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Meng, X., Carling, K., Håkansson, J. & Rebreyend, P. (2018). How do administrative borders affect accessibility to hospitals? The case of Sweden. International Journal of Health Planning and Management, 33(3)
Open this publication in new window or tab >>How do administrative borders affect accessibility to hospitals? The case of Sweden
2018 (English)In: International Journal of Health Planning and Management, ISSN 0749-6753, E-ISSN 1099-1751, Vol. 33, no 3Article in journal (Refereed) Published
Abstract [en]

An administrative border might hinder the optimal allocation of a given set of resources by restricting the flow of goods, services, and people. In this paper, we address the question: Do administrative borders lead to poor accessibility to public service? In answering the question, we have examined the case of Sweden and its regional administrative borders and hospital accessibility. We have used detailed data on the Swedish road network, its hospitals, and its geo-coded population. We have assessed the population's spatial accessibility to Swedish hospitals by computing the inhabitants' distance to the nearest hospital. We have also elaborated several scenarios ranging from strongly confining regional borders to no confinements of borders and recomputed the accessibility. Our findings imply that administrative borders are only marginally worsening the accessibility.

Keywords
administrative barriers, optimal location, population dynamics, public service, travel time
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27519 (URN)10.1002/hpm.2520 (DOI)000442224700014 ()29667273 (PubMedID)2-s2.0-85045844366 (Scopus ID)
Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2018-09-24Bibliographically approved
Rebreyend, P., Lemarchand, L., Massé, D. & Håkansson, J. (2018). Multiobjective Optimization for Multimode Transportation Problems. Advances in Operations Research, Article ID 8720643.
Open this publication in new window or tab >>Multiobjective Optimization for Multimode Transportation Problems
2018 (English)In: Advances in Operations Research, ISSN 1687-9147, E-ISSN 1687-9155, article id 8720643Article in journal (Refereed) Published
Abstract [en]

We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve it first exactly using an adapted -constraint multiobjective method. Because of the size of the instances considered, we have also explored the use of heuristic methods based on evolutionary multiobjective frameworks, namely, NSGA2 and a modified version of PAES. Those methods are mixed with an original local search technique to provide better results. Numerical comparisons of solutions sets quality are made using the hypervolume metric. Based on the results for test-cases that can be solved exactly, efficient implementation for PAES and NSGA2 allows execution times comparison for large instances. Results show good performances for the heuristic approaches as compared to the exact algorithm for small test-cases. Approximate methods present a scalable behavior on largest problem instances. A master/slave parallelization scheme also helps to reduce execution times significantly for the modified PAES approach.

National Category
Computer Systems Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27779 (URN)10.1155/2018/8720643 (DOI)2-s2.0-85049169081 (Scopus ID)
Note

Open Access APC beslut 30/2017

Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-09-26Bibliographically approved
Meng, X. & Rebreyend, P. (2016). On transforming a road network database to a graph for localization purpose. International Journal of Web Services Research, 13(2), 46-55
Open this publication in new window or tab >>On transforming a road network database to a graph for localization purpose
2016 (English)In: International Journal of Web Services Research, ISSN 1545-7362, E-ISSN 1546-5004, Vol. 13, no 2, p. 46-55Article in journal (Refereed) Published
Abstract [en]

The problems of finding best facility locations require complete and accurate road networks with the corresponding population data in a specific area. However the data obtained from road network databases usually do not fit in this usage. In this paper we propose a procedure of converting the road network database to a road graph which could be used for localization problems. Several challenging problems exist in the transformation process which are commonly met also in other data bases. The procedure of dealing with those challenges are proposed. The data come from the National road data base in Sweden. The graph derived is cleaned, and reduced to a suitable level for localization problems. The residential points are also processed in ordered to match the graph. The reduction of the graph is done maintaining the accuracy of distance measures in the network.

