Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14593
Title: GIS and ant algorithm for multi-objective siting of emergency facilities
Authors: LIU NAN
Keywords: Geographical Information System (GIS); Heuristics; Ant Algorithm; Multi-Objective Optimization; Emergency Facility Siting
Issue Date: 30-Apr-2005
Source: LIU NAN (2005-04-30). GIS and ant algorithm for multi-objective siting of emergency facilities. ScholarBank@NUS Repository.
Abstract: This research introduces a generic MO (Multi-Objective) optimization model for emergency facility siting problems in the GIS (Geographical Information System) environment. Without loss of generality, the model is formulated using the I> transformation, which maximizes the minimal achievement level of all the objectives considered. A relevant solution heuristics, an Ant Algorithm which is equipped with a novel two-phase local search measure, has been developed to solve the large scale MO siting problem on a raster data structure. A hypothetical case study of the optimal siting of the proposed fire stations in Singapore has been carried out to test the performance of the methodology developed in this research. In the study, a GIS has been employed as a data management and presentation platform. As compared with an existent GA (Genetic Algorithm) which is the only heuristics available for solving a similar problem, the Ant Algorithm (named as ANT in the case study) outperforms it in terms of both computational accuracy and stability. The ANT algorithm itself has also been thoroughly analyzed through a series of computational experiments, which lead to four findings: (i) the pheromone information contained in the pheromone matrix does help artificial ants find better solutions; (ii) the local search measure proposed in the Ant Algorithm is a better solution method than population-based search heuristics in solving this type of location problems; (iii) the first phase local search, which involves randomness and is typically handled by the ant part, is critical in improving the efficiency of the Ant Algorithm; (iv) the diversion mechanism, the optional component of the Ant Algorithm, may not provide it with an edge in solving this kind of large scale location problems.
URI: http://scholarbank.nus.edu.sg/handle/10635/14593
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