Maximal service area problem for optimal siting of emergency facilities

Abstract

Geographic information systems (GIS) have been integrated to many applications in facility location problems today. However, there are still some GIS capabilities yet to be explored thoroughly. This study utilizes the capability of GIS to generate service areas as the travel time zones in a facility location model called the maximal service area problem (MSAP). The model is addressed to emergency facilities for which accessibility is an important requirement. The objective of the MSAP is to maximize the total service area of a specified number of facilities. In the MSAP, continuous space is deemed as the demand area, thus the optimality was measured by how large the area could be served by a set of facilities. Fire stations in South Jakarta, Indonesia, were chosen as a case study. Three heuristics, genetic algorithm (GA), tabu search (TS) and simulated annealing (SA), were applied to solve the optimization problem of the MSAP. The final output of the study shows that the three heuristics managed to provide better coverage than the existing coverage with the same number of fire stations within the same travel time. GA reached 82.95% coverage in 50.60 min, TS did 83.20% in 3.73 min, and SA did 80.17% in 52.42 min, while the existing coverage only reaches 73.82%.

Open space suitability analysis for emergency shelter after an earthquake

“…The model follows some basic assumptions that are only applicable in rural areas where transportation friction can be modeled as a result of land cover and distance only. Indriasari et al (2010) use a similar approach to identify the optimal siting of emergency facilities like fire brigades or hospitals. They argue that maximum coverage is more applicable for identifying suitable emergency facilities among a larger set of candidate sites than methods minimizing the distance between demand and supply.…”

“…For example, the SPHERE Project provides minimum standards and general guidance for use in any of several response scenarios and includes provisions for strategic planning, settlement planning, covering living space, construction, and environmental impact for shelter and settlements (SPHERE Project, 2011). While the minimum standards provide the basis for developing an emergency shelter placement plan, optimal siting and accessibility of shelter sites based on shelter needs from comprehensive risk assessments are also required (Indriasari et al, 2010). There is still a lack of combined approaches to investigate demand for public emergency shelter sites with their suitability and accessibility incorporating capacity constraints of (candidate) shelter sites.…”

Location Optimization Using a Hierarchical Location-Allocation Model for Trauma Centers in Shenzhen, China

“…The model calculates the minimum number of required ambulances for each demand that can access a certain area within a specified coverage time. Indriasari et al [24] presented a Maximal Service Area Model that is aimed at maximizing the total service area of a specified number of facilities to optimally locate emergency facilities. The model was applied to solve the location problem of fire stations in South Jakarta, Indonesia.…”

Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

“…In order to overcome these limitations of exact methods, some scholars have resorted to a number of heuristic approaches to solve geographical optimization problems effectively and efficiently (Xiao 2008;Tong and Murray 2012). Among various modern heuristic approaches, evolutionary algorithms (e.g., GA) have shown great promise for generating solutions to large-scale ERFLs problem (Jia et al 2007b;Indriasari et al 2010;Mohammadi et al 2010). When GA is applied to solve multi-objective optimization problems, however, the scalar fitness information should be provided to combine multiple objectives into a single objective by using aggregating approaches (e.g., weighted sum approach, goal programming, etc.)…”

“…Research on facility location problem is abundant, and many decision models have been developed to solve various facility location problems, including the ones in the context of regular emergency services (e.g., fire stations, medical centers, etc.) (Chrissis 1980;Revelle and Snyder 1995;Liu et al 2006;Alçada-Almeida et al 2009;Indriasari et al 2010;Maliszewski and Horner 2010), as well as the ones for large-scale disasters emergency services (e.g., earthquakes, floods, hurricanes, etc.) (Jia et al 2007a(Jia et al , 2007bHorner and Downs 2010;Bell et al 2011;Caunhye et al 2012;Maliszewski et al 2012).…”