https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23127 Thu 12 Apr 2018 13:43:58 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:18194 Sat 24 Mar 2018 08:04:22 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:30707 Sat 24 Mar 2018 07:34:58 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23128 ij). Note that each row (column) of D is associated with a demand (facility) location. We say that column k dominates column l if d_ik ≤ d_il for all i ≠ k . We use the term strongly dominates in the case of strict inequalities. Observe that locating a facility at a dominated location l would provide no advantage to locating a facility at k except possibly in serving the demands of customers in location l. Further, strongly dominated columns would only be used for ‘self-serve’. Consequently, dominated column can be dropped to generate a feasible solution and the location can later be considered as a possible ‘self-service’ facility. We extend the concept of dominance somewhat further as follows. We say columns k and l dominate column j if d_ij ≤min{d_ik, d_il} for all i ≠ j . In this case there is no advantage in using location j (except for serving customers in location j) when locations k and l are used. So again we can drop the dominated column j if columns k and l are used. The term strongly is used as before. We further extend this concept of dominance as follows. We say that column k partially dominates column l if d_ik ≤ d_il for at least half or more of the entries for which i ≠ k . Similarly, we say columns k and l partially dominate column j if d_ij ≥ min{d_ik, d_il} for at least half or more of the entries for which i ≠ j. Partially dominated columns correspond to nodes which may be assigned ‘self-serve’ facilities in the original and the reduced matrix. In this paper, we developed a new greedy algorithm based on a concept known as dominance to obtain solutions for the p-median problem. This concept reduces the number of columns of a distance matrix by considering potential facilities that are near and those that are far from the population or demand. We illustrate our ideas and the algorithm with an example. We further applied the new algorithm to effectively locate additional ambulance stations in the Central and South East metropolitan areas of Perth to complement the existing ones. We also compare the performance of our new Greedy Reduction Algorithm (GRA) with the existing greedy algorithm of the p-median problem.]]> Sat 24 Mar 2018 07:16:36 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40427 p-center problem which addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category refers to those designed to minimize the average weighted distance or time. This objective leads to a location problem known as the p-median problem. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The objective of this study is to discuss the importance of the application of optimization models (maximal covering location and the p-median models) to locate health care facilities. We apply the p-median models and the maximal covering location models to real data from Mackay metropolitan area in Queensland, Australia. We compare the two models using the real data and with existing ambulance stations. The study shows that the p-median model gives a better solution than the maximal covering location model. We also noted that the results of the maximal covering location model depend on the pre-determined weighted coverage distance.]]> Fri 22 Jul 2022 14:30:27 AEST ]]>