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Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level

Year 2022, Volume: 5 Issue: 1, 282 - 296, 20.06.2022
https://doi.org/10.35341/afet.1071343

Abstract

The importance of disaster logistics and its share in the logistics sector are increasing significantly. Most disasters are difficult to predict; therefore, a set of measures seems to be necessary to reduce the risks. Thus, disaster logistics needs to be designed with the pre-disaster and post-disaster measures. These disasters are experienced intensely in Turkey and the importance of these measures becomes more evidential. Therefore, accurate models are required to develop an effective disaster preparedness system. One of the most important decisions to increase the preparedness is to locate the centres for handling material inventory. In this context, this paper analyses the response phase designing the disaster distribution centres in Turkey at the provincial level. AHP (Analytical Hierarchy Process) based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and goal programming model integration is used to decide alternative locations of distribution centres. TOPSIS method is employed for ranking the locations, which is based on hazard scores, total area, population, and distance to centre. Two conflicting objectives are first proposed in the goal programming formulation, in which maximization of the TOPSIS scores and minimization of the number of distribution centres covering all demands named set covering model are included. Although Gecimli has the highest priority with 0.8 p score in the TOPSIS ranking, Altincevre (0.77) and Buzlupınar (0.75) ensure both the TOPSIS score and coverage of the demand nodes. The results from this paper confirm that the computational results ensure disaster prevention insights especially in regions with limited data.

