Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

Abstract

This paper presents an integrated location-allocation model balancing efficiency and equity criteria. The new formulation combines two domains: facility location and data envelopment analysis. To support the decision maker with more realistic solutions based on the optimal location-allocation decisions, we endogenize the outputs of the model as a function dependent on the allocation variables. To illustrate the viability of the proposed approach, we investigated the potential application of the model to the design of an emergency medical service system.

Emergency medical services and beyond: Addressing new challenges through a wide literature review

“…By this definition, EMS systems are equitable if they favor disadvantaged groups, compensating for overall inequities. Horizontal equity has been researched extensively in health economics, while vertical inequity is receiving growing attention in the last years [16].…”

A multiobjective, maximal conditional covering location problem applied to the relocation of hierarchical emergency response facilities

“…The most common of these problems balance maximizing some measure of capability with minimizing cost [16,17,18,19,20,21]. Other notable bi-objective approaches include balancing coverage with distance [22,23], coverage with equity [24,25], or coverage efficiency with coverage equity [26]. For a thorough review of multiobjective facility location problems, we refer the reader to a robust survey conducted by Farahani et al [27] that applies taxonomies for both the number and types of objectives considered within the respective works, a notable subset of which address EMS location problems.…”

“…The weighting method involves parametric scaling of each objective in accordance with its relative importance and evaluating the corresponding objective function for a set of weights [33]. Among the various scalarization techniques, the ε-constraint method is more often utilized within recent multiobjective facilitation location literature (e.g., see [21,24,26,29,34,35]). The ε-constraint method involves iteratively optimizing one objective after converting the remaining objectives into constraints, for which the right-hand side (RHS) is parametrically changed to identify a set of non-inferior solutions [35].…”

“…The ε-constraint method is often preferred over the weighting method in integer programming formulations because the non-convex feasible region prevents the weighting method from finding all non-inferior solutions. For this reason, the use of the weighted sum method in the literature is less frequent (e.g., see [22,28]), and several authors have sought to improve upon it via the use of fuzzy set optimization [17], or evolutionary algorithms [19,37], optimizing a ratio of two objectives [26], or a combination of the weighted sum and ε-constraint methods [20]. Herein, we embrace the use of the ε-constraint method for its simplicity and, for our formulation in which one of the objectives assumes only discrete values, its ease of identifying appropriate values for ε to identify all non-inferior solutions.…”

Models, algorithms and applications for location problems

“…Khodaparasti et al [8] present an integrated location-allocation model balancing efficiency and equity criteria combining facility location analysis and data envelopment analysis. To illustrate the viability of the proposed approach, the authors investigate the potential application of the model to the design of an emergency medical service system.…”