تحلیل کمی معیارهای انتخاب مکان دستگاه‌های خودپرداز به منظور ارزیابی بهره وری عملیاتی بانک ها
محورهای موضوعی : مدیریت بازرگانی
حمید شاه بندرزاده
1
(دانشیار، گروه مدیریت صنعتی، دانشکده کسب و کار و اقتصاد ، دانشگاه خلیج فارس، بوشهر، ایران)
محمد حسین کبگانی
2
(دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده کسب و کار و اقتصاد، دانشگاه خلیج فارس،بوشهر،ایران.)
کلید واژه: اولویت بندی, مکان یابی, تسهیلات, مدلسازی ریاضی, دستگاه های خود پرداز,
چکیده مقاله :
مشتریان به همه تسهیلات خدماتی از قبیل فروشگاه های بزرگ، دستگاه های خودپرداز، پمپ بنزین و غیره نیازمندند و استفاده از این تسهیلات به صورت روزمره و جزء عادات آنها محسوب می گردد. به اعتقاد برخی پژوهشگران در هنگام انتخاب مکان برای این تسهیلات، هدف باید حداکثر پوشش جمعیت درون شبکه باشد. هدف آغازین این پژوهش، شناسایی عوامل موثر برای انتخاب مکان مناسب برای دستگاه های خودپرداز از طریق بازبینی پژوهش های موجود در حوزه بانکداری و مصاحبه باخبرگان این حوزه می باشد. همچنین در رویکرد پیشنهاد شده این پژوهش بعد از تعیین عوامل بالقوه جهت استقرار این دستگاه ها، مهمترین عوامل موثر برای تعیین مکان دستگاه های خودپرداز از طریق مدلسازی ریاضی مورد اولویت بندی قرار می گیرد. در این پژوهش 15 عامل برای تعیین مکان برای دستگاه های خودپرداز بر اساس پیشینه پژوهش و در نهایت بر اساس نظر کارشناسان به چهار گروه عوامل پوششی، عوامل اقتصادی، عوامل رقابتی، عوامل سرمایه گذاری- قانونی دسته بندی شد. همچنین براساس وزن های بدست آمده از مدلسازی ریاضی، عوامل پوششی با وزن 0/43 در رتبه اول و به عنوان مهمترین عامل شناسایی گردید. همچنین عوامل اقتصادی با وزن0/23 در رتبه دوم و عوامل رقابتی با وزن0/19 در رتبه سوم و عوامل سرمایه گذاری- قانونی با وزن 0/13 در رتبه چهارم قرار گرفت. همچنین نتایج حاصل از این پژوهش نشان می دهد که هرکدام از عوامل چهارگانه تعیین شده، می تواند اثرات متفاوتی بر روی تصمیم گیرندگان داشته باشد. از این رو به منظور اثربخشی بیشتر، مدیران بانک ها باید متناسب با هر عامل، تصمیمات متفاوتی اتخاذ نمایند.
Customers require various service facilities including department stores, ATMs, and gas stations the use of which has become a daily routine. Some researchers believe that location choice of such facilities should be based on maximum population coverage within the network. The current research set out to identify potential factors that may bear an impact on location selection of ATM machines through exploring the existing literature on banking and interviewing experts in the field. Moreover, it aimed to prioritize the most important factors among those already specified through mathematical modeling. The findings emerging from the literature and expert review indicated 15 factors influencing ATM location decisions that were further subcategorized into four major factor sets of economic, competitive, coverage, and investment - legal. The results of mathematical modeling weighing revealed the priority of factors with Coverage, weighing 0.43, as the first and most important one followed in significance by economic factors, weighing 0.23, competitive factors, weighing 0.19, and investment - law factors, weighing 0.13. The results of this study also indicated that each of the four factors sets could impact decision-makers differently; therefore, bank managers are suggested to vary their decisions with respect to each of the factors to achieve optimal effectiveness.
Aksoy, S., & Ozbuk, M. Y. (2017), . Multiple criteria decision making in hotel location: Does it relate to postpurchase consumer evaluations? Tourism Management Perspectives, 22, 73-81.
Boloori Arabani, A., & Zanjirani Farahani, R. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408–420 .
Brickley, J. A., Linck, J., & Smith, C. (2012). Vertical integration to avoid contractin gwith potential competitors:Evidence from bankers’banks. Journal of Financial Economics, 105(1), 113-130.
Brimberg, J.; and Drezner, Z. (2013), A new heuristic for solving the p-median problem in the plane. Computers & Operations Research, 40(1), 427-437.
Chauhan, A., & Singh, A.(2016), A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility. Journal of Cleaner Production, 139, 1001-1010.
