ORIGINAL_ARTICLE
Fuzzy Goal Programming for Linear Facility Location-Allocation in a Supply Chain; The Case of Steel Industry
This paper presents a mathematical model for a facility location-allocation problem in order to design an integrated supply chain. We consider a supply chain including multiple suppliers, multiple products, multiple plants, multiple transportation alternatives and multiple customer zones. The problem is to determine a number and capacity level of plants, allocation of customers demand, and selection and order allocation of suppliers. A multi-objective mixed-integer linear programming (MOMILP) is presented with two conflicting objectives simultaneously. The first objective is to minimize the total costs of a supply chain including raw material costs, transportation costs and establishment costs of plants. The second objective function aims to minimize the total deterioration rate occurred by transportation alternatives. Finally, by applying the fuzzy goal programming, the model is solved as a single objective mixed-integer programming model. An experiment study shows that the proposed procedure can provide a promising result to design an efficient supply chain.
https://www.riejournal.com/article_49157_2e64cded042e3cad74b6a5b9c42fc847.pdf
2017-06-01
90
105
10.22105/riej.2017.49157
Supply chain management
Facility location-allocation
fuzzy goal programming
steel industry
S. M.
Arabzad
m.arabzad@yahoo.com
1
Department of Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
M.
Ghorbani
mazaher.ghorbani@gmail.com
2
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
M. J.
Ranjbar
mjr_darab@yahoo.com
3
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
[1] Arabzad, S.M., Ghorbani, M. and Bahrami, M. (2012). Distribution evaluation problem based on data envelopment analysis. International Journal of Supply Chain Management, 1(1), 27-32.
1
[2] Arabzad, S.M., Kamali, A., Naji, B. and Ghorbani, M. (2013). DEA and TOPSIS techniques for purchasing management: the case of aircraft manufacturing industry. International Journal of Logistics Systems and Management, 14(2), 242-260.
2
[3] Amiri, A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research, 171(2), 567–576.
3
[4] Bashiri, M. and Hosseininezhad, S.J. (2009). A fuzzy group decision support system for multi- facility location problems. The International Journal of Advanced Manufacturing Technology, 42(5-6), 533-543.
4
[5] Beamon, B.M. (1998). Supply Chain Design and Analysis: Models and Methods, International Journal of Production Economics, 55(3), 281-294.
5
[6] Cooper, L. (1963). Location–allocation problems. Operations Research, 11(3), 331–343.
6
[7] Drezner, Z. and Hamacher, H.W. (2002). Facility location: Applications and theory. Springer.
7
[8] Farahani R.Z., SteadieSeifi M. and Asgari N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689–1709.
8
[9] Ghorbani, M., Arabzad, S.M. and Shahin, A. (2013). A novel approach for supplier selection based on the Kano model and fuzzy MCDM. International Journal of Production Research, 51(18), 5469-5484.
9
[10] Ghorbani, M., Arabzad, S.M. and Tavakkoli–Moghaddam, R. (2014). Service quality–based distributor selection problem: a hybrid approach using fuzzy ART and AHP–FTOPSIS. International Journal of Productivity and Quality Management, 13(2), 157-177.
10
[11] Ghorbani, M., Arabzad, S.M. and Tavakkoli–Moghaddam, R. (2014). A multi–objective fuzzy goal programming model for reverse supply chain design. International Journal of Operational Research, 19(2), 141-153.
11
[12] Ghorbani, M., Bahrami, M. and Arabzad, S.M. (2012). An Integrated Model for Supplier Selection and Order Allocation; Using Shannon Entropy, SWOT and Linear Programming. Procedia-Social and Behavioral Sciences, 41(1), 521-527.
12
[13] Ghorbani, M., Tavakkoli-Moghaddam, R., Razmi, J. and Arabzad, S.M. (2012). Applying the fuzzy ART algorithm to distribution network design. Management Science Letters, 2(1), 79-86.
13
[14] Harris, I., Mumford, C. L. and Naim, M. M. (2014). A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling. Transportation Research Part E: Logistics and Transportation Review, 66(1), 1-22.
14
[15] Jamalnia, A., Mahdiraji, H. A., Sadeghi, M. R., Hajiagha, S. H. R. and Feili, A. (2014). An integrated fuzzy QFD and fuzzy goal programming approach for global facility location- allocation problem. International Journal of Information Technology & Decision Making, 13(2), 263-290.
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[16] Jiang, J.-L. and Yuan, X.M. (2008). A heuristic algorithm for constrained multi-source Weber problem–The variational inequality approach. European Journal of Operational Research, 187(2), 357–370.
