Sunarin Chanta

Joint location and dispatching decisions for Emergency Medical Services

The main purpose of Emergency Medical Service systems is to save lives by providing quick response to emergencies. The performance of these systems is affected by the location of the ambulances and their allocation to the customers. Previous literature has suggested that simultaneously making location and dispatching decisions could potentially improve some performance measures, such as response times. We developed a mathematical formulation that combines an integer programming model representing location and dispatching decisions, with a hypercube model representing the queuing elements and congestion phenomena. Dispatching decisions are modeled as a fixed priority list for each customer. Due to the models complexity, we developed an optimization framework based on Genetic Algorithms. Our results show that minimization of response time and maximization of coverage can be achieved by the commonly used closest dispatching rule. In addition, solutions with minimum response time also yield good values of expected coverage. The optimization framework was able to consistently obtain the best solutions (compared to enumeration procedures), making it suitable to attempt the optimization of alternative optimization criteria. We illustrate the potential benefit of the joint approach by using a fairness performance indicator. We conclude that the joint approach can give insights of the implicit trade-offs between several conflicting optimization criteria.

Improving emergency service in rural areas: a bi-objective covering location model for EMS systems

Emergency medical service (EMS) systems are public services that often provide the first line of response to urgent health care needs within a community. Unfortunately, it has been widely documented that large disparities in access to care exist between rural and urban communities. While rural EMS is provided through a variety of resources (e.g. air ambulances, volunteer corps, etc.), in this paper we focus on ground ambulatory care. In particular our goal is to balance the level of first-response ambulatory service provided to patients in urban and rural areas by locating ambulances at appropriate stations. In traditional covering location models the objective is to maximize demand that can be covered; consequently, these models favor locating ambulances in more densely populated areas, resulting in longer response times for patients in more rural areas. To address the issue of fairness in semi-rural/semi-urban communities, we propose three bi-objective covering location models that directly consider fairness via a secondary objective. Results are discussed and compared which provide a menu of alternatives to policy makers.

The minimum p-envy location problem: a new model for equitable distribution of emergency resources

Equity is an important consideration in public services such as Emergency Medical Service (EMS) systems. In such systems not only equitability but also performance depends on the spatial distribution of facilities and resources. This paper proposes the minimum p-envy facility location model which aims to find optimal locations for facilities in order to balance customers’ perceptions of equity in receiving service. The model is developed and evaluated through the lens of EMS systems, where ambulances are located at facilities (stations) with the objective of minimizing the sum of “envy” among all demand zones (customer points) with respect to an ordered set of p operating stations weighted by the proportion of demand in each zone. The problem is formulated as an integer program, with priority weights assigned according the probability that an ambulance is available, which is estimated using the hypercube model. Because of the computational effort required to obtain solutions using commercially available software, a tabu search is developed to solve the problem. A case study using real-world data is presented. The performance of the proposed model is tested and compared to other location models such as the p-center and maximal-covering-location problems (MCLP).

The minimum p-envy location problem with requirement on minimum survival rate

In location problems for the public sector such as emergency medical service (EMS) systems, the issue of equity is an important factor for facility design. Several measures have been proposed to minimize inequity of a system. This paper considers an extension to the minimum p-envy location model by evaluating the objective of the model based on a survival function instead of on a distance function since survival probability is directly related to patient outcomes with a constraint on minimum survival rate. The model was tested on a real world data set from the EMS system at Hanover County, VA, and also compared to other location models. The results indicate that, not only does the enhanced p-envy model reduce inequity but we also find that more lives can be saved by using the survival function objective. A sensitivity analysis on different quality of service measures (survival probability and traveled distance) and different choices of priority assigned to serving facility is discussed.

A Single Allocation P-Hub Maximal Covering Model for Optimizing Railway Station Location

In this paper, we propose an optimization model for determining locations of railway stations. The objective is to maximize the covered the number of expected passengers that can be covered. We focus on the case study of locating optimal stations of high speed train on the north route railway line of Thailand, which is on the government plan to be built. We considered the number of expected passengers based on the real passengers traveling during year on the line. Two types of coverage are considered, which are the condition on time to go to a station and the condition on total travelling time. To solve this problem, we developed Simulated Annealing. Computational results showed that the proposed metaheuristic algorithm found the high quality solutions in reasonable time.

A Multi-Period Evacuation Vehicle Routing Problem Model

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.