Dinler, D., Kemal Tural, M., & Iyigun, C. (2015). Heuristics for a continuous multi-facility location problem with demand regions. Computers & Operations Research, 62, 237- 256.
Donze, J., & Dubec, I. (2006). The role of interchange fees in ATM networks. International Journal of Industrial Organization, 24(1), 29– 43 .
Izdebski, M., & Jacyna-Gołda, I. (2017), The Multi-criteria Decision Support in Choosing the Efficient Location of Warehouses in the Logistic Network. Procedia Engineering, 187, 635-640.
KalliorasA, TsangaratosP, PizpikisTh, VasileiouE, IliaI, & PliakasF. (2017),. Multi-criteria Decision Support System (DSS) for optimal locations of Soil Aquifer Treatment (SAT) facilities. Science of the Total Environment, 603-604, 472-486.
Kyu Suhr, J., Eum, S., Gi Jung, H., Li, G., Kim, G., & Kim, J. (2012). Recognizability assessment of facial images for automated teller machine applications. Pattern Recognition, 45(5), 1899–1914 .
Mahmood, T., & Mujtaba Shaikh, G. (2012). Adaptive Automated Teller Machines. Expert Systems with Applications, 40(4), 1152- 1169.
OLfat, L., Goli, A., & Foukardi, R. (2010). ATM Locator Using Analytical Hierarchy Process (AHP) Case Study: Agricultural Bank Branches in Tehran 10th District Municipality. Geography and Development, 8(18), 93-108. [In pershian].
Arena, M., & Dewally, M. (2012). Firm location and corporate debt. Journal of Banking & Finance, 36(4), 1079–1092 .
Pelegrn, B., Fernandez, P., Dolores Garcıa Perez, M., & Cano Hernandez, S. l. (2012), On the location of new facilities for chain expansion under delivered pricing. Omega, 40(2,)149–158 .
Puerto, J., Ramos, A., & Rodrıguez-Chıa, A. (2011). Single-allocation ordered median hub location problems. Computers &Operations Research 38(2)559–570 .
Rodríguez, D., Levine, J., Weinstein Agrawal, A., & Song, J. (2011). Can information promote transportation-friendly location decisions?A simulation experiment. Journal of Transport Geography, 19(2), 304–312 .
Seetharam , S., & Guanghua, k. (2010). Firm location choice in cities: Evidence from China, India, and Brazil. China Economic Review, 21(1) 113–122.
Tavana, M., J. Santos Arteaga, F., Mohammadi, S., & Alimohammadi, M. (2017), A fuzzy multi-criteria spatial decision support system for solar farm location planning. Energy Strategy Reviews, 18, 93-105.
Torfi, F., Zanjirani Farahani, R., & Mahdavi, I. (2016). Fuzzy MCDM for weight of object’s phrase in location routing problem. Applied Mathematical Modelling, 40(1), 526-541.
Tóth, B., Fernández, J., Pelegrín, B., & Plastria, F. (2009), Sequential versus simultaneous approach in the location and design of two new facilities using planar Huff-like models. Computers & Operations Research 36(5), 1393 – 1405 .
Tsolas, I. E. (2011). Bank branch-level DEA to assess overall efficiency. EuroMed Journal of Business, 6(3), 359-377 .
Villacreses, G., Gaona, G., Martinez-Gomez, D, & Juan Jion, D. (2017), Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renewable Energy, 109, 275-286.
Willmer Escobar, J. L. (2013), A two-phase hybrid heuristic algorithm for the capacitated location-routing problem. Computers & Operations Research, 40(1), 70-79.
Yen, W. C.-K. (2012), The connected p-center problem on block graphs with forbidden vertices. . Theoretical Computer Science, 426-427, 13-24.
Zanjirani Farahani, R., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling 34(7), 1689–1709 .
Zangirchi, seyyd mahmoud (2012). Fuzzy Analytical Hierarchy Process. Tehran: Sanei Shahmirzadi Publications, 1, 1-284. [In persian].
ŻAK, j., & WĘGLIŃSKIb, S. (2014). The selection of the logistics center location based on MCDM/A methodology. Transportation Research Procedia 3, 555 – 564.
Zhu, H., Eden, L., R. Miller, S., E. Thomas, D., & Fields, P. (2012). Host-country location decisions of early movers and latecomers:The role of local density and experiential learning. International Business Review, 21(2), 145–155 .
_||_Aksoy, S., & Ozbuk, M. Y. (2017), . Multiple criteria decision making in hotel location: Does it relate to postpurchase consumer evaluations? Tourism Management Perspectives, 22, 73-81.
Boloori Arabani, A., & Zanjirani Farahani, R. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408–420 .
Brickley, J. A., Linck, J., & Smith, C. (2012). Vertical integration to avoid contractin gwith potential competitors:Evidence from bankers’banks. Journal of Financial Economics, 105(1), 113-130.