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[17] Jolai, F., Tavakkoli-Moghaddam, R. and Taghipour, M. (2012). A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations. International Journal of Production Research, 50(15), 4279-4293.
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[18] Kanyalkar, A.P. and Adil, G.K. (2005). An integrated aggregate and detailed planning in a multi- site production environment using linear programming. International Journal of Production Research, 43(20), 4431-4454.
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[19] Klose, A. and Drexl, A. (2005). Facility location models for distribution system design.
19
European Journal of Operational Research, 162(1), 4–29.
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[20] Liu, S.C., and Lin, C.C. (2005). A heuristic method for the combined location routing and inventory problem. The International Journal of Advanced Manufacturing Technology, 26(4), 372-381.
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[21] Mahdavi, I., Aalaei, A., Paydar, M. and Solimanpur, M. (2011). Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems. International Journal of Production Research, 47(18), 4991–5017.
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[22] Manzini, R. and Gebennini, E. (2008). Optimization models for the dynamic facility location and allocation problem. International Journal of Production Research, 46(8), 2061-2086.
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[23] Melo, M.T., Nickel, S. and Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401-412.
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[26] Selim, H. and Ozkarahan I. (2009). A supply chain distribution network design model: An
27
interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology, 36(3-4), 401–418.
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[27] Singh, S.P. and Singh, V.K. (2011). Three-level AHP-based heuristic approach for a multi- objective facility layout problem. International Journal of Production Research, 49(4), 1105- 1125.
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[29] Tsai, W. and Hung, Sh. (2009). A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure. International Journal of Production Research, 47(18), 4991–5017.
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[31] Zahir, S. and Sarker, R. (2010). Optimising multi-objective location decisions in a supply chain using an AHP-enhanced goal-programming model. International Journal of Logistics Systems and Management, 6(3), 249-266.
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[32] Zarandi, M.H., Sisakht, A.H. and Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. The International Journal of Advanced Manufacturing Technology, 56(5-8), 809-821.
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35
ORIGINAL_ARTICLE
Extracting of Relationships Between Modern Management Techniques in SME Manufacturing Support and Procurement of Equipment for Oil Companies of Iran
The organization can fully benefit from efforts to improve quality through programs such as total quality management. Just-in-time and total quality management combine to support the successful implementation of agile manufacturing programs which, in turn, result in the organization’s ability to respond rapidly and aggressively to changes in customer demand. We contend that adoption of a market orientation combined with just in time, total quality management, and agile manufacturing programs leads to organizational capabilities of relatively low cost operation, high quality product and service production, and rapid response to changes in customer needs and demand. While studies on total quality management implementation appear to focus on identifying the role of total quality management practices on organizational success, total quality management practices are still directed from within the organization. Market orientation, however, requires more external engagement and shares the same ultimate aim as total quality management implementations.
https://www.riejournal.com/article_49164_17dc3756007dab25833e463416dab43e.pdf
2017-06-01
106
120
10.22105/riej.2017.49164
Market Orientation
Total Quality Management
just-in-time
agile manufacturing
Operational Performance
logistic performance
H.
Zandhessami
h.zand@qiau.ac.ir
1
Department of Industrial Management, Qazvin Branch, Islamic Azad University, Iran
AUTHOR
A.
Rahgozar
2
Department of Industrial Management, Qazvin Branch, Islamic Azad University, Iran
AUTHOR
[1] Adeleye, E.O., Yusuf, Y.Y., 2006. Towards agile manufacturing: Models of competition and performance outcomes. International journal of agile systems and management (1), p.p 93-110
1
[2] Arawati, A. Za’faran, H. (2011), "Enhancing Production Performance and Customer Performance Through Total Quality Management (TQM): Strategies For Competitive Advantage",Procedia Social and Behavioral Sciences 24 ,1650–1662
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[3] Cua, O., McKone, E., Schroeder ,G.,(2001), " Relationships between implementation of TQM,
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JIT, and TPM and manufacturing performance",Journal of Operations Management19.675–694
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Operations Research 38 (1990) 22–36.
