A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances

References

• Ansari S, McLay LA, Mayorga ME (2015) A maximum expected covering problem for district design. Transportation Sci. 51(1):376–390.Link, Google Scholar
• Aringhieri R, Bruni M, Khodaparasti S, van Essen J (2017) Emergency medical services and beyond: Addressing new challenges through a wide literature review. Comput. Oper. Res. 78:349–368.Crossref, Google Scholar
• Bakalos G, Mamali M, Komninos C, Koukou E, Tsantilas A, Tzima STR (2011) Advanced life support vs. basic life support in the pre-hospital setting: A meta-analysis. Resuscitation 82(9):1130–1137.Crossref, Google Scholar
• Batta R, Dolan JM, Krishnamurthy NN (1989) The maximal expected covering location problem: Revisited. Transportation Sci. 23(4):277–287.Link, Google Scholar
• Bélanger V, Ruiz A, Soriano P (2019) Recent optimization models and trends in location, Relocation, and dispatching of emergency medical vehicles. Eur. J. Oper. Res. 272(1):1–23.Crossref, Google Scholar
• Benders JF (1962) Partitioning procedures for solving mixed-variables programming problems. Numerische Math. 4(1):238–252.Crossref, Google Scholar
• Beraldi P, Bruni M (2009) A probabilistic model applied to emergency service vehicle location. Eur. J. Oper. Res. 196(1):323–331.Crossref, Google Scholar
• Birge JR (1982) The value of the stochastic solution in stochastic linear programs with fixed recourse. Math. Programming 24(1):314–325.Crossref, Google Scholar
• Bodur M, Luedtke JR (2016) Mixed-integer rounding enhanced benders decomposition for multiclass service-system staffing and scheduling with arrival rate uncertainty. Management Sci. 63(7):2073–2091.Link, Google Scholar
• Bodur M, Dash S, Günlük O, Luedtke J (2017) Strengthened Benders cuts for stochastic integer programs with continuous recourse. INFORMS J. Comput. 29(1):77–91.Link, Google Scholar
• Boujemaa R, Jebali A, Hammami S, Ruiz A, Bouchriha H (2018) A stochastic approach for designing two-tiered emergency medical service systems. Flexible Services Manufacturing J. 30(1):123–152.Crossref, Google Scholar
• Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models. Eur. J. Oper. Res. 147(3):451–463.Crossref, Google Scholar
• Chong KC, Henderson SG, Lewis ME (2016) The vehicle mix decision in emergency medical service systems. Manufacturing Service Oper. Management 18(3):347–360.Link, Google Scholar
• Clawson JJ, Dernocoeur KB (2001) Principles of Emergency Medical Dispatch, 11.1 ed. (National Academy of Emergency Medical Dispatch, Salt Lake City, UT).Google Scholar
• Daskin MS (1983) A maximum expected covering location model: Formulation, properties and heuristic solution. Transportation Sci. 17(1):48–70.Link, Google Scholar
• Enayati S, Ozaltin O, Mayorga M, Saydam C (2018) Ambulance redeployment and dispatching under uncertainty with personnel workload limitations. IIE Trans. 50(9):777–788.Google Scholar
• Goldberg J, Paz L (1991) Locating emergency vehicle bases when service time depends on call location. Transportation Sci. 25(4):264–280.Link, Google Scholar
• Grannan BC, Bastian ND, McLay LA (2015) A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets. Optim. Lett. 9(8):1511–1531.Crossref, Google Scholar
• Ingolfsson A, Budge S, Erkut E (2008) Optimal ambulance location with random delays and travel times. Health Care Management Sci. 11:262–274.Crossref, Google Scholar
• Khodaparasti S, Maleki H, Bruni ME, Jahedi S, Beraldi P, Conforti D (2016) Balancing efficiency and equity in location-allocation models with an application to strategic EMS design. Optim. Lett. 10(5):1053–1070.Crossref, Google Scholar
• Kleywegt AJ, Shapiro A, de Mello TH (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.Crossref, Google Scholar
• Lord D, Mannering F (2010) The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transporation Res. Part A Policy Practice 44(5):291–305.Crossref, Google Scholar
