Bi-Objective Model for Ambulance Routing for Disaster Response by Considering Priority of Patients

Document Type : Research Paper

Authors

1 College of Engineering, University of Tehran, Tehran, Iran

2 Industrial Engineering, Khatam University,Tehran, Iran

Abstract

Disaster situation could suddenly apply large number of injured people and disarrange the emergency medical service simultaneously (EMS). Using all facilities, ambulance, rescue helicopter and medical drone as integrated part and making decision for assigning to patient’s efficiency are concerns of EMS. therefore, in this study we introduce a new bi-objective model for EMS in order to reduce the rate of mobility and mortality with aim of reducing latest service completion time and total cost of system simultaneously. By recognition level of injury, we consider two types of patients: i. red code patients who have been injured seriously and have to be taken to an hospital. and ii. green code patients who are injured slightly and are treated at the same place. Since making decision and responding to disaster situation should be with high quality within seconds, for this purpose after validating and solving that by augmented ε-constraint method by GAMS, we applied two NSGA-II and customized Bees algorithm for multi objective solution that called Multi Objective Bees Algorithm (MOBA) in dynamics and uncertain situation for coping high frequency within very short response time and near the optimum solution result. The quality of the result is considered for choosing the metaheuristic solution. At the end, sensitive analysis is implemented on the model and the effect of reducing and increasing some of parameters on the model is investigated.

