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How do different densities in a network affect the optimal location of service centers?
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0003-4212-8582
Högskolan Dalarna, Akademin Humaniora och medier, Kulturvetenskap.ORCID-id: 0000-0003-4871-833X
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0003-1015-8015
2013 (engelsk)Rapport (Annet vitenskapelig)
Abstract [en]

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. 

sted, utgiver, år, opplag, sider
Borlänge: Högskolan Dalarna , 2013.
Serie
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2013:15
Emneord [en]
location-allocation problem, inter-urban location, intra-urban location, p-median model, network distance, simulated annealing heuristics
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-12606OAI: oai:DiVA.org:du-12606DiVA, id: diva2:627956
Tilgjengelig fra: 2013-06-13 Laget: 2013-06-13 Sist oppdatert: 2019-08-26
Inngår i avhandling
1. Heuristic optimization of the p-median problem and population re-distribution
Åpne denne publikasjonen i ny fane eller vindu >>Heuristic optimization of the p-median problem and population re-distribution
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.  

The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.  

Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

sted, utgiver, år, opplag, sider
Borlänge: Dalarna University, 2013. s. 126
Serie
Dalarna Doctoral Dissertations in Microdata Analysis ; 1
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-13255 (URN)978-91-89020-89-4 (ISBN)
Disputas
2013-11-22, Clas Ohlson, Borlänge, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2013-11-11 Laget: 2013-11-11 Sist oppdatert: 2019-08-26bibliografisk kontrollert

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