References

  • Ağdaş, M., Özkan, B., & Balli, H. (2014). Afet lojistiği kapsamında dağıtım merkezi için yer seçimi: smaa-2 tekniği ile bir uygulama, Beykoz Akademi Dergisi, 2(1): 75-94.
  • Ahmadi Choukolaei, H., Jahangoshai Rezaee, M., Ghasemi, P., & Saberi, M. (2021). Efficient crisis management by selection and analysis of relief centres in disaster integrating GIS and multicriteria decision methods: A case study of Tehran, Mathematical Problems in Engineering, 2021. DOI: https://doi.org/10.1155/2021/5944828
  • Ai, T. J., & Wigati, S. S. (2017). Model for determining logistic distribution centre: case study of Mount Merapi eruption disaster, In IOP Conference Series: Materials Science and Engineering, 166 (1), 012033. DOI: 10.1088/1757-899X/166/1/012033
  • Ali, A. S., Abolfazl, M., & Mohammad, E. (2020). Operational site selection for disaster management bases in Tehran, Iran, MAUSAM, 71(3), 431-442. DOI: https://doi.org/10.54302/mausam.v71i3.42
  • Balcik, B., & Beamon, B. (2008). Facility location in humanitarian relief, International Journal of Logistics: Research and Applications, 11(2), 101-121. https://doi.org/10.1080/13675560701561789
  • Campbell, A.M., & Jones, P.C. (2011). Prepositioning Supplies in Preparation for Disasters, European Journal of Operational Research, 209(2), 156-165. DOI: https://doi.org/10.1016/j.ejor.2010.08.029
  • Dal, M., Öcal, A. D., & Göktepe, D. (2017). Natural disaster of Tunceli province and its environment, In Proceedings of 4th International Regional Development Conference, Tunceli, 601-607.
  • Derse, O., & Göçmen, E. (2019), Transportation mode choice using fault tree analysis and mathematical modeling approach, Journal of Transportation Safety & Security, pp.1-19. DOI: https://doi.org/10.1080/19439962.2019.1665600
  • Derse, O., Göçmen, E., Yılmaz, E., & Erol, R. (2020). A mathematical programming model for facility location optimization of hydrogen production from renewable energy sources, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp.1-12. DOI: https://doi.org/10.1080/15567036.2020.1812769 Derse, O. (2021). A new approach to the fine kinney method with ahp based electre i and math model on risk assessment for natural disasters. Journal of Geography, (42), 42.
  • Duran, S., Gutierrez, M.A., & Keskinocak, P. (2011). Pre-positioning on emergency items worldwide for CARE International, Interfaces, 41(3), 223-237.
  • Bello-Garduño, M., Sánchez-Partida, D., Martínez-Flores, J. L., & Caballero-Morales, S. O. (2021). Selection of Humanitarian Response Distribution Centres (HRDC) in Puebla, Mexico, In Disaster Risk Reduction in Mexico (pp. 81-98). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-67295-9_4.
  • Ergün, M., Korucuk, S., & Memiş, S. (2020). Selection of ideal disaster warehouse location for sustainable disaster logistics: the example of giresun province. Çanakkale Onsekiz Mart University Journal of Science Institute, 6 (1), 144-165.
  • Göçmen, E., & Kuvvetli, Y. (2020). Humanitarian Logistics Management After a Disaster: An Earthquake Case, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(2), 679-688.
  • Gutjahr, W. J., & Dzubur, N. (2016). Bi-objective bilevel optimization of distribution centre locations considering user equilibria, Transportation Research Part E: Logistics and Transportation Review, 85, 1-22. DOI: https://doi.org/10.1016/j.tre.2015.11.001
  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014). The Ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management, International Journal of Production Research, 52(7), 2154-2172. DOI: https://doi.org/10.1080/00207543.2013.858836
  • Izadi, M., & Samouei, P. (2021). Distribution centre location and vehicle routing in the disaster condition using two stage programming, Journal of Emergency Management.
  • Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns, Computers & Industrial Engineering, 148,106734. DOI: https://doi.org/10.1016/j.cie.2020.106734
  • Onat, O., & Yön, B. (2018). Earthquake risk amplification based on architectural plan irregularity, Proceedings of 2nd International Symposium on Natural Hazards and Disaster Management, Sakarya (pp. 665–674).
  • Ozen, M., & Krishnamurthy, A. (2021). G-network models to support planning for disaster relief distribution”, International Journal of Production Research, pp. 1-12. https://doi.org/10.1080/00207543.2020.1867920.
  • Özkan, B., Süleyman, M., Çelik, E., & Özceylan, E. (2019). GIS-based maximum covering location model in times of disasters: The case of Tunceli, Beykoz Akademi Dergisi, pp.100-111.
  • Peker, İ., Korucuk, S., Ulutaş, Ş., OKATAN, B. S., & Yaşar, F. (2016). Afet lojistiği kapsamında en uygun dağıtım merkez yerinin AHS-VIKOR bütünleşik yöntemi ile belirlenmesi: Erzincan ili örneği. Yönetim Ve Ekonomi Araştırmaları Dergisi, 14(1), 82-103.
  • Ren, L., Zhang, Y., Wang, Y., and Sun, Z. (2007). Comparative analysis of a novel M-TOPSIS method and TOPSIS, Applied Mathematics Research eXpress, 2007.
  • Roh, S., Hyun-mi, J., & Chul-hwan H. (2013). Warehouse location decision factors in humanitarian relief logistics, The Asian Journal of Shipping and Logistics, 29(1),103-120. DOI: https://doi.org/10.1016/j.ajsl.2013.05.006
  • Saeidian, B., Mesgari, M. S., Pradhan, B., & Ghodousi, M. (2018). Optimized location-allocation of earthquake relief centres using PSO and ACO, complemented by GIS, clustering, and TOPSIS, ISPRS International Journal of Geo-Information, 7(8), 292. DOI: https://doi.org/10.3390/ijgi7080292
  • Sánchez-Lozano, J. M., Teruel-Solano, J., Soto-Elvira, P. L., & García-Cascales, M. S. (2013). Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain, Renewable and sustainable energy reviews, 24, pp. 544-556. DOI: https://doi.org/10.1016/j.rser.2013.03.019
  • Schempp, T., Zhang, H., Schmidt, A., Hong, M., & Akerkar, R. (2019). A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization, International Journal of Disaster Risk Reduction, 39, 101143. DOI: https://doi.org/10.1016/j.ijdrr.2019.101143
  • Stienen, V. F., Wagenaar, J. C., den Hertog, D., & Fleuren, H. A. (2021). Optimal depot locations for humanitarian logistics service providers using robust optimization, Omega, 104, 102494. DOI: https://doi.org/10.1016/j.omega.2021.102494
  • Timperio, G., Panchal, G. B., Samvedi, A., Goh, M., & De Souza, R. (2017). Decision support framework for location selection and disaster relief network design, Journal of Humanitarian Logistics and Supply Chain Management. DOI: https://doi.org/10.1108/JHLSCM-11-2016-0040
  • Turğut, B. T., Taş, G., Herekoğlu, A., Tozan, H. & Vayvay, O. (2011). A fuzzy AHP based decision support system for disaster centre location selection and a case study for Istanbul, Disaster Prevention and Management: An International Journal. DOI: https://doi.org/10.1108/09653561111178943
  • Yanilmaz, S., Baskak, D., Yucesan, M., & Gul, M. (2021). Extension of FEMA and SMUG models with Bayesian best-worst method for disaster risk reduction, International Journal of Disaster Risk Reduction, 102631. https://doi.org/10.1016/j.ijdrr.2021.102631
  • Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations, Transportation Research Part E: Logistics and Transportation Review, 43(6), 660-672.
  • Yilmaz, H., & Kabak, Ö. (2016). A multiple objective mathematical program to determine locations of disaster response distribution centres, IFAC-PapersOnLine, 49(12),520-525. DOI: https://doi.org/10.1016/j.ifacol.2016.07.682
  • Yılmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centres in humanitarian logistics using type-2 fuzzy MCDM approach, Journal of Enterprise Information Management. DOI: https://doi.org/10.1108/JEIM-09-2019-0310
  • Widener, M. J., & Horner, M. W. (2011). A hierarchical approach to modeling hurricane disaster relief goods distribution”, Journal of Transport Geography, 19(4),821-828. DOI: https://doi.org/10.1016/j.jtrangeo.2010.10.006
  • Zhong, S., Cheng, R., Jiang, Y., Wang, Z., Larsen, A., and Nielsen, O. A. (2020). Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand, Transportation Research Part E: Logistics and Transportation Review, 141, 102015. DOI: https://doi.org/10.1016/j.tre.2020.102015

Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level

Year 2022, Volume: 5 Issue: 1, 282 - 296, 20.06.2022
https://doi.org/10.35341/afet.1071343

Abstract

The importance of disaster logistics and its share in the logistics sector are increasing significantly. Most disasters are difficult to predict; therefore, a set of measures seems to be necessary to reduce the risks. Thus, disaster logistics needs to be designed with the pre-disaster and post-disaster measures. These disasters are experienced intensely in Turkey and the importance of these measures becomes more evidential. Therefore, accurate models are required to develop an effective disaster preparedness system. One of the most important decisions to increase the preparedness is to locate the centres for handling material inventory. In this context, this paper analyses the response phase designing the disaster distribution centres in Turkey at the provincial level. AHP (Analytical Hierarchy Process) based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and goal programming model integration is used to decide alternative locations of distribution centres. TOPSIS method is employed for ranking the locations, which is based on hazard scores, total area, population, and distance to centre. Two conflicting objectives are first proposed in the goal programming formulation, in which maximization of the TOPSIS scores and minimization of the number of distribution centres covering all demands named set covering model are included. Although Gecimli has the highest priority with 0.8 p score in the TOPSIS ranking, Altincevre (0.77) and Buzlupınar (0.75) ensure both the TOPSIS score and coverage of the demand nodes. The results from this paper confirm that the computational results ensure disaster prevention insights especially in regions with limited data.

