Abstract
We study the problem of locating service facilities to serve heterogeneous customers. Customers requiring service are classified as either high priority or low priority, where high priority customers are always served on a priority basis. The problem is to optimally locate service facilities and allocate their service zones to satisfy the following coverage and service level constraints: (1) each demand zone is served by a service facility within a given coverage radius; (2) at least \(\alpha ^h\) proportion of the high priority customers at any service facility should be served without waiting; (3) at least \(\alpha ^l\) proportion of the low priority cases at any service facility should not have to wait for more than \(\tau ^l\) minutes. For this, we model the network of service facilities as spatially distributed priority queues, whose locations and user allocations need to be determined. The resulting integer programming problem is challenging to solve, especially in absence of any known analytical expression for the service level function of low priority customers. We develop a cutting plane based solution algorithm, exploiting the concavity of the service level function of low priority customers to outer-approximate its non-linearity using supporting planes, determined numerically using matrix geometric method. Using an illustrative example of locating emerging medical service facilities in Austin, Texas, we present computational results and managerial insights.
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References
Abate, J., & Whitt, W. (1997). Asymptotics for m/g/1 low-priority waiting-time tail probabilities. Queueing Systems, 25(1–4), 173–233.
Aboolian, R., Berman, O., & Krass, D. (2012). Profit maximizing distributed service system design with congestion and elastic demand. Transportation Science, 46(2), 247–261.
Amiri, A. (1997). Solution procedures for the service system design problem. Computers & Operations Research, 24(1), 49–60.
Atlason, J., Epelman, M. A., & Henderson, S. G. (2004). Call center staffing with simulation and cutting plane methods. Annals of Operations Research, 127(1–4), 333–358.
Baron, O., Berman, O., & Krass, D. (2008). Facility location with stochastic demand and constraints on waiting time. Manufacturing & Service Operations Management, 10(3), 484–505.
Belotti, P., Labbé, M., Maffioli, F., & Ndiaye, M. M. (2007). A branch-and-cut method for the obnoxious p-median problem. 4OR, 5(4), 299–314.
Berman, O., & Krass, D. (2002). Facility location problems with stochastic demands and congestion. In Z. Drezner & H. Hamacher (Eds.), Facility location: Applications and theory. Berlin: Springer.
Berman, O., & Krass, D. (2015). Stochastic location models with congestion. Location science (pp. 443–486). Berlin: Springer.
Berman, O., Krass, D., & Wang, J. (2006). Locating service facilities to reduce lost demand. IIE Transactions, 38(11), 933–946.
Boffey, B., Galvao, R., & Espejo, L. (2007). A review of congestion models in the location of facilities with immobile servers. European Journal of Operational Research, 178(3), 643–662.
Cánovas, L., García, S., Labbé, M., & Marín, A. (2007). A strengthened formulation for the simple plant location problem with order. Operations Research Letters, 35(2), 141–150.
Castillo, I., Ingolfsson, A., & Sim, T. (2009). Socially optimal location of facilities with fixed servers, stochastic demand and congestion. Production and Operations Management, 18(6), 721–736.
Church, R. L., & Cohon, J. L. (1976). Multiobjective location analysis of regional energy facility siting problems. Brookhaven National Laboratory, Upton, NY, USA: Tech. rep.
Daskin, M. S. (1982). Application of an expected covering model to emergency medical service system design. Decision Sciences, 13(3), 416–439.
Daskin, M. S., & Stern, E. H. (1981). A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15(2), 137–152.
Dobson, G., & Karmarkar, U. S. (1987). Competitive location on a network. Operations Research, 35(4), 565–574.
Elhedhli, S. (2006). Service system design with immobile servers, stochastic demand, and congestion. Manufacturing & Service Operations Management, 8(1), 92–97.
Espejo, I., Marín, A., & Rodríguez-Chía, A. M. (2012). Closest assignment constraints in discrete location problems. European Journal of Operational Research, 219(1), 49–58.
Gilboy, N., Tanabe, T., Travers, D., & Rosenau, A., (2011). Emergency Severity Index (ESI): A triage tool for emergency department care, version 4. Implementation Handbook 2012 Edition.