Keywords
road network, graph, population, GIS
National Category
Economic Geography
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-17361 (URN)10.4018/IJWSR.2016040103 (DOI)000384810100004 ()
Available from: 2015-05-07 Created: 2015-05-07 Last updated: 2017-12-04Bibliographically approved
Rebreyend, P., Lemarchand, L. & Euler, R. (2015). A computational comparison of different algorithms for very large p-median problems. In: Gabriela Ochoa, Francisco Chicano (Ed.), Evolutionary Computation in Combinatorial Optimization: 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings. Paper presented at 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015 (pp. 13-24). Springer, 9026
Open this publication in new window or tab >>A computational comparison of different algorithms for very large p-median problems
2015 (English)In: Evolutionary Computation in Combinatorial Optimization: 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings / [ed] Gabriela Ochoa, Francisco Chicano, Springer, 2015, Vol. 9026, p. 13-24Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a new method for solving large scale p-median problem instances based on real data. We compare different approaches in terms of runtime, memory footprint and quality of solutions obtained. In order to test the different methods on real data, we introduce a new benchmark for the p-median problem based on real Swedish data. Because of the size of the problem addressed, up to 1938 candidate nodes, a number of algorithms, both exact and heuristic, are considered. We also propose an improved hybrid version of a genetic algorithm called impGA. Experiments show that impGA behaves as well as other methods for the standard set of medium-size problems taken from Beasley’s benchmark, but produces comparatively good results in terms of quality, runtime and memory footprint on our specific benchmark based on real Swedish data.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9026
National Category
Computer Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-19289 (URN)10.1007/978-3-319-16468-7_2 (DOI)000361701400002 ()978-3-319-16468-7 (ISBN)978-3-319-16467-0 (ISBN)
Conference
15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015
Available from: 2015-09-11 Created: 2015-09-11 Last updated: 2018-01-11Bibliographically approved
Carling, K., Han, M., Håkansson, J. & Rebreyend, P. (2015). Distance measure and the p-median problem in rural areas. Annals of Operations Research, 226(1), 89-99
Open this publication in new window or tab >>Distance measure and the p-median problem in rural areas
2015 (English)In: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 226, no 1, p. 89-99Article in journal (Refereed) Published
Abstract [en]

The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances with the Euclidean in a rural region with a sparse, heterogeneous network and a non-symmetric distribution of the population. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (1-100 facilities). We find that the network distance give as good a solution as the travel-time network. The Euclidean distance gives solutions some 4-10 per cent worse than the network distances, and the solutions tend to deteriorate with increasing P. Our conclusions extend to intra-urban location problems.

Place, publisher, year, edition, pages
Springer, 2015
Keywords
dense network, location model, optimal location, simulated annealing, travel-time, urban areas
National Category
Transport Systems and Logistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods; Complex Systems – Microdata Analysis, General Microdata Analysis - transports
Identifiers
urn:nbn:se:du-14682 (URN)10.1007/s10479-014-1677-4 (DOI)000349852400005 ()
Available from: 2014-07-18 Created: 2014-07-18 Last updated: 2019-08-26Bibliographically approved
Zhao, X., Rebreyend, P. & Håkansson, J. (2015). Does road network density matter in optimally locating facilities?.
Open this publication in new window or tab >>Does road network density matter in optimally locating facilities?
2015 (English)Report (Other (popular science, discussion, etc.))
Abstract [en]

Optimal location on the transport infrastructure is the preferable requirement for many decision making processes. Most studies have focused on evaluating performances of optimally locate p facilities by minimizing their distances to a geographically distributed demand (n) when p and n vary. The optimal locations are also sensitive to geographical context such as road network, especially when they are asymmetrically distributed in the plane. The influence of alternating road network density is however not a very well-studied problem especially when it is applied in a real world context. This paper aims to investigate how the density level of the road network affects finding optimal location by solving the specific case of p-median location problem. A denser network is found needed when a higher number of facilities are to locate. The best solution will not always be obtained in the most detailed network but in a middle density level. The solutions do not further improve or improve insignificantly as the density exceeds 12,000 nodes, some solutions even deteriorate. The hierarchy of the different densities of network can be used according to location and transportation purposes and increase the efficiency of heuristic methods. The method in this study can be applied to other location-allocation problem in transportation analysis where the road network density can be differentiated. 

Publisher
p. 14
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2015:10
Keywords
Road network; Density; p – median model; CPLEX; Heuristics
National Category
Computer and Information Sciences Human Geography
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-19079 (URN)
Available from: 2015-08-21 Created: 2015-08-21 Last updated: 2018-01-11Bibliographically approved
Zhao, X., Rebreyend, P. & Håkansson, J. (2015). How does the complexity of a road network affect optimal facility locations?. Borlänge: Högskolan Dalarna
Open this publication in new window or tab >>How does the complexity of a road network affect optimal facility locations?
2015 (English)Report (Other academic)
Abstract [en]