• Mandell MB (1998) Covering models for two-tiered emergency medical service systems. Location Sci. 6(1–4):355–368.Crossref, Google Scholar
• Marianov V, ReVelle C (1992) A probabilistic fire-protection siting model with joint vehicle reliability requirements. Papers Regional Sci. 71(3):212–241.Crossref, Google Scholar
• McLay LA (2009) A maximum expected covering location model with two types of servers. IIE Trans. 41(8):730–741.Crossref, Google Scholar
• McLay LA, Moore H (2012) Hanover County improves its response to emergency medical 911 patients. Interfaces 42(4):380–394.Link, Google Scholar
• Moore GC, ReVelle C (1982) The hierarchical service location problem. Management Sci. 28(7):775–780.Link, Google Scholar
• Naoum-Sawaya J, Elhedhli S (2013) A stochastic optimization model for real-time ambulance redeployment. Comput. Oper. Res. 40(5):1972–1978.Crossref, Google Scholar
• National Fire Protection Agency (2020) Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Career Fire Departments (National Fire Protection Agency, Quincy, MA).Google Scholar
• Nickel S, Reuter-Oppermann M, da Gama FS (2016) Ambulance location under stochastic demand: A sampling approach. Oper. Res. Health Care 8(Supplement C):24–32.Crossref, Google Scholar
• Noyan N (2010) Alternate risk measures for emergency medical service system design. Ann. Oper. Res. 181(1):559–589.Crossref, Google Scholar
• O’Keeffe C, Nicholl J, Turner J, Goodacre S (2011) Role of ambulance response times in the survival of patients with out-of-hospital cardiac arrest. Emergency Med. J. 28(8):703–706.Crossref, Google Scholar
• Peng C, Delage E, Li J (2018) Dynamic emergency medical services network design: A novel probabilistic envelope constrained stochastic model and decomposition scheme. Working paper, HEC Montreal, Montreal.Google Scholar
• Restrepo M, Henderson S, Topaloglu H (2009) Erlang loss models for the static deployment of ambulances. Health Care Management Sci. 12:67–79.Crossref, Google Scholar
• Reuter-Oppermann M, van den Berg PL, Vile JL (2017) Logistics for emergency medical service systems. Health Systems 6(3):187–208.Crossref, Google Scholar
• Robbins H, Monro S (1951) A stochastic approximation method. Ann. Math. Statist. 22:400–407.Crossref, Google Scholar
• Schilling D, Elzinga DJ, Cohon J, Church R, ReVelle C (1979) The team/fleet models for simultaneous facility and equipment siting. Transportation Sci. 13(2):163–175.Link, Google Scholar
• Setzler H, Saydam C, Park S (2009) EMS call volume predictions: A comparative study. Comput. Oper. Res. 36(6):1843–1851.Crossref, Google Scholar
• Sung I, Lee T (2018) Scenario-based approach for the ambulance location problem with stochastic call arrivals under a dispatching policy. Flexible Services Manufacturing J. 30(1–2):153–170.Crossref, Google Scholar
• Toro-Díaz H, Mayorga ME, Chanta S, McLay LA (2013) Joint location and dispatching decisions for emergency medical services. Comput. Indust. Engrg. 64(4):917–928.Crossref, Google Scholar
• Toro-Díaz H, Mayorga ME, McLay LA, Rajagopalan HK, Saydam C (2015) Reducing disparities in large-scale emergency medical service systems. J. Oper. Res. Soc. 66(7):1169–1181.Crossref, Google Scholar
• Yoon S, Albert LA (2018) Dynamic resource assignment for emergency response with multiple types of vehicles. Technical Report, University of Wisconsin–Madison, Madison, WI.Google Scholar
• Yoon S, Albert LA (2020) A dynamic ambulance routing model with multiple response. Transportation Res., Part E Logististics Transportation Rev. 133:101807.Crossref, Google Scholar
• Zhou Z, Matteson DS, Woodard DB, Henderson SG, Micheas AC (2015) A spatio-temporal point process model for ambulance demand. J. Amer. Statist. Assoc. 110(509):6–15.Crossref, Google Scholar