Abstract
In this contribution we build on the approach proposed by Zhang et al. (OR Spectrum 34:349–370, 2012) to consider clients’ choice in preventive health care facility location planning. The objective is to maximize the participation in a preventive health care program for early detection of breast cancer in women. In order to account for clients’ choice behavior the multinomial logit model is employed. In this paper, we show that instances up to 20 potential locations and 400 demand points can be easily solved (to optimality or at least close to optimality) by a commercial solver in reasonable time if the problem is modeled by an alternative formulation. We present an intelligible approach to derive a lower bound to the problem. Our paper provides interesting insights into the trade-off between minimum workload requirement (to ensure quality of care) and participation (and thus early diagnosis of disease). We present a general definition of clients’ utility (which allows for clients’ characteristics, for example) and discuss some fundamental issues (and pitfalls) concerning the specification of utility functions.
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References
Achabal D, Gorr W, Mahajan V (1982) Multiloc: a multiple store location decision model. J Retail 58:5–25
Anderson S, de Palma A, Thisse JF (1992) Discrete choice theory of product differentiation. MIT Press, Cambridge
Aros-Vera F, Marianov V, Mitchell JE (2013) p-hub approach for the optimal park-and-ride facility location problem. Eur J Oper Res 226(2):277–285
Ben-Akiva M, Bierlaire M (2003) Discrete choice models with applications to departure time and route choice. In: Hall R (ed) Handbook of transportation science, International series in operations research and management science, Kluwer, pp 7–38
Ben-Akiva M, Lerman S (1985) Discrete choice analysis, theory and application to travel demand. MIT Press, Cambridge
Benati S, Hansen P (2002) The maximum capture problem with random utilities: problem formulation and algorithms. Eur J Oper Res 143:518–530
Berman O, Krass D (2002) Locating multiple competitive facilities: spatial interaction models with variable expenditures. Ann Oper Res 111:197–225
Bierlaire M, Lotan T, Toint P (1997) On the overspecification of multinomial and nested logit models due to alternatives specific constants. Transp Sci 31:363–371
Canadian Institute for Health Information (2011) Wait times in Canada—a comparison by Province, 2011. Technical report, Canadian Institute for Health Information
Carling K, Haansson J (2013) A compelling argument for the gravity p-median model. Eur J Oper Res 226(3):658–660
CPLEX, IBM ILOG (2009) V12. 1: users manual for cplex. Int Bus Mach Corp 46(53):157
Drezner T (1994) Optimal continuous location of a retail facility, facility attractiveness, and market share: an interactive model. J Retail 70:49–64
Haase K (2009) Discrete location planning. Technical Report WP-09-07, Institute for Transport and Logistics Studies, University of Sydney
Haase K, Müller S (2014) A comparison of linear reformulations for multinomial logit choice probabilities in facility location models. Eur J Oper Res 232(3):689–691
Haase K, Müller S (2013) Management of school locations allowing for free school choice. Omega 41(5):847–855
Health Council of Canada (2007) Wading through wait times. what do meaningful reductions and guarantees mean? Published online, http://www.healthcouncilcanada.ca/rpt_det.php?id=127. Accessed 12 Feb 2014
Louviere J, Hensher D, Swait J (2000) Stated choice methods: analysis and applications. Cambridge University Press, Cambridge
Marianov V, Rfos M, Icaza MJ (2008) Facility location for market capture when users rank facilities by shorter travel and waiting times. Eur J Oper Res 191(1):30–42
McCarl B, Meeraus A, van der Eijk P, Bussieck M, Dirkse S, Steacy P (2008) McCarl GAMS user guide. http://gams.com/docs/document.htm
McFadden D (1973) Conditional logit analysis of qualtitative choice behaviour. In: Zarembka P (ed) Frontiers of econometrics. Academic Press, New York, pp 105–142
McFadden D (2001) Economic choices. American economic review 91(3):351–378
Müller S, Haase K (2014) Customer segmentation in retail facility location planning. Business research. Accepted for publication
Müller S, Haase K, Kless S (2009) A multi-period school location planning approach with free school choice. Environ Plan A 41(12):2929–2945
Müller S, Haase K, Seidel F (2012) Exposing unobserved spatial similarity: evidence from German school choice data. Geogr Anal 44:65–86
Müller S, Tscharaktschiew S, Haase K (2008) Travel-to-school mode choice modelling and patterns of school choice in urban areas. J Transp Geogr 16(5):342–357
Perry N, Broeders M, de Wolf C, Toernberg S, Holland R, von Karsa L (2006) European guidelines for quality assurance in breast cancer screening and diagnosis. European communities. Health and consumer protection, 4th edn
Sener I, Pendyala R, Bhat C (2011) Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior. J Transp Geogr 19(2):294–303
