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Tehlikeli Madde Taşımacılığı Güzergâh Seçimi Problemi İçin Stokastik Bir Risk Analizi

Year 2018, Volume: 6 Issue: 6, 935 - 943, 02.12.2018

Abstract

Tehlikeli madde
taşımacılığı günümüzde can ve mal güvenliğinin sağlanması ve faaliyetlerin
aksamadan yürütülebilmesi için üzerinde önemle durulması gereken konulardan
biridir. Artan sanayileşme düzeyi ile birlikte daha fazla kullanılmaya başlanan
tehlikeli maddelerin bir noktadan diğer bir noktaya güvenle taşınması daha
önemli bir hâl almıştır. Tehlikeli maddelerin taşınmasında her ne kadar tüm
taşıma modları etkin olarak kullanılsa da en çok karayolu kullanılmaktadır. Bu
yüzden, tehlikeli maddelerin içerdiği risk göz önüne alınarak, daha titiz bir
taşıma ve dolayısıyla daha etkin bir risk yönetimi gerekmektedir. Bu çalışmada
tehlikeli madde taşımacılığının maliyet etkin, güvenli ve kesintiye uğramadan
gerçekleştirilebilmesi için en uygun güzergâhın belirlenmesi amaçlanmıştır. Bu
çerçevede Gaziantep ile Erzurum illeri arasında tehlikeli madde taşımacılığı yapan
bir firmanın yetkilileriyle yüz yüze görüşmeler gerçekleştirilerek SMAA-2
yöntemiyle tehlikeli madde taşımacılığı güzergâh seçimi yapılmıştır.

