Improving Accessibility and Efficiency of Service Facility through Location-Based Approach: A Case Study at Narvik University College

Abstract:

Location problem of service facility has never lost its appeal to both academics and practitioners due to the complexity in balancing availability, responsiveness and efficiency. In this paper, a location-based study is performed in order to improve the accessibility of service facility in terms of availability and responsiveness for customers as well as the efficiency for service providers. This study employs two well-known location models for service facility: Maximal covering location model which aims to maximize the coverage of customer demands with limited number of facilities (efficiency) and p-median location model which aims to minimize the overall distance travelled from customs to service facilities (accessibility), and location-based comparison of the two solutions in a case study at the 3^rd floor of the main building of Narvik University College (NUC) for improving the overall performance of printing service is conducted so as to illustrates a deep insight of real-world application. The optimal solutions for maximizing the overall performance are obtained under different scenarios, and Lingo software is applied for resolving the computational optimization problems.

• Periodical: Advanced Materials Research (Volume 1039)
• Pages: 593-602
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1039.593
• Online since: October 2014
• Authors: Hao Yu, Wei Deng Solvang*, Jin Guang Yang
• Keywords: Accessibility, Efficiency, Maximal Covering Location Model, P-Mediam Location Model, Service Facility Location

References

[1] J. M. Lee, Y. H. Lee, Facility location and scale decision problem with customer preference, Computers & Industrial Engineering. 63 (2012) 184-191.

DOI: 10.1016/j.cie.2012.02.005

[2] C. S. ReVelle, H. A. Eiselt, Locatioin analysis: A synthesis and survey, European Journal of Operational Research. 165 (2005) 1-19.

[3] M. S. Daskin, What you should know about location modeling, Naval Research Logistics. 55 (2008) 283-294.

DOI: 10.1002/nav.20284

[4] R. Z. Farahani, M. SteadieSeifi, N. Asgari, Multiple criteria facility location problems: A survey, Applied Mathematical Modelling. 34 (2010) 1689-1709.

DOI: 10.1016/j.apm.2009.10.005

[5] R. Z. Farahani, N. Asgari, N. Heidari, M. Hosseininia, M. Goh, Covering problems in facility location: A review, Computers & Industrial Engineering. 62 (2012) 368-407.

DOI: 10.1016/j.cie.2011.08.020

[6] A. B. Arabani, R. Z. Farahani, Facility location dynamics: An overview of classifications and applications, Computers & Industrial Engineering. 62 (2012) 408-420.

DOI: 10.1016/j.cie.2011.09.018

[7] R. Z. Farahani, M. Hekmatfar, B. Fahimnia, N. Kazemzadeh, Hierarchical facility location problem: Models, classifications, techniques, and applications, Computers & Industrial Engineering. 68 (2014) 104-117.

DOI: 10.1016/j.cie.2013.12.005

[8] R. Batta, M. Lejeune, S. Prasad, Public facility location using dispersion, population, and equity criteria, European Journal of Operational Research. 234(3) (2014) 819-829.

DOI: 10.1016/j.ejor.2013.10.032

[9] A. T. Murray, R. A. Gerrard, Capacitated service and regional constraints in location-allocation modeling, Location Science. 5(2) (1997) 103-118.

DOI: 10.1016/s0966-8349(97)00016-8

[10] W. Khamjan, S. Khamjan, S. Pathumnakul, Determination of the locations and capacities of sugar cane loading stations in Thailand, Computers & Industrial Engineering. 66 (2013) 663-674.

DOI: 10.1016/j.cie.2013.09.006

[11] N. Saidani, F. Chu, H. Chen, Competitive facility location and design with reactions of competitors already in the market, European Journal of Operational Research. 219 (2012) 9-17.

DOI: 10.1016/j.ejor.2011.12.017

[12] M. Albareda-Sambola, E. Fernandez, Y. Hinojosa, J. Puerto, The multi-period incremental service facility location problem, Computers & Operations Research. 36 (2009) 1356-1375.

DOI: 10.1016/j.cor.2008.02.010

[13] S. S. Radiah Shariff, N. H. Moin, M. Omar, Location allocation modeling for healthcare facility planning in Malaysia, Computers & Industrial Engineering. 62 (2012) 1000-1010.

DOI: 10.1016/j.cie.2011.12.026

[14] M. Ndiaye, H. Alfares, Modeling health care facility location for moving population groups, Computers & Operations Research. 35 (2008) 2154-2161.

DOI: 10.1016/j.cor.2006.09.025

[15] H. Toro-Diaz, M. E. Mayorga, S. Chanta, L. A. Mclay, Joint location and dispatching decisions for emergency medical services, Computers & Industrial Engineering. 64 (2013) 917-928.

DOI: 10.1016/j.cie.2013.01.002

[16] X. Tang, J. Zhang, P. Xu, A multi-objective optimization model for sustainable logistics facility location, Transportation Research Part D. 22 (2013) 45-48.

DOI: 10.1016/j.trd.2013.03.003

[17] H. Yu, W. D. Solvang, A reverse logistics network design model for sustainable treatment of multi-sourced waste of electrical and electronic equitpments (WEEE), Proceeding of 4th IEEE International Conference on Cognitive Infocommunications, Budapest, 2013. pp: 595-600.

DOI: 10.1109/coginfocom.2013.6719172

[18] W. D. Solvang, R. Elisabeth, H. Yu, M. Y. Mustafa, A decision support system for establishing a waste treatment plant for recycling organic waste into bio-energy in north Norway, Proceeding of 4th IEEE International Conference on Cognitive Infocommunications, Budapest, 2013. pp: 659-664.

DOI: 10.1109/coginfocom.2013.6719184

[19] S. Bojic, D. Datkov, D. Brcanov, M. Georgijevic, M. Martinov, Location allocation of solid biomass power plants: Case study of Vojvodina, Renewable and Sustainable Energy Reviews. 26 (2013) 769-775.

DOI: 10.1016/j.rser.2013.06.039

[20] R. L. Church, C. ReVelle, The maximal covering location problem, Papers of the Regional Science Association. 32 (1974) 101-118.

DOI: 10.1007/bf01942293

[21] L. N. Cai, Logistics system modeling, Tsinghua University Publishing House, Beijing, 2003. (In Chinese).

[22] M. L. Burkey, J. Bhadury, H. A. Eiselt, A location-based comparison of health care services in four U.S. states with efficiency and equity, Socia-Economic Planning Sciences. 46 (2012) 157-163.

DOI: 10.1016/j.seps.2012.01.002

[23] H. Yu, W. D. Solvang, S. Yuan, A multi-objective decision support system for simulation and optimization of municipal solid waste management system, Proceeding of 3rd IEEE International Conference on Cognitive Infocommunications, Kosice, 2012. pp: 193-199.

DOI: 10.1109/coginfocom.2012.6421978