Accurate estimation of expected coverage: revisited

As noted in several studies (Batta et al., Transp. Sci. 23 (1989) 277), (Burwell et al., Comput. Opns. Res. 20 (1993) 113), (Daskin, Network and Discrete Location, Wiley, New York, 1995), (Marianov and ReVelle, Eur. J. Opns. Res. 93 (1996) 110), (Saydam et al., Socio-Econ. Plann. Sci. 28(2) (1994) 113), the accurate estimation of expected coverage is an important and open issue. Although the maximum expected coverage model is empirically shown to prescribe a robust set of “optimal” locations, earlier findings suggest that it could also over or underestimate the coverage by a significant margin. In this study, we present a genetic algorithm (GA) that combines the expected coverage approach with the hypercube model (Jarvis, Mgmt. Sci. 31 (1985) 235), (Larson, Comput. Opns. Res. 1 (1974) 67), (Larson, Opns. Res. 23 (1975) 845) to solve the maximum expected coverage location problem with increased accuracy and realism. Our findings suggest that the GA provides at least as good solutions 94% of the time making it a viable alternative to the two-step procedures stipulated earlier.