Brimberg, J.; and Drezner, Z. (2013), A new heuristic for solving the p-median problem in the plane. Computers & Operations Research, 40(1), 427-437.
Chauhan, A., & Singh, A.(2016), A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility. Journal of Cleaner Production, 139, 1001-1010.
Dinler, D., Kemal Tural, M., & Iyigun, C. (2015). Heuristics for a continuous multi-facility location problem with demand regions. Computers & Operations Research, 62, 237- 256.
Donze, J., & Dubec, I. (2006). The role of interchange fees in ATM networks. International Journal of Industrial Organization, 24(1), 29– 43 .
Izdebski, M., & Jacyna-Gołda, I. (2017), The Multi-criteria Decision Support in Choosing the Efficient Location of Warehouses in the Logistic Network. Procedia Engineering, 187, 635-640.
KalliorasA, TsangaratosP, PizpikisTh, VasileiouE, IliaI, & PliakasF. (2017),. Multi-criteria Decision Support System (DSS) for optimal locations of Soil Aquifer Treatment (SAT) facilities. Science of the Total Environment, 603-604, 472-486.
Kyu Suhr, J., Eum, S., Gi Jung, H., Li, G., Kim, G., & Kim, J. (2012). Recognizability assessment of facial images for automated teller machine applications. Pattern Recognition, 45(5), 1899–1914 .
Mahmood, T., & Mujtaba Shaikh, G. (2012). Adaptive Automated Teller Machines. Expert Systems with Applications, 40(4), 1152- 1169.
OLfat, L., Goli, A., & Foukardi, R. (2010). ATM Locator Using Analytical Hierarchy Process (AHP) Case Study: Agricultural Bank Branches in Tehran 10th District Municipality. Geography and Development, 8(18), 93-108. [In pershian].
Arena, M., & Dewally, M. (2012). Firm location and corporate debt. Journal of Banking & Finance, 36(4), 1079–1092 .
Pelegrn, B., Fernandez, P., Dolores Garcıa Perez, M., & Cano Hernandez, S. l. (2012), On the location of new facilities for chain expansion under delivered pricing. Omega, 40(2,)149–158 .
Puerto, J., Ramos, A., & Rodrıguez-Chıa, A. (2011). Single-allocation ordered median hub location problems. Computers &Operations Research 38(2)559–570 .
Rodríguez, D., Levine, J., Weinstein Agrawal, A., & Song, J. (2011). Can information promote transportation-friendly location decisions?A simulation experiment. Journal of Transport Geography, 19(2), 304–312 .
Seetharam , S., & Guanghua, k. (2010). Firm location choice in cities: Evidence from China, India, and Brazil. China Economic Review, 21(1) 113–122.
Tavana, M., J. Santos Arteaga, F., Mohammadi, S., & Alimohammadi, M. (2017), A fuzzy multi-criteria spatial decision support system for solar farm location planning. Energy Strategy Reviews, 18, 93-105.
Torfi, F., Zanjirani Farahani, R., & Mahdavi, I. (2016). Fuzzy MCDM for weight of object’s phrase in location routing problem. Applied Mathematical Modelling, 40(1), 526-541.
Tóth, B., Fernández, J., Pelegrín, B., & Plastria, F. (2009), Sequential versus simultaneous approach in the location and design of two new facilities using planar Huff-like models. Computers & Operations Research 36(5), 1393 – 1405 .
Tsolas, I. E. (2011). Bank branch-level DEA to assess overall efficiency. EuroMed Journal of Business, 6(3), 359-377 .
Villacreses, G., Gaona, G., Martinez-Gomez, D, & Juan Jion, D. (2017), Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renewable Energy, 109, 275-286.
Willmer Escobar, J. L. (2013), A two-phase hybrid heuristic algorithm for the capacitated location-routing problem. Computers & Operations Research, 40(1), 70-79.
Yen, W. C.-K. (2012), The connected p-center problem on block graphs with forbidden vertices. . Theoretical Computer Science, 426-427, 13-24.
Zanjirani Farahani, R., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling 34(7), 1689–1709 .
Zangirchi, seyyd mahmoud (2012). Fuzzy Analytical Hierarchy Process. Tehran: Sanei Shahmirzadi Publications, 1, 1-284. [In persian].
ŻAK, j., & WĘGLIŃSKIb, S. (2014). The selection of the logistics center location based on MCDM/A methodology. Transportation Research Procedia 3, 555 – 564.
Zhu, H., Eden, L., R. Miller, S., E. Thomas, D., & Fields, P. (2012). Host-country location decisions of early movers and latecomers:The role of local density and experiential learning. International Business Review, 21(2), 145–155 .