References

  • Akçetin, E. (2013). Tehlikeli Madde Lojistiğinde Risk Yönetimi (1.baskı). İstanbul: Nobel Yayın Dağıtım.
  • Alumur, S., & Kara, B. Y. (2007). A new model for the hazardous waste location-routing problem. Computers & Operations Research, 34(5), 1406-1423.
  • Bahramian, Z., & Bagheri, G. (2017). An approach for road, railway, pipeline routing problem in hazardous materials transportation using multiple criteria. 4th International Conference on Transportation Information and Safety (ICTIS), (pp. 1088-1092), August 8-10, 2017, Banff, Canada.
  • Bell, M.G.H. (2007). Mixed routing strategies for hazardous materials: decision-making under complete uncertainty, International Journal of Sustainable Transportation, 1(2), 133-142.
  • BMAEK, Birleşmiş Milletler Avrupa Ekonomik Komisyonu, (2007). Tehlikeli Malların Karayolu İle Taşınmasına İlişkin Avrupa Anlaşması (ADR), Cenevre.
  • Bonvicini, S., & Spadoni, G. (2008). A hazmat multi-commodity routing model satisfying risk criteria: A case study. Journal of Loss Prevention in the Process Industries, 21(4), 345-358.
  • Bronfman, A., Marianov, V., Paredes-Belmar, G., & Lüer-Villagra, A. (2015). The maximin HAZMAT routing problem. European Journal of Operational Research, 241(1), 15-27.
  • Bula, G. A., Prodhon, C., Gonzalez, F. A., Afsar, H. M., & Velasco, N. (2017). Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. Journal of Hazardous Materials, 324, 472-480.
  • Caramia, M., Giordani, S., & Iovanella, A. (2009). On the selection of k routes in multiobjective hazmat route planning. IMA Journal of Management Mathematics, 21(3), 239-251.
  • Carotenuto, P., Giordani, S., Ricciardelli, S., & Rismondo, S. (2007). A tabu search approach for scheduling hazmat shipments. Computers & Operations Research, 34(5), 1328-1350.
  • Chakrabarti, U. K., & Parikh, J. K. (2013). Risk-based route evaluation against country-specific criteria of risk tolerability for hazmat transportation through Indian State Highways. Journal of Loss Prevention in the Process Industries, 26(4), 723-736.
  • Dadkar, Y., Jones, D., & Nozick, L. (2008). Identifying geographically diverse routes for the transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 44(3), 333-349.
  • Dadkar, Y., Nozick, L., & Jones, D. (2010). Optimizing facility use restrictions for the movement of hazardous materials. Transportation Research Part B: Methodological, 44(2), 267-281.
  • Daimonlogistics. (2017). Tehlikeli Madde Lojistiği. (Erişim: 02.01 2018), http://daimonlogistics.com/26-tehlikeli-madde-tasimaciligi/.
  • Das, A., Mazumder, T. N., & Gupta, A. K. (2012). Pareto frontier analyses based decision making tool for transportation of hazardous waste. Journal of Hazardous Materials, 227, 341-352.
  • Demirdöğen, O., Erdal, H., Yazıcılar, F.G. & Aykol, S. (2017). Disaster Logistics Facility Location Problem: An Application For TRA1 Region, The International New Issues in Social Sciences, 5(5), 323-342.
  • Desai, S., & Lim, G. J. (2013). Solution time reduction techniques of a stochastic dynamic programming approach for hazardous material route selection problem. Computers & Industrial Engineering, 65(4), 634-645.
  • Erkut, E., & Alp, O. (2007). Integrated Routing and Scheduling of Hazmat Trucks with Stops En Route. Transportation Science, 41(1), 107-122.
  • Erkut, E., Tjandra, S. A., & Verter, V. (2007). Hazardous materials transportation. Handbooks In Operations Research And Management Science, 14, 539-621.
  • Faghih-Roohi, S., Ong, Y. S., Asian, S., & Zhang, A. N. (2016). Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks. Annals of Operations Research, 247(2), 715-734.
  • Garrido, R. A., & Bronfman, A. C. (2016). Equity and social acceptability in multiple hazardous materials routing through urban areas. Transportation Research Part A: Policy and Practice, 102, 244-260.
  • Glickman, T. S., Erkut, E., & Zschocke, M. S. (2007). The cost and risk impacts of rerouting railroad shipments of hazardous materials. Accident Analysis & Prevention, 39(5), 1015-1025.
  • Hosseini, S. D., & Verma, M. (2017). A Value-at-Risk (VAR) approach to routing rail hazmat shipments. Transportation Research Part D: Transport and Environment, 54, 191-211.
  • Huang, B. (2006). GIS-based route planning for hazardous material transportation. Journal of Environmental Informatics, 8(1), 49-57.
  • Karabulut, S., & Öcalır-Akünal, E. V. (2015). Karayolu ile tehlikeli madde taşımacılığı için coğrafi bilgi sistemi destekli çevresel risk analizi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(3), 351-359.
  • Kang, Y., Batta, R., & Kwon, C. (2014). Generalized route planning model for hazardous material transportation with VaR and equity considerations. Computers & Operations Research, 43, 237-247.
  • KGM, Karayolları Genel Müdürlüğü. (2010). 2010 Trafik ve Ulaşım Bilgileri. (Erişim: 17.09.2017),http://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Istatistikler/TrafikveUlaşımBilgileri/10TrafikUlasimBilgileri%20.pdf.
  • Lahdelma R., Hokkanen J., & Salminen P. (1998). SMAA-stochastic multiobjective acceptability analysis. European Journal of Operational Research, 106, 137-143.
  • Lahdelma, R., & Salminen, P. (2001). SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49(3), 444-454.
  • Li, R., & Leung, Y. (2011). Multi-objective route planning for dangerous goods using compromise programming. Journal of Geographical Systems, 13(3), 249-271.
  • Mahmoudabadi, A., & Seyedhosseini, S. M. (2014). Developing a chaotic pattern of dynamic Hazmat routing problem. IATSS Research, 37(2), 110-118.
  • OSHA, Occupational Safety and Health Administration. (2017). (Erişim:14.08.2017), http://www.osha.gov.
  • MEB, Millî Eğitim Bakanlığı. (2011). Tehlikeli Madde Taşımacılığı Modülü, Ankara.
  • Murat, Ş., & Kulak, O. (2005). Ulaşım ağlarında bilgi aksiyomu kullanılarak güzergâh (rota) seçimi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,11(3), 425-435.
  • PHMSA, United States Department of Transportation Pipeline and Hazardous Materials Safety Administration. Hazmat Intelligence Portal. (Erişim: 03.01.2018.), https://hip.phmsa.dot.gov/analytics/saw.dll.
  • Szeto, W.Y., Farahani, R. Z., & Sumalee, A. (2017). Link-based multi-class hazmat routing-scheduling problem: A multiple demon approach. European Journal of Operational Research, 261(1), 337-354.
  • Tervonen, T. (2014). JSMAA: Open source software for SMAA computations. International Journal of Systems Science, 45(1), 69-81.
  • Tervonen, T., & Lahdelma, R., (2007). Implementing Stochastic Multicriteria Acceptability Analysis. European Journal of Operational Research, 178(2), 500-513.
  • Ulaştırma Bakanlığı. (2008). Tehlikeli Maddelerin Karayoluyla Taşınması Hakkında Yönetmelik. 15.06.2008 tarih ve 26907 Sayılı Resmi Gazete (1. Değişiklik).
  • Verma, M., Verter, V., & Zufferey, N. (2012). A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 48(1), 132-149.
  • Yılmaz, Z., Erol, S., & Aplak, H.S. (2016). Transportation of hazardous materials (hazmat) a literature survey. Pamukkale University Journal of Engineering Sciences, 22(1), 39-63.