References

  • Ağdaş, M., Özkan, B., & Balli, H. (2014). Afet lojistiği kapsamında dağıtım merkezi için yer seçimi: smaa-2 tekniği ile bir uygulama, Beykoz Akademi Dergisi, 2(1): 75-94.
  • Ahmadi Choukolaei, H., Jahangoshai Rezaee, M., Ghasemi, P., & Saberi, M. (2021). Efficient crisis management by selection and analysis of relief centres in disaster integrating GIS and multicriteria decision methods: A case study of Tehran, Mathematical Problems in Engineering, 2021. DOI: https://doi.org/10.1155/2021/5944828
  • Ai, T. J., & Wigati, S. S. (2017). Model for determining logistic distribution centre: case study of Mount Merapi eruption disaster, In IOP Conference Series: Materials Science and Engineering, 166 (1), 012033. DOI: 10.1088/1757-899X/166/1/012033
  • Ali, A. S., Abolfazl, M., & Mohammad, E. (2020). Operational site selection for disaster management bases in Tehran, Iran, MAUSAM, 71(3), 431-442. DOI: https://doi.org/10.54302/mausam.v71i3.42
  • Balcik, B., & Beamon, B. (2008). Facility location in humanitarian relief, International Journal of Logistics: Research and Applications, 11(2), 101-121. https://doi.org/10.1080/13675560701561789
  • Campbell, A.M., & Jones, P.C. (2011). Prepositioning Supplies in Preparation for Disasters, European Journal of Operational Research, 209(2), 156-165. DOI: https://doi.org/10.1016/j.ejor.2010.08.029
  • Dal, M., Öcal, A. D., & Göktepe, D. (2017). Natural disaster of Tunceli province and its environment, In Proceedings of 4th International Regional Development Conference, Tunceli, 601-607.
  • Derse, O., & Göçmen, E. (2019), Transportation mode choice using fault tree analysis and mathematical modeling approach, Journal of Transportation Safety & Security, pp.1-19. DOI: https://doi.org/10.1080/19439962.2019.1665600
  • Derse, O., Göçmen, E., Yılmaz, E., & Erol, R. (2020). A mathematical programming model for facility location optimization of hydrogen production from renewable energy sources, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp.1-12. DOI: https://doi.org/10.1080/15567036.2020.1812769 Derse, O. (2021). A new approach to the fine kinney method with ahp based electre i and math model on risk assessment for natural disasters. Journal of Geography, (42), 42.
  • Duran, S., Gutierrez, M.A., & Keskinocak, P. (2011). Pre-positioning on emergency items worldwide for CARE International, Interfaces, 41(3), 223-237.
  • Bello-Garduño, M., Sánchez-Partida, D., Martínez-Flores, J. L., & Caballero-Morales, S. O. (2021). Selection of Humanitarian Response Distribution Centres (HRDC) in Puebla, Mexico, In Disaster Risk Reduction in Mexico (pp. 81-98). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-67295-9_4.
  • Ergün, M., Korucuk, S., & Memiş, S. (2020). Selection of ideal disaster warehouse location for sustainable disaster logistics: the example of giresun province. Çanakkale Onsekiz Mart University Journal of Science Institute, 6 (1), 144-165.
  • Göçmen, E., & Kuvvetli, Y. (2020). Humanitarian Logistics Management After a Disaster: An Earthquake Case, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(2), 679-688.
  • Gutjahr, W. J., & Dzubur, N. (2016). Bi-objective bilevel optimization of distribution centre locations considering user equilibria, Transportation Research Part E: Logistics and Transportation Review, 85, 1-22. DOI: https://doi.org/10.1016/j.tre.2015.11.001
  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014). The Ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management, International Journal of Production Research, 52(7), 2154-2172. DOI: https://doi.org/10.1080/00207543.2013.858836
  • Izadi, M., & Samouei, P. (2021). Distribution centre location and vehicle routing in the disaster condition using two stage programming, Journal of Emergency Management.
  • Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns, Computers & Industrial Engineering, 148,106734. DOI: https://doi.org/10.1016/j.cie.2020.106734
  • Onat, O., & Yön, B. (2018). Earthquake risk amplification based on architectural plan irregularity, Proceedings of 2nd International Symposium on Natural Hazards and Disaster Management, Sakarya (pp. 665–674).
  • Ozen, M., & Krishnamurthy, A. (2021). G-network models to support planning for disaster relief distribution”, International Journal of Production Research, pp. 1-12. https://doi.org/10.1080/00207543.2020.1867920.
  • Özkan, B., Süleyman, M., Çelik, E., & Özceylan, E. (2019). GIS-based maximum covering location model in times of disasters: The case of Tunceli, Beykoz Akademi Dergisi, pp.100-111.
  • Peker, İ., Korucuk, S., Ulutaş, Ş., OKATAN, B. S., & Yaşar, F. (2016). Afet lojistiği kapsamında en uygun dağıtım merkez yerinin AHS-VIKOR bütünleşik yöntemi ile belirlenmesi: Erzincan ili örneği. Yönetim Ve Ekonomi Araştırmaları Dergisi, 14(1), 82-103.
  • Ren, L., Zhang, Y., Wang, Y., and Sun, Z. (2007). Comparative analysis of a novel M-TOPSIS method and TOPSIS, Applied Mathematics Research eXpress, 2007.
  • Roh, S., Hyun-mi, J., & Chul-hwan H. (2013). Warehouse location decision factors in humanitarian relief logistics, The Asian Journal of Shipping and Logistics, 29(1),103-120. DOI: https://doi.org/10.1016/j.ajsl.2013.05.006
  • Saeidian, B., Mesgari, M. S., Pradhan, B., & Ghodousi, M. (2018). Optimized location-allocation of earthquake relief centres using PSO and ACO, complemented by GIS, clustering, and TOPSIS, ISPRS International Journal of Geo-Information, 7(8), 292. DOI: https://doi.org/10.3390/ijgi7080292
  • Sánchez-Lozano, J. M., Teruel-Solano, J., Soto-Elvira, P. L., & García-Cascales, M. S. (2013). Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain, Renewable and sustainable energy reviews, 24, pp. 544-556. DOI: https://doi.org/10.1016/j.rser.2013.03.019
  • Schempp, T., Zhang, H., Schmidt, A., Hong, M., & Akerkar, R. (2019). A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization, International Journal of Disaster Risk Reduction, 39, 101143. DOI: https://doi.org/10.1016/j.ijdrr.2019.101143
  • Stienen, V. F., Wagenaar, J. C., den Hertog, D., & Fleuren, H. A. (2021). Optimal depot locations for humanitarian logistics service providers using robust optimization, Omega, 104, 102494. DOI: https://doi.org/10.1016/j.omega.2021.102494
  • Timperio, G., Panchal, G. B., Samvedi, A., Goh, M., & De Souza, R. (2017). Decision support framework for location selection and disaster relief network design, Journal of Humanitarian Logistics and Supply Chain Management. DOI: https://doi.org/10.1108/JHLSCM-11-2016-0040
  • Turğut, B. T., Taş, G., Herekoğlu, A., Tozan, H. & Vayvay, O. (2011). A fuzzy AHP based decision support system for disaster centre location selection and a case study for Istanbul, Disaster Prevention and Management: An International Journal. DOI: https://doi.org/10.1108/09653561111178943
  • Yanilmaz, S., Baskak, D., Yucesan, M., & Gul, M. (2021). Extension of FEMA and SMUG models with Bayesian best-worst method for disaster risk reduction, International Journal of Disaster Risk Reduction, 102631. https://doi.org/10.1016/j.ijdrr.2021.102631
  • Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations, Transportation Research Part E: Logistics and Transportation Review, 43(6), 660-672.
  • Yilmaz, H., & Kabak, Ö. (2016). A multiple objective mathematical program to determine locations of disaster response distribution centres, IFAC-PapersOnLine, 49(12),520-525. DOI: https://doi.org/10.1016/j.ifacol.2016.07.682
  • Yılmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centres in humanitarian logistics using type-2 fuzzy MCDM approach, Journal of Enterprise Information Management. DOI: https://doi.org/10.1108/JEIM-09-2019-0310
  • Widener, M. J., & Horner, M. W. (2011). A hierarchical approach to modeling hurricane disaster relief goods distribution”, Journal of Transport Geography, 19(4),821-828. DOI: https://doi.org/10.1016/j.jtrangeo.2010.10.006
  • Zhong, S., Cheng, R., Jiang, Y., Wang, Z., Larsen, A., and Nielsen, O. A. (2020). Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand, Transportation Research Part E: Logistics and Transportation Review, 141, 102015. DOI: https://doi.org/10.1016/j.tre.2020.102015
There are 35 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Elifcan Göçmen Polat 0000-0002-0316-281X