Keywords

References

Garner A. Lee K. H and Schultz C. H. (2001). Comparative analysis of multiple-casualty incident triage algorithms. Ann Emerg Med, Vol. 38(5), pp. 541–548.
Jotshi Q. and Batta R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion,” Socioecon. Plann. Sci., Vol. 43(1), pp. 1–24.
Caunhye M, Li M. and. Nie X, (2015). A location-allocation model for casualty response planning during catastrophic radiological incidents. Socioecon. Plann. Sci., Vol. 50, pp. 32–44.
Caunhye M., Nie X. and Pokharel S. (2012). Optimization models in emergency logistics: A literature review. Socioecon. Plann. Sci., Vol. 46(1), pp. 4–13.
Campbell M., Vandenbussche D. and W. Hermann. (2008). Routing for Relief Efforts. Transp. Sci., vol. 42, no. 2, pp. 127–145.
Iannoni P., Morabito R. and Saydam C. (2009). An optimization approach for ambulance location and the districting of the response segments on highways. Eur. J. Oper. Res., vol. 195, no. 2, pp. 528–542.
Saeidian B., Saeidian M. S., and Ghodousi M. (2016). Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location–allocation of earthquake relief centers. Int. J. Disaster Risk Reduct., vol. 15, pp. 94–107.
Vahdani B., Shekari D. V. N. and Mousavi S. M. (2016). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair.Neural Comput. Appl.
Van De Walle B., Turoff M. and Hiltz S. R.(2014). Information systems for emergency management. Routledge, 2014.
Kahn C. A., Schultz C. H., Miller K. T. and Anderson C. L. (2009). Does START triage work? An outcomes assessment after a disaster. Ann Emerg Med, vol. 54, no. 3, p. 424–30, 430 e1.
Travers D. A., Waller A. E., Bowling J. M., Flowers D. and Tintinalli J. (2002). Five-level triage system more effective than three-level in tertiary emergency department. J. Emerg. Nurs., vol. 28, no. 5, pp. 395–400.
Berkoune D., Renaud J., Rekik M. and Ruiz A. (2012). Transportation in disaster response operations. Socioecon. Plann. Sci., vol. 46, no. 1, pp. 23–32.
Pham D. T., Ghanbarzadeh A., Koc E., Otri S., Rahim S. and Zaidi M. (20122). The bee’s algorithm-A novel tool for complex optimisation. in Intelligent Production Machines and Systems-2nd I* PROMS Virtual International Conference (3-14 July 2006).
Paton D. and Johnston D. (2017). Disaster resilience: an integrated approach. Charles C Thomas Publisher.
Sapir D., Hargitt D. and P. Hoyois. (2004). Thirty years of natural disasters. 1974-2003: The numbers. Presses univ. de Louvain.
Lerner E. B. (2011). Mass casualty triage: an evaluation of the science and refinement of a national guideline. Disaster Med. Public Health Prep., vol. 5, no. 2, pp. 129–137.
Ghiani G., Guerriero F, Laporte G. and. Musmanno R. (2003). Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies,” Eur. J. Oper. Res., vol. 151, no. 1, pp. 1–11.
Tirado G., Vitoriano B. and Scaparra M. P. (2012). Computers & Operations Research a hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics.
Mavrotas G. and Florios K. (2013). An improved version of the augmented s-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems.Appl. Math. Comput., vol. 219, no. 18, pp. 9652–9669.
Mavrotas G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput., vol. 213, no. 2, pp. 455–465.
Jaldell H., Lebnak. P, and Amornpetchsathaporn A. (2014). Time Is Money, But How Much? The Monetary Value of Response Time for Thai Ambulance Emergency Services,” Value Heal. vol. 17, no. 5, pp. 555–560.
Rajagopalan H. K., Saydam C. and Xiao J. (2008). A multiperiod set covering location model for dynamic redeployment of ambulances. Comput. Oper. Res., vol. 35, no. 3, pp. 814–826.
Fitzsimmons J. A. and Srikar B. N. (1982). Emergency ambulance location using the contiguous zone search routine,” J. Oper. Manag., vol. 2, no. 4, pp. 225–237.
Goldberg J., Dietrich R., Chen J. M., Mitwasi M., Valenzuela T. and Criss E. (1990).A simulation model for evaluating a set of emergency vehicle base locations: Development, validation, and usage,” Socioecon. Plann. Sci., Vol. 24(2), pp. 125–141.
Deb K., Pratap A., Agarwal S. and Meyarivan T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., Vol. 6(2), pp. 182–197.
Eshghi K. and Larson R. C. (2008). Disasters: lessons from the past 105 years. Disaster Prev. Manag. An Int. J., vol. 17, no. 1, pp. 62–82.
Zografos K. G. and Androutsopoulos K. N. (2008). A decision support system for integrated hazardous materials routing and emergency response decisions,” Transp. Res. Part C Emerg. Technol., Vol. 16(6), pp. 684–703.
Scholten K., Sharkey Scott P. and Fynes B. (2014). Mitigation processes – antecedents for building supply chain resilience. Supply Chain Manag. An Int. J., Vol. 19(2), pp. 211–228.
Brotcorne L., Laporte G. and Semet F. (2003). Ambulance location and relocation models. Eur. J. Oper. Res., Vol. 147, (3), pp. 451–463.
de la Torre L. E., Dolinskaya I. S. and Smilowitz K. R. (2012). Disaster relief routing: Integrating research and practice, Socioecon. Plann. Sci., Vol. 46(1), pp. 88–97.
Talarico L., Meisel F. and Sörensen K. (2015). Ambulance routing for disaster response with patient groups,” Comput. Oper. Res., Vol. 56, pp. 120–133.
Birnbaum M. L., Daily E. K. and Rourke A. P. O. (2016). Research and evaluations of the health aspects of disasters, part VII: The Relief/Recovery Framework. Prehosp. Disaster Med., Vol. 31(2), pp. 195–210.
Silva M. S. (2016). Reducing Emergency Medical Service response time via the reallocation of ambulance bases.  pp. 31–42.
Altay N. and Green W. G. (2006). OR/MS research in disaster operations management. Eur. J. Oper. Res., Vol. 175(1), pp. 475–493.
Kunz N., Reiner G. and Gold S.J. (2013). Production Economics Investing in disaster management capabilities versus pre-positioning inventory: A new approach to disaster preparedness. Intern. J. Prod. Econ., pp. 1–12.
Tatham P. and Kovács G. (2010). The application of ‘swift trust’ to humanitarian logistics. Int. J. Prod. Econ., Vol. 126(1), pp. 35–45.
Abounacer R., Rekik M. and Renaud J. (2014). An exact solution approach for multi-objective location–transportation problem for disaster response. Comput. Oper. Res., Vol. 41, pp. 83–93.
Aringhieri R., Bruni M. E., Khodaparasti S. and van Essen J. T. (2017). Emergency medical services and beyond: Addressing new challenges through a wide literature review. Comput. Oper. Res., Vol. 78, pp. 349–368.
Goldberg R. and Listowsky P. (1994). Critical factors for emergency vehicle routing expert systems. Expert Syst. Appl., Vol. 7(4), pp. 589–602.
Tavakkoli-Moghaddam R., Shishegar S., Siadat A. and Mohammadi M. (2016). Design of a Reliable Bi-objective Relief Routing Network in the Earthquake Response Phase. Procedia Comput. Sci., Vol. 102, pp. 74–81.
Seidgar, H., Kiani, M.  and Fazlollahtabar, H. (2016). Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem. Production & Manufacturing Research, Vol. 4(1), 133-151.
Tufekci S. and Wallace W. A. (1998). The emerging area of emergency management and engineering,” IEEE Trans. Eng. Manag., Vol. 45(2), pp. 103–105.
Balaman Ş. Y. (2016). Investment planning and strategic management of sustainable systems for clean power generation: An ε-constraint based multi objective modelling approach. J. Clean. Prod., Vol. 137, pp. 1179–1190.
Andersson T. and Värbrand P. (2006). Decision support tools for ambulance dispatch and relocation,” J. Oper. Res. Soc., vol. 58, no. 2, pp. 195–201.
W. G. III. (2002). Four phases of emergency management,” Encycl. Civ. Def. Emerg.
Yi W. and Özdamar L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities,” Eur. J. Oper. Res., Vol. 179(3), pp. 1177–1193.
International Journal of Supply and Operations Management
Volume 9, Issue 1
February 2022
Pages 80-94

Files

Share

How to cite

Statistics