and 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: 3643-3656.

Sakakibara, K. Tian, Y. and Nishikawa, I. (2012). An Incremental Approach for Storage and Delivery Planning Problems. Decision Making in Manufacturing and Services, 6(1): 5–23

Serrano-Hernandez, A., Faulin, J., Astiz, P., Sánchez, M. and Belloso, J. (2015). Locating and Designing a Biorefinery Supply Chain under Uncertainty in Navarre: A Stochastic Facility Location Problem Case. Transportation Research Procedia 10: 704-713

Shavandi, H. and Mahlooji, H. (2006). A fuzzy queuing model with a genetic algorithm for congested systems. Applied Mathematics and Computation, 181(1): 440–456.

Tragantalerngsak, S., Holt, J. and Rönnqvist, M. (2000). An exact method for the two-echelon, single-source, capacitated facility location problem. European Journal of Operational Research, 123(3): 473-489.

Zhao, J. and Verter, V. (2015) A bi-objective model for the used oil location-routing problem. Computers and Operations Research, 62: 157-168.

Downloads

Published

2017-12-05

How to Cite

Serrano-Hernandez, A., Faulin, J., Belloso, J., & Sawik, B. (2017). The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem. Decision Making in Manufacturing and Services, 11(1-2), 19–30. https://doi.org/10.7494/dmms.2017.11.1-2.19

Issue

Section

Articles