A Stochastic Risk Analysis for the Problem of Route Selection of Hazardous Materials’ Transportation

Year 2018, Volume: 6 Issue: 6, 935 - 943, 02.12.2018

Abstract

Today, hazardous
materials’ transportation is one of the important issues that must be taken
into consideration in order to ensure the safety of life and property and to
carry out operations without any interruption. With the increasing level of
industrialization, it has become more important to move hazardous materials,
which have begun to be used more frequently, safely from one point to another.
Although all transportation modes are used effectively in the hazardous
materials’ transportation, mostly the roads are used.  For this reason, by taking into consideration
the risk, involved in hazardous materials, they require more rigorous
transportation and more effective risk management. In this study, it is aimed
to determine the most appropriate route for the hazardous materials’
transportation in a cost effective, safe and uninterrupted manner.  In this context, face-to-face meeting has
been held with the managers of a company transporting hazardous materials
between Gaziantep and Erzurum provinces, and the hazardous materials’
transportation routes are selected by SMAA-2 method.
   

References

  • Akçetin, E. (2013). Tehlikeli Madde Lojistiğinde Risk Yönetimi (1.baskı). İstanbul: Nobel Yayın Dağıtım.
  • Alumur, S., & Kara, B. Y. (2007). A new model for the hazardous waste location-routing problem. Computers & Operations Research, 34(5), 1406-1423.
  • Bahramian, Z., & Bagheri, G. (2017). An approach for road, railway, pipeline routing problem in hazardous materials transportation using multiple criteria. 4th International Conference on Transportation Information and Safety (ICTIS), (pp. 1088-1092), August 8-10, 2017, Banff, Canada.
  • Bell, M.G.H. (2007). Mixed routing strategies for hazardous materials: decision-making under complete uncertainty, International Journal of Sustainable Transportation, 1(2), 133-142.
  • BMAEK, Birleşmiş Milletler Avrupa Ekonomik Komisyonu, (2007). Tehlikeli Malların Karayolu İle Taşınmasına İlişkin Avrupa Anlaşması (ADR), Cenevre.
  • Bonvicini, S., & Spadoni, G. (2008). A hazmat multi-commodity routing model satisfying risk criteria: A case study. Journal of Loss Prevention in the Process Industries, 21(4), 345-358.
  • Bronfman, A., Marianov, V., Paredes-Belmar, G., & Lüer-Villagra, A. (2015). The maximin HAZMAT routing problem. European Journal of Operational Research, 241(1), 15-27.
  • Bula, G. A., Prodhon, C., Gonzalez, F. A., Afsar, H. M., & Velasco, N. (2017). Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. Journal of Hazardous Materials, 324, 472-480.
  • Caramia, M., Giordani, S., & Iovanella, A. (2009). On the selection of k routes in multiobjective hazmat route planning. IMA Journal of Management Mathematics, 21(3), 239-251.
  • Carotenuto, P., Giordani, S., Ricciardelli, S., & Rismondo, S. (2007). A tabu search approach for scheduling hazmat shipments. Computers & Operations Research, 34(5), 1328-1350.
  • Chakrabarti, U. K., & Parikh, J. K. (2013). Risk-based route evaluation against country-specific criteria of risk tolerability for hazmat transportation through Indian State Highways. Journal of Loss Prevention in the Process Industries, 26(4), 723-736.
  • Dadkar, Y., Jones, D., & Nozick, L. (2008). Identifying geographically diverse routes for the transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 44(3), 333-349.
  • Dadkar, Y., Nozick, L., & Jones, D. (2010). Optimizing facility use restrictions for the movement of hazardous materials. Transportation Research Part B: Methodological, 44(2), 267-281.
  • Daimonlogistics. (2017). Tehlikeli Madde Lojistiği. (Erişim: 02.01 2018), http://daimonlogistics.com/26-tehlikeli-madde-tasimaciligi/.
  • Das, A., Mazumder, T. N., & Gupta, A. K. (2012). Pareto frontier analyses based decision making tool for transportation of hazardous waste. Journal of Hazardous Materials, 227, 341-352.
  • Demirdöğen, O., Erdal, H., Yazıcılar, F.G. & Aykol, S. (2017). Disaster Logistics Facility Location Problem: An Application For TRA1 Region, The International New Issues in Social Sciences, 5(5), 323-342.
  • Desai, S., & Lim, G. J. (2013). Solution time reduction techniques of a stochastic dynamic programming approach for hazardous material route selection problem. Computers & Industrial Engineering, 65(4), 634-645.
