@2024 Afarand., IRAN
ISSN: 2538-4384 Geographical Researches 2020;35(4):307-316
ISSN: 2538-4384 Geographical Researches 2020;35(4):307-316
Application of Dynamic Locating in long-term Urban Management Planning; the Case Study of Counters Offices of Civil Registration Organization
ARTICLE INFO
Article Type
Original ResearchAuthors
Fazel Torshizi D. (1)Naji-Azimi Z. (*1)
Kazemi M. (1)
(1) Department of Management, Faculty of Economics and Business Administration, Ferdowsi University of Mashhad, Mashhad, Iran
Correspondence
Address: Department of Management, Faculty of Economics and Business Administration, Ferdowsi University of Mashhad, Azadi Square, Mashhad, Iran. Postal Code: 9177948951.Phone: +98 (51) 38805352
Fax: +98 (51) 38811243
znajiazimi@um.ac.ir
Article History
Received: June 27, 2020Accepted: September 2, 2020
ePublished: December 12, 2020
ABSTRACT
Aims & Backgrounds
Improving spatial access to public services is one of the effective tasks of urban management to develop urban living standards which provides justly facilitating and benefiting of received services for citizens. The aim of this study was to apply the dynamic locating of public services centers in long-term urban management planning.
Methodology In this applied descriptive research, at first the studied conditions were defined in terms of decision variables, parameters, objective function and limitations by reviewing the research background and experts’ opinions. Then the data were collected using databases and expert opinions and finally, mathematical modeling of dynamic locating was performed to maximize the desirability of selecting the counters offices of Civil Registration in Mashhad and the model was solved by the CPLEX solver with the branch and cut algorithm.
Findings While identifying the indicators of desirability of candidate points, the location of offices in urban areas, the time of reopening and closing, and relevant time period were determined. Activity timing and locating offices, covering 80% of the population and suitable geographical distribution with 39 offices were performed, and optimal value of the objective function was achieved in terms of the indicators considered by the stakeholders and maximum desirability.
Conclusion Due to the low performance of static models, demographic changes and demand in the field of urban management, a dynamic model was used to solve this problem to provide a more accurate picture of the time of office creation in each area, the ability to select offices with appropriate reliability for activity. And allocating the customers of every region per period to the best location in terms of providing stakeholder indicators.
Methodology In this applied descriptive research, at first the studied conditions were defined in terms of decision variables, parameters, objective function and limitations by reviewing the research background and experts’ opinions. Then the data were collected using databases and expert opinions and finally, mathematical modeling of dynamic locating was performed to maximize the desirability of selecting the counters offices of Civil Registration in Mashhad and the model was solved by the CPLEX solver with the branch and cut algorithm.
Findings While identifying the indicators of desirability of candidate points, the location of offices in urban areas, the time of reopening and closing, and relevant time period were determined. Activity timing and locating offices, covering 80% of the population and suitable geographical distribution with 39 offices were performed, and optimal value of the objective function was achieved in terms of the indicators considered by the stakeholders and maximum desirability.
Conclusion Due to the low performance of static models, demographic changes and demand in the field of urban management, a dynamic model was used to solve this problem to provide a more accurate picture of the time of office creation in each area, the ability to select offices with appropriate reliability for activity. And allocating the customers of every region per period to the best location in terms of providing stakeholder indicators.
CITATION LINKS
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[9]Giménez-Gaydou DA, Ribeiro AS, Gutiérrez J, Antunes AP (2016). Optimal location of battery electric vehicle charging stations in urban areas: A new approach. International Journal of Sustainable Transportation. 10(5):393-405.
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[15]Lin JR, Yang TH (2011). Strategic design of public bicycle sharing systems with service level constraints. Transportation Research Part E: Logistics and Transportation Review. 47(2):284-294.
[16]Lin JR, Yang TH, Chang YC (2013). A hub location inventory model for bicycle sharing system design: Formulation and solution. Computers & Industrial Engineering. 65(1):77-86.
[17]Ljubić I, Moreno E (2018). Outer approximation and submodular cuts for maximum capture facility location problems with random utilities. European Journal of Operational Research. 266(1):46-56.
[18]Lotfi H, Adalatkhah F, Mirzaei M, Vazirpour S (2009). Urban management and its place in Promoting Citizens' Rights. Journal of Human Geography. 2(1):101-110. [Persian]
[19]Marianov V, Serra D (2011). Location of multiple-server common service centers or facilities, for minimizing general congestion and travel cost functions. International Regional Science Review. 34(3):323-338.
[20]Moghimi M, Taghizadeh Yazdi M (2017). Applying multi-Criteria decision-making (MCDM) methods for economic ranking of Tehran-22 districts to establish financial and commercial centers (Case: City of Tehran). Journal of Urban Economics and Management. 5(20):39-51. [Persian]
[21]Rajagopalan HK, Saydam C, Xiao J (2008). A multiperiod set covering location model for dynamic redeployment of ambulances. Computers & Operations Research. 35(3):814-826.
[22]ReVelle CS, Swain RW (1970). Central facilities location. Geographical Analysis. 2(1):30–42.
[23]Sadeghi SM, Shahbazi GM, Bigdeli V (2012). Prioritizing the barriers of public-Private partnerships development in transportation sector of Iran using MCDM Models. Journal of Economic Modeling Research. 2(6):107-130. [Persian]
[24]Sterle C, Sforza A, Amideo, AE (2016). Multi-period location of flow intercepting portable facilities of an intelligent transportation system. Socio-Economic Planning Sciences. 53:4-13.
