ORIGINAL_ARTICLE
Countries credit ranking: a simple weighted non-linear programming model
In this study a non-linear weighted sum model is proposed to rank countries based on economic factors. This ranking problem could be new and useful as most of previous researches rated countries not rank them. The countries are ranked from the best to the worst one by their score obtained by the model from credit point of view. As an advantage of the model, it is solvable by an analytical solution method manually instead of using optimization software. The analytical solution is useful for managers and decision makers to apply the model easily. The obtained ranking is compared with Moody’s rating to discuss the efficiency of the model.
https://aotp.fabad-ihe.ac.ir/article_68822_9a0b267867b945142715f3060d11f966.pdf
2018-11-01
1
13
10.22121/aotp.2018.68822
Country credit ranking
Country credit rating
Weighted additive model
Non-linear model
Sadegh
Niroomand
sadegh.niroomand@yahoo.com
1
Department of Industrial Engineering, Firouzabad Institute of Higher Education, Firouzabad, Fars, Iran
LEAD_AUTHOR
Abad, P., & Robles, M.D. (2014). Credit rating agencies and idiosyncratic risk: is there a linkage? Evidence from the Spanish Market. International Review of Economics and Finance, 33, 152-171.
1
Afonso, A. (2003). Understanding the determinant of sovereign debt rating: Evidence for two leading agencies. Journal of Economics and Finance, 27, 56-74.
2
Afonso, A. (2010). Long-term government bond yields and economic forecasts: evidence for the EU. Applied Economic Letter, 15, 1437–1441.
3
Allen, L., & Saunders, A. (2003), A survey of cyclical effects in credit risk measurement models, Technical Report, BIS Working Paper 126.
4
Amira, K. (2004). Determinants of sovereign Eurobonds yield spreads. Journal of Business Finance & Accounting, 31, 795–821.
5
Baldacci, E., & Kumar, M. (2010). Fiscal deficits, public debt and sovereign debt yields, International Monetary Fund Working Paper 184.
6
Ballı, S. & Korukoğlu, S. (2014). Development of a fuzzy decision support framework for complex multi-attribute decision problems: A case study for the selection of skilful basketball players. Expert Systems, 31, 56–69. doi: 10.1111/exsy.12002.
7
Chen, S-Y., & Lu, C-C. (2015). Assessing the competitiveness of insurance corporations using fuzzy correlation analysis and improved fuzzy modified TOPSIS. Expert Systems, 32, 392–404. doi: 10.1111/exsy.12099.
8
Cifter, A., Yilmazer, S., & Cifer E. (2009). Analysis of sectorial credit default cycle dependency with wavelet networks: Evidence from Turkey. Economic Modelling, 26, 1382-1388.
9
Eyssella, T., Fungb, H., & Zhanga, G. (2013). Determinants and price discovery of China sovereign credit default swaps. China Economic Review, 24, 1-15.
10
Figlewski, S., Frydman, H., & Liang, W. (2012). Modeling the effect of macroeconomic factors on corporate default and credit rating transitions. International Review of Economics and Finance, 21, 87-105.
11
Fleming, W.H., & Stein, J.L. (2004). Stochastic optimal control, international finance and debt. Journal of Banking & Finance, 28, 979-996.
12
Gonzalez J., & Hinojosa, I. (2010). Estimation of conditional time-homogeneous credit quality transition matrices. Economic Modelling, 27, 89-96.
13
Hammer, P.L., Kogan, A., & Legeune, M.A. (2007). Reverse-engineering banks’ financial strength pattern using logical analysis of data. Rutcor Research Report (RRR 10-2007).
14
Hirth S. (2014). Credit rating dynamics and competition. Journal of Banking & Finance, 49, 100-112.
15
Hischer, J., & Nosbusch, Y. (2010). Determinants of sovereign risk: macroeconomic fundamentals and the pricing of sovereign debt. Review of Finance, 14, 235–262.
16
Hu, M., Kiesel, R., & Perraudin, W. (2002). The estimation of transition matrices for sovereign credit rating. Journal of Banking & Finance, 26, 1383-1406.
17
International Monetary Fund Data and Statistics: http://www.imf.org
18
Izadikhah, M., & Farzipoor Saen, R. (2015). A new data envelopment analysis method for ranking decision making units: an application in industrial parks, Expert Systems, doi: 10.1111/exsy.12112.
