Modeling data envelopment analysis (DEA) efficient location/allocation decisions
Introduction The initial focus of mathematical programming models for facility location was based upon the spatial availability of a server to fulfill demand [for a review of covering models, see ReVelle (1987) and Schilling et al. (1993)]. Toregas et al. (1971) formulated the Location Set Covering Problem (LSCP) that required coverage of all demand points within some time standard. A derivative of this was the p-center problem, which minimized the maximum distance separating any demand point from its nearest facility (Hakimi 1964, ReVelle and Swain 1970). Both of these models considered demand, server capability, and service time to be homogeneous. Budgetary constraints, which might make full coverage impossible, led to the development of the Maximal Covering Location Problem (MCLP) (Church and ReVelle 1974, White and Case 1974). Non-uniform distribution of demand nodes was incorporated into these partial covering models to develop the best deployment of a given number of facilities.
The Supply Chain in Manufacturing, Distribution, and Transportation: Modeling, Optimization, and Applications
Klimberg, Ronald K.; Ratick, Samuel J.; Tavva, Vinay; Vuyyuru, Sasanka; and Mrazik, Daniel, "Modeling data envelopment analysis (DEA) efficient location/allocation decisions" (2010). International Development, Community, and Environment. 468.