Street D, Burgess L (2007) The construction of optimal stated choice experiments. Wiley, New York
Tejada JJ, Ivy JS, King RE, Wilson JR, Ballan MJ, Kay MG, Diehl KM, Yankaskas BC (2014) Combined des/sd model of breast cancer screening for older women, ii: screening-and-treatment simulation. IIE Trans 46(7):707–727
Train K (2003) Discrete choice methods with simulation. MIT Press, Cambridge
Wait Time Alliance (2012) Shedding light on Canadians’ total wait for care—report card on wait times in Canada. Technical report, Wait Time Alliance
Zhang Y, Berman O, Verter V (2012) The impact of client choice on preventive healthcare facility network design. OR Spectr 34:349–370
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Appendix
Appendix
1.1 Model proposed by Zhang et al. (2012)
Additionally to the definitions of Sect. 2.1 we denote the parameters
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\(h_{i}\) fraction of clients at node \(i\),
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\(\lambda \) expected number of clients per period over the entire area (Poisson rate); the Poisson rate of node \(i\) is \(\lambda \cdot h_i\),
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\(\overline{\lambda }_k\) maximum participation rate at a facility with \(k\) servers; \(\overline{\lambda }_0 = 0\),
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\(\Delta \lambda _k\) \(= \overline{\lambda }_k - \overline{\lambda }_{k-1}\),
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\(M_1\) big number; = 1 for example Zhang et al. (2012),
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\(M_2\) big number; = 1 for example Zhang et al. (2012),
as well as
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\(z_{ijo}\) artificial continuous variable for avoiding non-linearity with \(o \in J\); corresponds to the result of \(x_{ij} w_{o1}\).
and the mathematical model (Model Z) corresponding to the original contribution of Zhang et al. (2012)
subject to
1.2 Basics of discrete choice analysis
The MNL is well known for analyzing discrete choice decisions of individuals (McFadden 1973, 2001). Let \(N\) be the set of individuals (customers, clients etc.), \(M\) the choice set (set of alternatives the individual chooses from), and \(L\) the set of attributes or characteristics (attractiveness determinants). The choice set \(M\) must be exhaustive and the alternatives have to be mutually exclusive. Roughly speaking, all alternatives the individuals face have to be included in the choice set. Individual \(n \in N\) chooses exactly one alternative from choice set \(M\). In the discrete choice modeling literature it is assumed that an individual \(n \in N\) chooses alternative \(j\in M\) that maximizes utility (see Train 2003, for example). That is, \(n\) chooses \(j\), iff
The utility \(u_{nj}\) of alternative \(j\) for individual \(n\) consists of a deterministic component \(v_{nj}\) and a stochastic component \(\epsilon _{nj}\), i.e.
Usually, the deterministic component is modeled as a linear function:
where \(c_{njl}\) is the value of attribute \(l\) concerning individual \(n\) and alternative \(j\), and the coefficient \(\beta _{jl}\) is the utility contribution per unit of attribute \(l\) related to alternative \(j\) (Ben-Akiva and Lerman 1985). In applications, \(\beta _{jl}\) have to be estimated (by maximum-likelihood) using choice data from empirical studies (Anderson et al. 1992; Ben-Akiva and Lerman 1985; Louviere et al. 2000; Street and Burgess 2007; Müller et al. 2008; and Train 2003). Since \(u_{nj}\) of (41) is stochastic we can only make probabilistic statements about (40):
Assuming that the stochastic component \(\epsilon _{nj}\) is independent, identically extreme value distributed, the probability (43) that individual \(n\) chooses alternative \(j\) is determined by
which is the well-known MNL (Ben-Akiva and Bierlaire , 2003). Having said this, it is obvious that the MNL of (44) exhibits utility maximization behavior of the choice makers. In other words, using MNL means to assume that clients choose the facility that maximizes their utility (i.e., clients choose the most attractive—“the optimal”—facility). Hence, the underlying choice rule is utility maximization. Note, if \(\epsilon _{nj}=0 \ \forall \ n,j,\) then the choice problem of (40) becomes deterministic. Consider, for example, \(u_{nj}=v_{nj}=-t_{nj}\) with \(t_{nj}\) as the travel time of individual \(n\) to location \(j\). This means that clients wish to obtain services from the facility with the shortest travel time. Obviously, such a choice model is characterized to be deterministic. If we assume that all clients located in \(i \in I\) exhibit the same observable characteristics, then (1) results from (42) and (2) results from (44).
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Haase, K., Müller, S. Insights into clients’ choice in preventive health care facility location planning. OR Spectrum 37, 273–291 (2015). https://doi.org/10.1007/s00291-014-0367-6
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DOI: https://doi.org/10.1007/s00291-014-0367-6