19
Koopman, S.J., & Lucas, A. (2005). Business and default cycles for credit risk. Journal of Applied Econometrics, 20, 311–323.
20
Kovács, G., Marian, M., & Vizvári, B. (2002). Viability results in control of one-dimensional discrete time dynamical systems defined by a multi-function. Pure Mathematics and Applications, 13(1-2), 185-195.
21
Lei, A.C.H., Yick, M.H.Y., & Lam, K.S.K. (2014). The effects of tax convexity on default and investment decisions. Applied Economics, 46, 1267-1278.
22
Maltritz, D., & Molchanov A. (2014). Country risk credit determinants with model uncertainty. International Review of Economics and Finance, 29, 224-234.
23
Mirzaei, N., & Vizvari, B. (2011). Reconstruction of World Bank’s classification of countries. African Journal of Business Management, 32, 12577-12585.
24
Moreira, C., Calado, P., & Martins, B. (2015). Learning to rank academic experts in the DBLP dataset. Expert Systems, 32, 477–493. doi: 10.1111/exsy.12062.
25
Nickell, P., Perraudin, W., & Varotto, S. (2000). Stability of rating transitions. Journal of Banking and Finance, 24, 203–227.
26
Özatay, F., Özmen, E., & Şahinbeyoğlu, G. (2009). Emerging market sovereign spreads, global financial conditions and U.S. macroeconomic news. Economic Modeling, 26, 526-531.
27
Pantelous, A.A. (2008). Dynamic risk management of the lending rate policy of an interacted portfolio of loans via an investment strategy into a discrete stochastic framework. Economic Modelling, 25, 658-675.
28
Pesaran, M.H., Schuermann, T., Treutler, B.J., & Weiner, S.M. (2006). Macroeconomic dynamics and credit risk: a global perspective. Journal of Money, Credit and Banking, 38, 1211–1261.
29
Schumacher, I. (2014). On the self-fulfilling prophecy of changes in sovereign ratings. Economic Modeling, 38, 351-356.
30
Surma, J. (2015). Case-based approach for supporting strategy decision making. Expert Systems, 32: 546–554. doi: 10.1111/exsy.12003.
31
Wang, Y.M., & Fu, G.W. (1993). A new multiattribute decision-making method based on DEA thought. Journal of Industrial Engineering and Engineering Management, 7, 44–49.
32
Wilson, T. (1997). Portfolio credit risk, Part I. Risk, 111–117.
33
Xu, J., & Zhang, X. (2014). China's sovereign debt: A balance-sheet perspective. China Economic Review, 31, 55-73.
34
Zopounidis, C., & Doumpos, M. (2000). Multicriteria Sorting Methods. Encyclopedia of optimization. Academic Publishers.
35
Zopounnidis, C., & Doumpos, M. (2002). Multicriteria classification and sorting method: A literature review. European Journal of Operational Resaerch, 138, 229-246.
36
ORIGINAL_ARTICLE
Local search based meta-heuristic algorithms for optimizing the cyclic flexible manufacturing cell problem
Flexible robotic cells are used in many real-life industries to produce standardized items at a high production speed. Determining the schedules of these cells is an important optimization problem in those industries. In this study, the cell's machines are identical and parallel. In the cell, there is an input and an output buffer wherein items being processed and the finished items are kept, respectively. There is a robot performing the loading/unloading operations of the machines and transporting the items. The system repeats a cycle in its run. Each machine processes one part in each cycle. The cycle time depends on the order of the loading/unloading activities. Therefore, determining the order of these activities for the minimum cycle time is needed. We propose a new mathematical model to solve the problem. For large size problems, three metaheuristic algorithms based on local search algorithm are proposed. In the metaheuristics, in order to compute the minimum cycle time of a given solution a linear programming model is needed to be solved which is one of the recent cases in the literature to the best of our knowledge. Several numerical examples are solved by the proposed algorithms and their performance and solutions are compared.