Jayaswal, S. (2009). Product differentiation and operations strategy for price and time sensitive markets. PhD thesis. Ontario: Department of Management Sciences, University of Waterloo.
Jayaswal, S., Jewkes, E., & Ray, S. (2011). Product differentiation and operations strategy in a capacitated environment. European Journal of Operational Research, 210(3), 716–728.
Jayaswal, S., & Jewkes, E. M. (2016). Price and lead time differentiation, capacity strategy and market competition. International Journal of Production Research, 54(9), 2791–2806.
Kelley, J. E, Jr. (1960). The cutting-plane method for solving convex programs. Journal of the Society for Industrial & Applied Mathematics, 8(4), 703–712.
Latouche, G., & Ramaswai, V., (1999). Introduction to matrix analytic methods in stochastic modeling. SIAM Series on Statistics and Applied Probability.
Marianov, V., & Serra, D. (1998). Probabilistic, maximal covering locationallocation models for congested systems. Journal of Regional Science, 38(3), 401–424.
Marianov, V., & Serra, D. (2002). Location-allocation of multiple-server service centers with constrained queues or waiting times. Annals of Operations Research, 111(1–4), 35–50.
Marín, A. (2011). The discrete facility location problem with balanced allocation of customers. European Journal of Operational Research, 210(1), 27–38.
Murray, J. M. (2003). The canadian triage and acuity scale: A canadian perspective on emergency department triage. Emergency Medicine, 15(1), 6–10.
Nair, R., & Miller-Hooks, E. (2009). Evaluation of relocation strategies for emergency medical service vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2137(1), 63–73.
Neuts, M. F. (1981). Matrix-geometric solutions in stochastic models: An algorithmic approach. Courier Dover Publications.
Ramaswami, V., & Lucantoni, D. M. (1985). Stationary waiting time distribution in queues with phase type service and in quasi-birth-and-death processes. Communications in Statistics. Stochastic Models, 1(2), 125–136.
Rojeski, P., & ReVelle, C. (1970). Central facilities location under an investment constraint. Geographical Analysis, 2(4), 343–360.
Silva, F., & Serra, D. (2008). Locating emergency services with different priorities: The priority queuing covering location problem. Journal of the Operational Research Society, 59(9), 1229–1238.
Stephan, F. F. (1958). Two queues under preemptive priority with poisson arrival and service rates. Operations research, 6(3), 399–418.
Vidyarthi, N., & Jayaswal, S. (2014). Efficient solution of a class of location-allocation problems with stochastic demand and congestion. Computers & Operations Research, 48, 20–30.
Vidyarthi, N., & Kuzgunkaya, O. (2014). The impact of directed choice on the design of preventive healthcare facility network under congestion. Health Care Management Science,. doi:10.1007/s10729-014-9274-2.
Wagner, J., & Falkson, L. (1975). The optimal nodal location of public facilities with price-sensitive demand. Geographical Analysis, 7(1), 69–83.
Wang, Q., Batta, R., & Rump, C. M. (2002). Algorithms for a facility location problem with stochastic customer demand and immobile servers. Annals of Operations Research, 111(1–4), 17–34.
Zhang, Y., Berman, O., & Verter, V. (2012). The impact of client choice on preventive healthcare facility network design. OR spectrum, 34(2), 349–370.
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This research was supported by the Research and Publication Grant, Indian Institute of Management Ahmedabad, provided to the first author.
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Appendix 1: Infinitesimal generator sub-matrices under non-preemptive priority
Appendix 1: Infinitesimal generator sub-matrices under non-preemptive priority
where \(*\) is such that \(A_0 \mathbf {e}\) + \(B_0 \mathbf {e}\) = \(\mathbf {0}\). \(A_1 = B_0 - A_2\).
1.1 Appendix 2: Data
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Jayaswal, S., Vidyarthi, N. Facility location under service level constraints for heterogeneous customers. Ann Oper Res 253, 275–305 (2017). https://doi.org/10.1007/s10479-016-2353-7
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DOI: https://doi.org/10.1007/s10479-016-2353-7