Publication Date June 20, 2022
Acceptance Date June 15, 2022
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

APA Göçmen Polat, E. (2022). Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level. Afet Ve Risk Dergisi, 5(1), 282-296. https://doi.org/10.35341/afet.1071343
AMA Göçmen Polat E. Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level. Afet ve Risk Dergisi. June 2022;5(1):282-296. doi:10.35341/afet.1071343
Chicago Göçmen Polat, Elifcan. “Distribution Centre Location Selection for Disaster Logistics With Integrated Goal Programming-AHP Based TOPSIS Method at the City Level”. Afet Ve Risk Dergisi 5, no. 1 (June 2022): 282-96. https://doi.org/10.35341/afet.1071343.
EndNote Göçmen Polat E (June 1, 2022) Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level. Afet ve Risk Dergisi 5 1 282–296.
IEEE E. Göçmen Polat, “Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level”, Afet ve Risk Dergisi, vol. 5, no. 1, pp. 282–296, 2022, doi: 10.35341/afet.1071343.
ISNAD Göçmen Polat, Elifcan. “Distribution Centre Location Selection for Disaster Logistics With Integrated Goal Programming-AHP Based TOPSIS Method at the City Level”. Afet ve Risk Dergisi 5/1 (June 2022), 282-296. https://doi.org/10.35341/afet.1071343.
JAMA Göçmen Polat E. Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level. Afet ve Risk Dergisi. 2022;5:282–296.
MLA Göçmen Polat, Elifcan. “Distribution Centre Location Selection for Disaster Logistics With Integrated Goal Programming-AHP Based TOPSIS Method at the City Level”. Afet Ve Risk Dergisi, vol. 5, no. 1, 2022, pp. 282-96, doi:10.35341/afet.1071343.
Vancouver Göçmen Polat E. Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level. Afet ve Risk Dergisi. 2022;5(1):282-96.