https://aotp.fabad-ihe.ac.ir/article_82797_3dcfff483a096f274e55cdd83cb23036.pdf
2018-11-01
15
32
10.22121/aotp.2019.148030.1015
scheduling
Flexible manufacturing system
Robotic cell
Metaheuristic
Bela
Vizvari
vizvaribela@gmail.com
1
Department of Industrial Engineering, Eastern Mediterranean University, Mersin 10, Turkey
LEAD_AUTHOR
Huseyin
Guden
hueyin.guden@emu.edu.tr
2
Department of Industrial Engineering, Eastern Mediterranean University, Mersin 10, Turkey
AUTHOR
Mazyar
G. Nejad
mazyarghadirinejad@gau.edu.tr
3
Department of Industrial Engineering, Girne American University, Mersin 10, Turkey
AUTHOR
Abdekhodaee, A. H., Wirth, A., & Gan, H. S. (2004). Equal processing and equal setup time cases of scheduling parallel machines with a single server. Computers & Operations Research, 31(11), 1867-1889.
1
Brauner, N., & Finke, G. (2001). Cycles and permutations in robotic cells. Mathematical and Computer Modelling, 34(5-6), 565-591.
2
Crama, Y. (1997). Combinatorial optimization models for production scheduling in automated manufacturing systems. European Journal of Operational Research, 99, 136-153.
3
Crama, Y., & Van de Klundert, J. (1999). Cyclic scheduling in 3‐machine robotic flow shops. Journal of Scheduling, 2(1), 35-54.
4
Caramia, M., & Mari, R. (2017). A manufacturing cell formation problem with a maximum cell workload constraint. IMA Journal of Management Mathematics, 28(2), 279-298.
5
Dawande, M., Geismar, H. N., Sethi, S. P., & Sriskandarajah, C. (2005). Sequencing and scheduling in robotic cells: Recent developments. Journal of Scheduling, 8(5), 387-426.
6
Foumani, M., & Jenab, K. (2012). Cycle time analysis in reentrant robotic cells with swap ability. International Journal of Production Research, 50(22), 6372-6387.
7
Foumani, M., & Jenab, K. (2013). Analysis of flexible robotic cells with improved pure cycle. International Journal of Computer Integrated Manufacturing, 26(3), 201-215.
8
Ghadiri Nejad, M., & Banar, M. (2018). Emergency response time minimization by incorporating ground and aerial transportation. Annals of Optimization Theory and Practice, 1(1), 43-57.
9
Ghadiri Nejad, M., Güden, H., Vizvári, B., & Vatankhah Barenji, R. (2018). A mathematical model and simulated annealing algorithm for solving the cyclic scheduling problem of a flexible robotic cell. Advances in Mechanical Engineering, 10(1) 1-12.
10
Ghadirinejad, M., & Mosallaeipour, S. (2013). A new approach to optimize a flexible manufacturing cell. In 1st international conference on new directions in business, management, finance and economics (Vol. 38).
11
Gultekin, H., Akturk, M. S., & Karasan, O. E. (2008). Scheduling in robotic cells: process flexibility and cell layout. International Journal of Production Research, 46(8), 2105-2121.
12
Gultekin, H., Karasan, O. E., & Akturk, M. S. (2009). Pure cycles in flexible robotic cells. Computers & Operations Research, 36(2), 329-343.
13
Jiang, Y., Zhang, Q., Hu, J., Dong, J., & Ji, M. (2015). Single-server parallel-machine scheduling with loading and unloading times. Journal of Combinatorial Optimization, 30(2), 201-213.
14
Jolai, F., Foumani, M., Tavakoli-Moghadam, R., & Fattahi, P. (2012). Cyclic scheduling of a robotic flexible cell with load lock and swap. Journal of Intelligent Manufacturing, 23(5), 1885-1891.
15
Kim, H., Kim, H. J., Lee, J. H., & Lee, T. E. (2013). Scheduling dual-armed cluster tools with cleaning processes. International Journal of Production Research, 51(12), 3671-3687.
16
Mjirda, A., Jarboui, B., Mladenović, J., Wilbaut, C., & Hanafi, S. (2014). A general variable neighbourhood search for the multi-product inventory routing problem. IMA Journal of Management Mathematics, 27(1), 39-54.
