Optimization of medical waste routing problem: The case of TRB1 region in Turkey
DOI:
https://doi.org/10.11121/ijocta.01.2019.00714Keywords:
Medical waste, GIS, routing, case studyAbstract
A fundamental problem concerning medical waste disposal is the evaluation of the real and potential risks arising from waste with the focus on the risk of infection. Therefore, the optimization of medical waste routing from collection to disposal center can minimize the risk of infection. The routing of medical waste considers significant to determine potential routes and select the route with minimum distance. The management of the medical waste is important decision for environmental sustainability and includes the collection, transportation and disposal of these materials. In this paper, a geographic information system (GIS) solution approach is proposed to determine the best location of disposal center. Proposed approach is applied to medical waste transportation between 167 health institutions (collection centers) and predetermined 5 disposal centers through TRB1 region in Turkey, which consist of Malatya, Elaz??, Bingöl and Tunceli provinces. The results of case study are examined and suggestions for future research are provided.
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References
Birpınar, M. E., Bilgili, M. S., & Erdoğan, T. (2009). Medical waste management in Turkey: A case study of Istanbul. Waste Management, 29(1), 445-448.
Alagöz, A. Z., & Kocasoy, G. (2008). Improvement and modification of the routing system for the health-care waste collection and transportation in Istanbul. Waste Management, 28(8), 1461-1471.
Özceylan, E., Erbaş, M., Çetinkaya, C., & Kabak, M. (2017). A GIS-based risk reduction approach for the hazardous materials routing problem in Gaziantep. Human and Ecological Risk Assessment: An International Journal, 23(6), 1437-1453.
Wu, T. H., Low, C., & Bai, J. W. (2002). Heuristic solutions to multi-depot location-routing problems. Computers & Operations Research, 29(10), 1393-1415.
Laporte, G., Nobert, Y., (1981). An exact algorithm for minimizing routing and operating costs in depot location. European Journal of Operational Research 6, 224–226.
Laporte G, Nobert Y, Desrochers M (1985) Optimal routing under capacity and distance restrictions. Oper Res 33:1058–1073
Ghiani, G., Laporte, G., (1999). Eulerian location problems. Networks 34, 291–302.
Averbakh, I., Berman, O., (2002). Minmax p-traveling salesmen location problems on a tree. Annals of Operations Research, 110, 55–62.
Labbé, M., Laporte, G., Martin, I. R., & González, J. J. S. (2005). Locating median cycles in networks. European Journal of Operational Research, 160(2), 457-470.
Alumur, S., & Kara, B. Y. (2007). A new model for the hazardous waste location-routing problem. Computers & Operations Research, 34(5), 1406-1423.
Ponboon, S., Qureshi, A. G., & Taniguchi, E. (2016). Branch-and-price algorithm for the location-routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review, 86, 1-19.
Farham, M. S., Süral, H., & Iyigun, C. (2018). A column generation approach for the location-routing problem with time windows. Computers & Operations Research, 90, 249-263.
Perl, Jossef, and Mark S. Daskin (1985). A warehouse location-routing problem. Transportation Research Part B: Methodological,19(5): 381-396.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European journal of operational research, 177(2), 649-672.
Vincent, F. Y., Lin, S. W., Lee, W., & Ting, C. J. (2010). A simulated annealing heuristic for the capacitated location routing problem. Computers & Industrial Engineering, 58(2), 288-299.
Ting, C. J., & Chen, C. H. (2013). A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141(1), 34-44.
Albareda-Sambola, M., Dı́az, J. A., & Fernández, E. (2005). A compact model and tight bounds for a combined location-routing problem. Computers & Operations Research, 32(3), 407-428.
Caballero, R., González, M., Guerrero, F. M., Molina, J., & Paralera, C. (2007). Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177(3), 1751-1763.
Marinakis, Y., & Marinaki, M. (2008). A bilevel genetic algorithm for a real-life location routing problem. International Journal of Logistics: Research and Applications, 11(1), 49-65.
Asgari, N., Rajabi, M., Jamshidi, M., Khatami, M., & Farahani, R. Z. (2017). A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study. Annals of Operations Research, 250(2), 279-308.
Erkut, E., & Verter, V. (1998). Modeling of transport risk for hazardous materials. Operations research, 46(5), 625-642.
Leonelli, P., Bonvicini, S., & Spadoni, G. (2000). Hazardous materials transportation: a risk-analysis-based routing methodology. Journal of Hazardous Materials, 71(1-3), 283-300.
Androutsopoulos, K. N., & Zografos, K. G. (2010). Solving the bicriterion routing and scheduling problem for hazardous materials distribution. Transportation Research Part C: Emerging Technologies, 18(5), 713-726.
Mahmoudabadi, A., & Seyedhosseini, S. M. (2014). Solving Hazmat Routing Problem in chaotic damage severity network under emergency environment. Transport policy, 36, 34-45.
Zografos, K. G., & Androutsopoulos, K. N. (2004). A heuristic algorithm for solving hazardous materials distribution problems. European Journal of Operational Research, 152(2), 507-519
Huang, B., Cheu, R. L., & Liew, Y. S. (2004). GIS and genetic algorithms for HAZMAT route planning with security considerations. International Journal of Geographical Information Science, 18(8), 769-787.
Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. (2016). Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15.
Yilmaz, O., Kara, B. Y., & Yetis, U. (2017). Hazardous waste management system design under population and environmental impact considerations. Journal of environmental management, 203, 720-731.
Erkut, E., Tjandra, S. A., & Verter, V. (2007). Hazardous materials transportation. Handbooks in operations research and management science, 14, 539-621.
Shih, L. H., & Chang, H. C. (2001). A routing and scheduling system for infectious waste collection. Environmental Modeling & Assessment, 6(4), 261-269.
Mourao, M. C., & Almeida, M. T. (2000). Lower-bounding and heuristic methods for a refuse collection vehicle routing problem1. European Journal of operational research, 121(2), 420-434.
Marinković, N., Vitale, K., Holcer, N. J., Džakula, A., & Pavić, T. (2008). Management of hazardous medical waste in Croatia. Waste management, 28(6), 1049-1056.
Abdulla, F., Qdais, H. A., & Rabi, A. (2008). Site investigation on medical waste management practices in northern Jordan. Waste management, 28(2), 450-458.
Windfeld, E. S., & Brooks, M. S. L. (2015). Medical waste management–A review. Journal of environmental management, 163, 98-108.
Alshraideh, H., & Qdais, H. A. (2017). Stochastic modeling and optimization of medical waste collection in Northern Jordan. Journal of Material Cycles and Waste Management, 19(2), 743-753.
Mmereki, D., Baldwin, A., Li, B., & Liu, M. (2017). Healthcare waste management in Botswana: storage, collection, treatment and disposal system. Journal of Material Cycles and Waste Management, 19(1), 351-365.
Baldacci, R., Mingozzi, A., & Roberti, R. (2012). Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research, 218(1), 1-6.
Huang, B. (2006). GIS-based route planning for hazardous material transportation. Journal of Environmental Informatics, 8(1).
TUIK (2016). Turkish Statistical Institute URL http://www.tuik.gov.tr/PreHaberBultenleri.do?id=24871 (accessed 08.03.2018)
Koç, Ç., Erbaş, M., & Ozceylan, E. (2018). A rich vehicle routing problem arising in the replenishment of automated teller machines. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 8(2), 276-287.
Özceylan, E., Uslu, A., Erbaş, M., Çetinkaya, C., & İşleyen, S. K. (2017). Optimizing the location-allocation problem of pharmacy warehouses: A case study in Gaziantep. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 7(1), 117-129.
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