Hospital Cardio Vascular people People with serious Respiratory problem Acute Care Center(ACC) People with Respiratory problems Cooling Center People who are suffering from Dehydration Emergency Shelter Location and Resource Allocation S. Ghorbani, M. Baykal-Gürsoy, P. Kazemian, E. Boros and N. Fefferman Industrial & Systems Engineering Department Rutgers, The State University of New Jersey Sara Ghorbani, Melike Baykal-Gursoy, Pooyan Kazemian, Extreme Weather Events Any weather event that might lead to a catastrophic situation affecting human life. Research shows an increasing trend: Hurricane Floyd 1999, Katrina 2005, like 2008 Heat wave in Chicago 1995 and France 2003 Blackouts due to extreme heat or cold and rising electricity consumption References for Heat Risk Index To design an efficient plan for the city of Newark in the case of a heat event we utilize the following data: Identification of areas as blocks Population size and age groups living in each block Existing facilities: Hospitals, Acute Care centers, and Cooling centers (Churches, Schools, Libraries, …) Road maps with distances Results Mathematical Model I Elderly Morbidly obese with assistance needs Patients who are sustained at home using medical equipments Input data from GIS  Joint Location/Allocation and Supply Management problem  Oh and Haghani, 1996  Yi and Ozdamar, 2006  Griffin et al., 2007 We are going to present a model which assigns people to the health centers based on their medical needs associated with the demographic data. We are going to estimate the number of people in need by utilizing the mortality information. Xpress-MP is utilized to solve these models for 100 blocks of population and 5 hospitals as well as 10 candidates for cooling centers in Newark. The results is as follows: Result for Model Result for Model II Results show that bringing number of deaths to attention significantly affects the problem solution and results in setting up more cooling center. Vulnerable Population People are categorized to four groups based on their health problems: Cardio Vascular Respiratory Dehydration N/A Health Groups Heat Risk Index Risk index helps us to estimate the number of people at risk for each group I k = Number of people in need of medical care for group k B k = Baseline “bad outcome” for percent change in death per 1˚C increase in temperature taken from “normal” rates of hospitalization during non-heat events = Increase in temperature (degrees of Fahrenheit) C k = Number of deaths in the normal condition in the hospitals for group k Objective Assignment Policy Mathematical Model II Literature Survey & Contribution Two mathematical assignment models were proposed for a heat wave problem with GIS based data. Results show that mortality factor is so important and affects the assignment results. This issue is more highlighted when we want to solve the problem for the entire city of Newark. Conclusion Conti et al., 2007, “General and specific mortality among the elderly during the 2003 heat wave in Genoa (Italy)” Knowlton et al., 2009, “The 2006 California Heat Wave: Impacts on Hospitalizations and Emergency Department Visits” Basu & Ostro, 2009, “Multi-County Analysis Identifying The Vulnerable Population for Mortality Associated with High Ambient Temperature in California” Rutgers University Academic Excellence Fund Health Care Centers PeopleTriage Hospital ACC Cooling  Location/ Allocation problem of vulnerable populations into health care centers in case of a heat event in order to minimize the total distance traveled subject to a constraint on the number of possible deaths. x ijk : The coverage percentage of people type k from block i by center j, pop ik : Number of type k people living in block-building i, cap j : Number of patients that can be accommodated in center j, (For potential locations, this capacity is the estimated capacity if a new shelter is built at that place) d ij : The transportation cost to go from block i to center j, j = 1,…, J + M, v kj : 0 If center j can provide the appropriate treatment for patient type k & 1 Otherwise. Parameters Decision Variables Objective function Subject to: We consider an imaginary center with a very big numbers for distance parameter and capacity which refers to people left at home due to lack of enough capacity in the health centers. The coverage constraint The capacity constraint The constraint on the average number of death i = set of blocks = {1,2,…,100} j = set of cooling centers = {1,2,…,10} pop i = population of block i d ij = distance between block i and cooling center j 1 if block i is assigned to cooling center j 0 O.W. 1 If cooling center is selected as a triage 0 O.W. Min z = X ij,u j binary