Facility location under service level constraints for heterogeneous customers
- 378 Downloads
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.
KeywordsFacility location Congestion Service level Priority queue Cutting plane
This research was supported by the Research and Publication Grant, Indian Institute of Management Ahmedabad, provided to the first author.
- 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.Google Scholar
- 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.Google Scholar
- Berman, O., & Krass, D. (2015). Stochastic location models with congestion. Location science (pp. 443–486). Berlin: Springer.Google Scholar
- 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.Google Scholar
- Jayaswal, S. (2009). Product differentiation and operations strategy for price and time sensitive markets. PhD thesis. Ontario: Department of Management Sciences, University of Waterloo.Google Scholar
- Latouche, G., & Ramaswai, V., (1999). Introduction to matrix analytic methods in stochastic modeling. SIAM Series on Statistics and Applied Probability.Google Scholar
- Neuts, M. F. (1981). Matrix-geometric solutions in stochastic models: An algorithmic approach. Courier Dover Publications.Google Scholar