Policy decision makers at global and local levels develop plans to improve public readiness for emergency response scenarios that might require evaluation of trade offs due to either insufficient capacity or practicality constraints. These problems are also computationally difficult since they are formulated as large scale service network design models and involve facility location-allocation decisions, which are already known as NP-hard. Heuristic methods can help finding practically feasible solutions but often decision makers may sacrifice in one objective while they aim to satisfy multiple objectives. This paper formulates the location-allocation problem as a p-median problem that includes capacity and time constraints and it aims to minimize the time for individuals to receive service. In addition, we discuss changes to the model if some of the original hard constraints must be relaxed because of resource scarcity. Due to the complexity to solve the problem for large scale scenarios, we propose a set of algorithms to relax capacity and/or time. In particular, we propose an algorithm that relaxes both types of constraints simultaneously. The results show how decision makers can incentivize using one type of resource and when is better to balance adding more capacity or service time. In addition, we analyze how the resource allocation strategy interacts with the number of open sites. With this approach, decision makers can expand their choices to design a service network and even force the algorithm to return solutions that aligns with their preferences.

Adrián Ramírez Nafarrate es profesor asociado en el Departamento Académico de Ingeniería Industrial y Operaciones del ITAM y es Nivel 2 del Sistema Nacional de Investigadores de CONACYT. Adrián recibió el grado de doctor en filosofía en ingeniería industrial por Arizona State University, también es maestro en ciencias en sistemas de manufactura por el ITESM Campus Monterrey y obtuvo la licenciatura en ingeniería industrial y de sistemas por la Universidad de Sonora. Ha trabajado para compañías e instituciones como Motorola, Lear Corporation, Universidad de Sonora y el Decision Theater de Arizona State University. Además, ha participado en proyectos de diseño y mejora de procesos para Chedraui, Arizona Department of Health Services, Mayo Clinic y Ford Motor Company. Adrián obtuvo el premio al mejor artículo de estudiante doctoral en Winter Simulation Conference 2010. Sus artículos han sido publicados en revistas arbitradas como Journal of Business Research, European Journal of Operational Research, IIE Transactions on Healthcare Systems Engineering, Production and Operations Management y Annals of Operations Research. Además, es miembro del International Activities Committee de INFORMS y representante de SMIO ante ALIO y ante el Comité de Países en Desarrollo de IFORS. Adrián ha impartido clases y seminarios en diversas instituciones de los Estados Unidos y México. Sus intereses de investigación incluyen la modelación y optimización de procesos de manufactura y servicios.