How do different densities in a network affect the optimal location of service centers?
2013 (English)Report (Other academic)
The p-median problem is often used to locate p service centers by minimizing their distances to a geographically distributed demand (n). The optimal locations are sensitive to geographical context such as road network and demand points especially when they are asymmetrically distributed in the plane. Most studies focus on evaluating performances of the p-median model when p and n vary. To our knowledge this is not a very well-studied problem when the road network is alternated especially when it is applied in a real world context. The aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the density in the road network is alternated. The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 service centers we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000. To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when nodes in the road network increase and p is low. When p is high the improvements are larger. The results also show that choice of the best network depends on p. The larger p the larger density of the network is needed.
Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna , 2013.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2013:15
location-allocation problem, inter-urban location, intra-urban location, p-median model, network distance, simulated annealing heuristics
Probability Theory and Statistics
Research subject Komplexa system - mikrodataanalys
IdentifiersURN: urn:nbn:se:du-12606OAI: oai:DiVA.org:du-12606DiVA: diva2:627956