17
Mosallaeipour, S., Nazerian, R., & Ghadirinejad, M. (2018a). A Two-Phase Optimization Approach for Reducing the Size of the Cutting Problem in the Box-Production Industry: A Case Study. In Industrial Engineering in the Industry 4.0 Era (pp. 63-81). Springer, Cham.
18
Mosallaeipour, S., Nejad, M. G., Shavarani, S. M., & Nazerian, R. (2018b). Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study. Production Engineering, 12(1), 83-94.
19
Nejad, M. G., Kashan, A. H., & Shavarani, S. M. (2018a). A novel competitive hybrid approach based on grouping evolution strategy algorithm for solving U-shaped assembly line balancing problems. Production Engineering, 1-12.
20
Nejad, M. G., Kovács, G., Vizvári, B., & Barenji, R. V. (2018b). An optimization model for cyclic scheduling problem in flexible robotic cells. The International Journal of Advanced Manufacturing Technology, 95(9-12), 3863-3873.
21
Nejad, M. G., Shavarani, S. M., Vizvári, B., & Barenji, R. V. (2018c). Trade-off between process scheduling and production cost in cyclic flexible robotic cells. The International Journal of Advanced Manufacturing Technology, 96(1-4), 1081-1091.
22
Ribeiro, C. C., & Resende, M. G. (2012). Path-relinking intensification methods for stochastic local search algorithms. Journal of heuristics, 18(2), 193-214.
23
Sethi, S. P., Sriskandarajah, C., Sorger, G., Blazewicz, J., & Kubiak, W. (1992). Sequencing of parts and robot moves in a robotic cell. International Journal of Flexible Manufacturing Systems, 4(3-4), 331-358.
24
Sevkli, M., & Aydin, M. E. (2007). Parallel variable neighbourhood search algorithms for job shop scheduling problems. IMA Journal of Management Mathematics, 18(2), 117-133.
25
Shavarani, S. M., Nejad, M. G., Rismanchian, F., & Izbirak, G. (2018). Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon prime air in the city of San Francisco. The International Journal of Advanced Manufacturing Technology, 95(9-12), 3141-3153.
26
Vatankhah Barenji, R., Ghadiri Nejad, M., & Asghari, I. (2018). Optimally sized design of a wind/photovoltaic/fuel cell off-grid hybrid energy system by modified-gray wolf optimization algorithm. Energy & Environment, 0958305X18768130.
27
ORIGINAL_ARTICLE
A new model to rate the level of customers' loyalty using mixed ANP and TOPSIS approach (Case study: grocery stores in the Ahvaz city)
Considering the importance of customers' loyalty and the existing competitive environment, it is necessary to assess the factors affecting customer loyalty to find out the levels of effectiveness of different factors on customer' loyalty and the performance of different stores in this field. Therefore, in this research, a model is provided to rate the customer loyalty levels of grocery stores in the Ahvaz city with ANP and TOPSIS mixed approach. Different stages of the presented model include designing the conceptual model of the effective factors on loyalty of the customers of grocery stores, measuring the weights of the factors using ANP and rating the performances of the stores using TOPSIS. The results show that pricing is the most important factor when it comes to customer loyalty and after that quality of the goods, quality of the services, variety and innovation and technology are the most important factors in customer loyalty, respectively. Also the best performance by a grocery store is determined.
https://aotp.fabad-ihe.ac.ir/article_82799_4b29b1dbe8c4792bbf5098e54471ca67.pdf
2018-11-01
33
44
10.22121/aotp.2019.149969.1016
customer loyalty
Multi-criteria decision making
Analytic Network Process
TOPSIS
Amir
Rezaei
dr_amirrezaei@yahoo.com
1
Department of Industrial Engineering, Masjed Soleyman Branch, Islamic Azad University, Masjed Soleyman, Iran
LEAD_AUTHOR
Saber
Molla-Alizadeh-Zavardehi
saber.alizadeh@gmail.com
2
Department of Industrial Engineering, Masjed Soleyman Branch, Islamic Azad University, Masjed Soleyman, Iran
AUTHOR
Arman
Sajedinezhad
sajedinejad@irandoc.ac.ir
3
Iranian Research Institute for Information Science and Technology (IRANDOC), Tehran, Iran
AUTHOR
Davar, V. Safaiian, M. (2002). Applicabl methods for banking service marketing for Iranian banks . Negahe Danesh.