[25]Taghipourian F, Mahdavi I, Mahdavi-Amiri N, Makui A (2012). A fuzzy programming approach for dynamic virtual hub location problem. Applied Mathematical Modelling. 36(7):3257-3270.
[26]Zheng H, He X, Li Y, Peeta S (2017). Traffic equilibrium and charging facility locations for electric vehicles. Networks and Spatial Economics. 17(2):435-457.
[2]Arabani AB, Farahani RZ (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering. 62(1):408-420.
[3]Başar A, Çatay B, Ünlüyurt T (2011). A multi-period double coverage approach for locating the emergency medical service stations in Istanbul. Journal of the Operational Research Society. 62(4):627-637.
[4]Basu R, Jana A, Bardhan R (2018). A health care facility allocation model for expanding cities in developing nations: strategizing urban health policy implementation. Applied Spatial Analysis and Policy. 11(1):21-36.
[5]Boran F (2011). An integrated intuitionistic fuzzy multi criteria decision making method for facility location selection. Mathematical and Computational Applications. 16(2):487-496.
[6]Castillo-López I, López-Ospina HA (2015). School location and capacity modification considering the existence of externalities in students school choice. Computers & Industrial Engineering. 80:284-294.
[7]Djenić A, Marić M, Stanimirović Z, Stanojević P (2017). A variable neighbourhood search method for solving the long-term care facility location problem. IMA Journal of Management Mathematics. 28(2):321-338.
[8]Fredriksson A (2017). Location-allocation of public services–Citizen access, transparency and measurement. A method and evidence from Brazil and Sweden. Socio-Economic Planning Sciences. 59:1-12.
[9]Giménez-Gaydou DA, Ribeiro AS, Gutiérrez J, Antunes AP (2016). Optimal location of battery electric vehicle charging stations in urban areas: A new approach. International Journal of Sustainable Transportation. 10(5):393-405.
[10]Haase K, Knörr L, Krohn R, Müller S, Wagner M (2019). Facility Location in the Public Sector. In: Location Science. Laporte G, Nickel S, Saldanha da Gama F, editors. New York: Springer; pp.745-764.
[11]Hale TS, Moberg CR (2003). Location science research: A review. Annals of Operations Research. 123:21-35.
[12]Jena SD, Cordeau JF, Gendron B (2017). Lagrangian heuristics for large-scale dynamic facility location with generalized modular capacities. INFORMS Journal on Computing. 29(2):388-404.
[13]Klier MJ, Haase K (2015). Urban public transit network optimization with flexible demand. OR Spectrum. 37(1):195-215.
[14]Leonardi G (1981). A unifying framework for public facility location problems-part 1: A critical overview and some unsolved problems. Environment and Planning A: Economy and Space. 13(8):1001-1028.
[15]Lin JR, Yang TH (2011). Strategic design of public bicycle sharing systems with service level constraints. Transportation Research Part E: Logistics and Transportation Review. 47(2):284-294.
[16]Lin JR, Yang TH, Chang YC (2013). A hub location inventory model for bicycle sharing system design: Formulation and solution. Computers & Industrial Engineering. 65(1):77-86.
[17]Ljubić I, Moreno E (2018). Outer approximation and submodular cuts for maximum capture facility location problems with random utilities. European Journal of Operational Research. 266(1):46-56.
[18]Lotfi H, Adalatkhah F, Mirzaei M, Vazirpour S (2009). Urban management and its place in Promoting Citizens' Rights. Journal of Human Geography. 2(1):101-110. [Persian]
[19]Marianov V, Serra D (2011). Location of multiple-server common service centers or facilities, for minimizing general congestion and travel cost functions. International Regional Science Review. 34(3):323-338.
[20]Moghimi M, Taghizadeh Yazdi M (2017). Applying multi-Criteria decision-making (MCDM) methods for economic ranking of Tehran-22 districts to establish financial and commercial centers (Case: City of Tehran). Journal of Urban Economics and Management. 5(20):39-51. [Persian]
[21]Rajagopalan HK, Saydam C, Xiao J (2008). A multiperiod set covering location model for dynamic redeployment of ambulances. Computers & Operations Research. 35(3):814-826.
[22]ReVelle CS, Swain RW (1970). Central facilities location. Geographical Analysis. 2(1):30–42.
[23]Sadeghi SM, Shahbazi GM, Bigdeli V (2012). Prioritizing the barriers of public-Private partnerships development in transportation sector of Iran using MCDM Models. Journal of Economic Modeling Research. 2(6):107-130. [Persian]
[24]Sterle C, Sforza A, Amideo, AE (2016). Multi-period location of flow intercepting portable facilities of an intelligent transportation system. Socio-Economic Planning Sciences. 53:4-13.
[25]Taghipourian F, Mahdavi I, Mahdavi-Amiri N, Makui A (2012). A fuzzy programming approach for dynamic virtual hub location problem. Applied Mathematical Modelling. 36(7):3257-3270.
[26]Zheng H, He X, Li Y, Peeta S (2017). Traffic equilibrium and charging facility locations for electric vehicles. Networks and Spatial Economics. 17(2):435-457.