  • Erkut, E., & Alp, O. (2007). Integrated Routing and Scheduling of Hazmat Trucks with Stops En Route. Transportation Science, 41(1), 107-122.
  • Erkut, E., Tjandra, S. A., & Verter, V. (2007). Hazardous materials transportation. Handbooks In Operations Research And Management Science, 14, 539-621.
  • Faghih-Roohi, S., Ong, Y. S., Asian, S., & Zhang, A. N. (2016). Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks. Annals of Operations Research, 247(2), 715-734.
  • Garrido, R. A., & Bronfman, A. C. (2016). Equity and social acceptability in multiple hazardous materials routing through urban areas. Transportation Research Part A: Policy and Practice, 102, 244-260.
  • Glickman, T. S., Erkut, E., & Zschocke, M. S. (2007). The cost and risk impacts of rerouting railroad shipments of hazardous materials. Accident Analysis & Prevention, 39(5), 1015-1025.
  • Hosseini, S. D., & Verma, M. (2017). A Value-at-Risk (VAR) approach to routing rail hazmat shipments. Transportation Research Part D: Transport and Environment, 54, 191-211.
  • Huang, B. (2006). GIS-based route planning for hazardous material transportation. Journal of Environmental Informatics, 8(1), 49-57.
  • Karabulut, S., & Öcalır-Akünal, E. V. (2015). Karayolu ile tehlikeli madde taşımacılığı için coğrafi bilgi sistemi destekli çevresel risk analizi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(3), 351-359.
  • Kang, Y., Batta, R., & Kwon, C. (2014). Generalized route planning model for hazardous material transportation with VaR and equity considerations. Computers & Operations Research, 43, 237-247.
  • KGM, Karayolları Genel Müdürlüğü. (2010). 2010 Trafik ve Ulaşım Bilgileri. (Erişim: 17.09.2017),http://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Istatistikler/TrafikveUlaşımBilgileri/10TrafikUlasimBilgileri%20.pdf.
  • Lahdelma R., Hokkanen J., & Salminen P. (1998). SMAA-stochastic multiobjective acceptability analysis. European Journal of Operational Research, 106, 137-143.
  • Lahdelma, R., & Salminen, P. (2001). SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49(3), 444-454.
  • Li, R., & Leung, Y. (2011). Multi-objective route planning for dangerous goods using compromise programming. Journal of Geographical Systems, 13(3), 249-271.
  • Mahmoudabadi, A., & Seyedhosseini, S. M. (2014). Developing a chaotic pattern of dynamic Hazmat routing problem. IATSS Research, 37(2), 110-118.
  • OSHA, Occupational Safety and Health Administration. (2017). (Erişim:14.08.2017), http://www.osha.gov.
  • MEB, Millî Eğitim Bakanlığı. (2011). Tehlikeli Madde Taşımacılığı Modülü, Ankara.
  • Murat, Ş., & Kulak, O. (2005). Ulaşım ağlarında bilgi aksiyomu kullanılarak güzergâh (rota) seçimi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,11(3), 425-435.
  • PHMSA, United States Department of Transportation Pipeline and Hazardous Materials Safety Administration. Hazmat Intelligence Portal. (Erişim: 03.01.2018.), https://hip.phmsa.dot.gov/analytics/saw.dll.
  • Szeto, W.Y., Farahani, R. Z., & Sumalee, A. (2017). Link-based multi-class hazmat routing-scheduling problem: A multiple demon approach. European Journal of Operational Research, 261(1), 337-354.
  • Tervonen, T. (2014). JSMAA: Open source software for SMAA computations. International Journal of Systems Science, 45(1), 69-81.
  • Tervonen, T., & Lahdelma, R., (2007). Implementing Stochastic Multicriteria Acceptability Analysis. European Journal of Operational Research, 178(2), 500-513.
  • Ulaştırma Bakanlığı. (2008). Tehlikeli Maddelerin Karayoluyla Taşınması Hakkında Yönetmelik. 15.06.2008 tarih ve 26907 Sayılı Resmi Gazete (1. Değişiklik).
  • Verma, M., Verter, V., & Zufferey, N. (2012). A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 48(1), 132-149.
  • Yılmaz, Z., Erol, S., & Aplak, H.S. (2016). Transportation of hazardous materials (hazmat) a literature survey. Pamukkale University Journal of Engineering Sciences, 22(1), 39-63.
There are 41 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Hamit Erdal 0000-0001-8352-6427

Publication Date December 2, 2018
Acceptance Date May 10, 2018
Published in Issue Year 2018 Volume: 6 Issue: 6

Cite

APA Erdal, H. (2018). Tehlikeli Madde Taşımacılığı Güzergâh Seçimi Problemi İçin Stokastik Bir Risk Analizi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6(6), 935-943.

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.