1
Hapson, B., Logari, J., Morgatid, S., Eskali, M., & Simpson, D. (2002). Service Management (Customer oriented Culture). Translated by Irannejad Parizi, M.(1381). National IRANNIAN Library.
2
Ball, D., Simões Coelho, P., & Machás, A. (2004). The role of communication and trust in explaining customer loyalty: An extension to the ECSI model. European journal of marketing, 38(9/10), 1272-1293.
3
Gorondutse, A. H., Hilman, H., & Nasidi, M. (2014). Relationship between corporate reputation and customer loyalty on Nigerian food and beverages industry: PLS approach. International Journal of Management and Business Research, 4(2), 125-136.
4
Donio', J., Massari, P., & Passiante, G. (2006). Customer satisfaction and loyalty in a digital environment: an empirical test. Journal of Consumer Marketing, 23(7), 445-457.
5
Haghighi Kafash, M., Akbari M., and Lianpour, N. (2010). Effective Factors On Customer's Loyalty (Case study: IRAN INSURANCE COMPANY). Insurance Journal 75-95.
6
Haghighi Kafash, M., & Akbari, M. Prioratization of the Factors Affecting Customer Loyalty Using ESCI Model. Marketing Management Magazine, (10), 95-118.
7
Zamani Moghaddam A., and K. LAHIJI. (2012). Surveying factors influencing customer loyalty in private banks based on fast response organization's model. 63-79.
8
Maleki, Gholandoz M., et al. (2014). Identifying and ranking of factors affecting customer satisfaction of the household wood furniture industry by a multi attribute decision making method (Case study: Home furnitue). 691-708.
9
Abbasi, A., Rajabi, A., (2014). Investigating factors and barriers of e-loyalty to e-banking services in private banking customers in Golestan province, Journal of Business Management, vol. 6, no. 4, pp. 828-844.
10
Sabzeei, A., Husseini, A., Bandarkhani, M. (2014). Reviews the factors affecting customer loyalty (case study agricultural bank). Journal of Management.93, 83-73.
11
Noyan, F. Şimşek, GG. (2013). The antecedents of customer loyalty. Journal of Procedia - Social and Behavioral Sciences, Vol2 109, 1220-12242
12
Giovanis, A. N., Zondiros, D., & Tomaras, P. (2014). The antecedents of customer loyalty for broadband services: The role of service quality, emotional satisfaction and corporate image. Procedia-Social and Behavioral Sciences, 148, 236-244.
13
Chen, S. C. (2015). Customer value and customer loyalty: Is competition a missing link?. Journal of Retailing and Consumer Services, 22, 107-116.
14
ORIGINAL_ARTICLE
Dynamics of rumor spreading
In this paper, we explain the rumor spreader model with a differential equation system and analyses and consider this system in dynamical system view. The model which we consider in a society contains ignoring, spreading, stifle and controlling factors. In this work, we study on a new rumor spreading model, Ignorant-Spreader-Stifler-Controller (ISRC) model, is developed. The model extends the classical Ignorant-Spreader-Stifler (ISR) rumor spreading model by adding a new kind of people that spread a new rumor against previous rumor to control and reduce the maximum rumor influence.The model is an extension of SIR model which has studied before. In this research, we give a dynamical system which explains SIRC dynamical factors. Moreover, we consider the equilibrium conditions near the equilibrium point.
https://aotp.fabad-ihe.ac.ir/article_82803_a9272157ec22951ed080bd1e01062aba.pdf
2018-11-01
45
54
10.22121/aotp.2019.158003.1017
Epidemic model
Rumor spreading
Asymptotic behavior
Numerical Simulations
Hojjat Allah
Ebadizadeh
ebadizadeh.h@gmail.com
1
Young Researchers and Elite Club, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
LEAD_AUTHOR
Hamidreza
Haghbayan
2
Young Researchers and Elite Club, Shahr Ghods Branch, Islamic Azad University, Shahr Ghods, Tehran, Iran
AUTHOR
Zhang, Z. L., & Zhang, Z. Q. (2009). An interplay model for rumour spreading and emergency development. Physica A: Statistical Mechanics and its Applications, 388(19), 4159-4166.