The road network is a necessary component in transportation. It facilitiesspatial movements of people and goods, and it also influences the optimal locations of facilities that usually serve as destinations of the movements. To fulfill the transportation needs and to adapt to the facility development, the road network is often organized hierarchically and asymmetrically with various road levels and spatial structures. The complexity of the road network increases along with the increase of road levels and spatial structures. However, location models locate facilities on a given road network, usually the most complex one, and the influence from the complexity of road network in finding optimal locations is not well-studied. This paper aims to investigate how the complexity of a road network affects the optimal facility locations by applying the widely-applied p-median model. The main result indicates that an increase in road network complexity, up to a certain level, can obviously improve the solution, and the complexity beyond that level does not always lead to better solutions. Furthermore, the result is not sensitive to the choice of algorithms. In a specific case study, a detailed sensitivity analysis of algorithm and facility number further provides insight into computation complexity and location problems from intra-urban to inter-urban.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2015. p. 19
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2015:10
Keywords
Transportation system; Spatial optimization; Location models; Heuristics
National Category
Social Sciences Computer Sciences
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - transports
Identifiers
urn:nbn:se:du-24682 (URN)
Note

New updated version of paper.

Available from: 2017-04-04 Created: 2017-04-04 Last updated: 2018-01-13Bibliographically approved
Carling, K., Han, M., Håkansson, J. & Rebreyend, P. (2015). Testing the gravity p-median model empirically. Operations Research Perspectives, 2(124), Article ID 132.
Open this publication in new window or tab >>Testing the gravity p-median model empirically
2015 (English)In: Operations Research Perspectives, ISSN 2214-7160, Vol. 2, no 124, article id 132Article in journal (Refereed) Published
Abstract [en]

Regarding the location of a facility, the presumption in the widely used p-median model is that the customer opts for the shortest route to the nearest facility. However, this assumption is problematic on free markets since the customer is presumed to gravitate to a facility by the distance to and the attractiveness of it. The recently introduced gravity p-median model offers an extension to the p-median model that account for this. The model is therefore potentially interesting, although it has not yet been implemented and tested empirically. In this paper, we have implemented the model in an empirical problem of locating vehicle inspections, locksmiths, and retail stores of vehicle spare-parts for the purpose of investigating its superiority to the p-median model. We found, however, the gravity p-median model to be of limited use for the problem of locating facilities as it either gives solutions similar to the p-median model, or it gives unstable solutions due to a non-concave objective function.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
p-median model, distance decay, market share, network, retail, simulated annealing, travel time
National Category
Discrete Mathematics Economic Geography
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - retail; Complex Systems – Microdata Analysis, General Microdata Analysis - methods; Complex Systems – Microdata Analysis, General Microdata Analysis - transports
Identifiers
urn:nbn:se:du-17767 (URN)10.1016/j.orp.2015.06.002 (DOI)
Funder
Swedish Retail and Wholesale Development Council
Available from: 2015-06-10 Created: 2015-06-10 Last updated: 2019-08-26Bibliographically approved
Meng, X. & Rebreyend, P. (2014). From the road network database to a graph for localization purposes. Borlänge: Högskolan Dalarna
Open this publication in new window or tab >>From the road network database to a graph for localization purposes
2014 (English)Report (Other academic)
Abstract [en]

The problems of finding best facility locations require complete and accurate road network with the corresponding population data in a specific area. However the data obtained in road network databases usually do not fit in this usage. In this paper we propose our procedure of converting the road network database to a road graph which could be used in localization problems. The road network data come from the National road data base in Sweden. The graph derived is cleaned, and reduced to a suitable level for localization problems. The population points are also processed in ordered to match with that graph. The reduction of the graph is done maintaining most of the accuracy for distance measures in the network.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2014
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2014:09
Keywords
road network, graph, population, GIS
National Category
Computer and Information Sciences
Research subject
Komplexa system - mikrodataanalys
Identifiers
urn:nbn:se:du-14066 (URN)
Available from: 2014-05-05 Created: 2014-05-05 Last updated: 2018-01-11Bibliographically approved
Han, M., Håkansson, J. & Rebreyend, P. (2014). How does data quality in a network affect heuristic solutions?. Borlänge: Högskolan Dalarna
Open this publication in new window or tab >>How does data quality in a network affect heuristic solutions?
2014 (English)Report (Other academic)
Abstract [en]

To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly used

p-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p).

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 supply points 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 (which is aggregated from the 1.5 million nodes). 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 the accuracy in the road network increase and the combinatorial problem (low

p) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2014. p. 19
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2014:08
Keywords
complex networks, p-median model, simulated annealing heuristics
National Category
Other Social Sciences not elsewhere specified
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-14054 (URN)
Available from: 2014-04-29 Created: 2014-04-29 Last updated: 2019-08-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1015-8015

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