1
Galam, S. (2003). Modelling rumors: the no plane Pentagon French hoax case. Physica A: Statistical Mechanics and Its Applications, 320, 571-580.
2
Kimmel, A. J. (2004). Rumors and the financial marketplace. The Journal of Behavioral Finance, 5(3), 134-141.
3
Kosfeld, M. (2005). Rumours and markets. Journal of Mathematical Economics, 41(6), 646-664.
4
Kosmidis, K., & Bunde, A. (2007). On the spreading and localization of risky information in social networks. Physica A: Statistical Mechanics and its Applications, 386(1), 439-445.
5
Wang, Y. Q., Yang, X. Y., & Wang, J. (2014). A rumor spreading model with control mechanism on social networks. Chinese Journal of Physics, 52(2), 816-829.
6
Thomas, S. A. (2007). Lies, damn lies, and rumors: an analysis of collective efficacy, rumors, and fear in the wake of Katrina. Sociological Spectrum, 27(6), 679-703.
7
Bhavnani, R., Findley, M. G., & Kuklinski, J. H. (2009). Rumor dynamics in ethnic violence. The Journal of Politics, 71(3), 876-892.
8
Daley, D. J., & Kendall, D. G. (1964). Epidemics and rumours. Nature, 204(4963), 1118.
9
D. Maki, M. Thomson. (1973). Mathematical Models and Applications, Prentice-Hall, Englewood Cliffs.
10
Pittel, B. (1990). On a Daley-Kendall model of random rumours. Journal of Applied Probability, 27(1), 14-27.
11
Lefevre, C., & Picard, P. (1994). Distribution of the final extent of a rumour process. Journal of Applied Probability, 31(1), 244-249.
12
Gu, J., Li, W., & Cai, X. (2008). The effect of the forget-remember mechanism on spreading. The European Physical Journal B, 62(2), 247-255.
13
Huo, L. A., Huang, P., & Fang, X. (2011). An interplay model for authorities’ actions and rumor spreading in emergency event. Physica A: Statistical mechanics and its applications, 390(20), 3267-3274.
14
Zhao, L., Wang, Q., Cheng, J., Chen, Y., Wang, J., & Huang, W. (2011). Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal. Physica A: Statistical Mechanics and its Applications, 390(13), 2619-2625.
15
Wang, J., & Wang, Y. Q. (2015). SIR rumor spreading model with network medium in complex social networks. Chinese Journal of Physics, 53(1), 020702-1.
16
Zhao, X., & Wang, J. (2013). Dynamical model about rumor spreading with medium. Discrete Dynamics in Nature and Society, 2013.
17
Zanette, D. H. (2001). Critical behavior of propagation on small-world networks. Physical Review E, 64(5), 050901.
18
Zanette, D. H. (2002). Dynamics of rumor propagation on small-world networks. Physical review E, 65(4), 041908.
19
Moreno, Y., Nekovee, M., & Vespignani, A. (2004a). Efficiency and reliability of epidemic data dissemination in complex networks. Physical Review E, 69(5), 055101.
20
Moreno, Y., Nekovee, M., & Pacheco, A. F. (2004b). Dynamics of rumor spreading in complex networks. Physical Review E, 69(6), 066130.
21
Moreno, Y., Pastor-Satorras, R., & Vespignani, A. (2002). Epidemic outbreaks in complex heterogeneous networks. The European Physical Journal B-Condensed Matter and Complex Systems, 26(4), 521-529.
22
Dietz, K. (1967). Epidemics and rumours: A survey. Journal of the Royal Statistical Society. Series A (General), 505-528.
23
Zhao, L., Wang, J., Chen, Y., Wang, Q., Cheng, J., & Cui, H. (2012). SIHR rumor spreading model in social networks. Physica A: Statistical Mechanics and its Applications, 391(7), 2444-2453.
24
Sudbury, A. (1985). The proportion of the population never hearing a rumour. Journal of applied probability, 22(2), 443-446.
25
Sharomi, O., Podder, C. N., Gumel, A. B., Mahmud, S. M., & Rubinstein, E. (2011). Modelling the transmission dynamics and control of the novel 2009 swine influenza (H1N1) pandemic. Bulletin of mathematical biology, 73(3), 515-548.
26