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[5] Demirbag, M. Koh, L. Tatoglu, E. Zaim, S. (2006), "TQM and market orientation’s impact on SMEs’ performance", Industrial Management & Data Systems, No. 8, pp. 1206-1228
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[6] Feng, M., Terziovski, M. and Samson, D. (2008), "Relationship of ISO 9001:2000 quality system certification with operational and business performance", Journal of Manufacturing technology management, vol.19 No1, pp.22-37
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[7] Green k.w, Jr and Inman, R.A.(2007), "The impact of JIT-II-selling on organizational performance", Industrial Management and Data systems, vol 107 No.7.p.p1018-35
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[8] Harrison – Wlker L.Jean,( 2001), "the measurement of a market orientation and its impact on
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business performance" Journal of Quality management, No. 6 P.P. 139 –
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Intelligent Enterprise, vol.11 Ni.9, p.13
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[10] Hoang, D.T. and gel, B. (2006), "the impact of total quality management Gn innovation",
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International jaurnal of Quality & Reliability management vol.23.
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37
ORIGINAL_ARTICLE
Competition in Supply Chain Network: Retailers’ Risk Averseness Approach
This paper formulates a competitive supply chain network throughout a mixed integer linear programming problem, considering demand uncertainty and retailers risk averseness. That is, makes the model more realistic in comparison with the others. Employed conditional value at risk method through the data-driven approach, makes the model to be convex and sensitive to the risk averseness level. Finally, the model outputs and its results are illustrated through a numerical example.
https://www.riejournal.com/article_49171_f846f6b5f1ff58019e8826baf392b277.pdf
2017-06-01
121
128
10.22105/riej.2017.49171
competitive supply chain
supply chain network equilibrium
risk averseness
Conditional value at risk
H.
Golpira
herishgolpira@gmail.com
1
Department of Industrial Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Kurdistan, Iran
LEAD_AUTHOR
S. E.
Najafi
2
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
M.
Zandieh
3
Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, G.C., Tehran, Iran
AUTHOR
S.
Sadi-Nezhad
4
Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada
AUTHOR
[1] Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37(4), 668-683.
1
[2] Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269.
2
[3] Jamshidi, R., Ghomi, S. F., & Karimi, B. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876-1886.
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[4] Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, 32(1), 1-19.
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[5] Lou, C. X., & Dai, W. (2012, September). Robust Supply Chain Services System through Optimization Modeling for Enterprises. In 2012 15th International Conference on Network- Based Information Systems (pp. 518-523). IEEE.
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[6] Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research,227(1), 199-215.
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[7] Fahimnia, B., Farahani, R. Z., & Sarkis, J. (2013). Integrated aggregate supply chain planning using memetic algorithm–A performance analysis case study. International Journal of Production Research, 51(18), 5354-5373.
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[8] Liu, S., & Papageorgiou, L. G. (2013). Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega, 41(2), 369-382.
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[9] Hashim, M., Nazim, M., & Nadeem, A. H. (2013). Production-Distribution Planning in Supply Chain Management Under Fuzzy Environment for Large-Scale Hydropower Construction Projects. In Proceedings of the Sixth International Conference on Management Science and Engineering Management (pp. 559-576). Springer London.
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[10] Xiao, T., & Yang, D. (2008). Price and service competition of supply chains with risk-averse retailers under demand uncertainty. International Journal of Production Economics, 114(1), 187- 200.
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[11] Rezapour, S., & Farahani, R. Z. (2014). Supply chain network design under oligopolistic price and service level competition with foresight. Computers & Industrial Engineering, 72, 129-142.
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[12] Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.
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[13] Chen, H. K., Chou, H. W., & Chiu, Y. C. (2007). On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem.Transportation Research Part C: Emerging Technologies, 15(4), 218-234.
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[14] Majumder, P., & Srinivasan, A. (2008). Leadership and competition in network supply chains. Management Science, 54(6), 1189-1204.
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[15] Liu, Z., & Nagurney, A. (2012). Multiperiod competitive supply chain networks with inventorying and a transportation network equilibrium reformulation. Optimization and Engineering, 13(3), 471-503.
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[17] Gui-tao, Z., Hao, S., & Jin-song, H. (2014, June). Research on supply chain network equilibrium problem with multi-type suppliers. In Service Systems and Service Management (ICSSSM), 2014 11th International Conference on(pp. 1-5). IEEE.
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[18] Dong, J., Zhang, D., & Nagurney, A. (2004). A supply chain network equilibrium model with random demands. European Journal of Operational Research, 156(1), 194-212.
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[19] Nagurney, A., Dong, J., & Zhang, D. (2002). A supply chain network equilibrium model. Transportation Research Part E: Logistics and Transportation Review, 38(5), 281-303.
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[20] Nagurney, A., & Dong, J. (2002). Supernetworks: decision-making for the information age. Elgar, Edward Publishing, Incorporated.
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[21] Nagurney, A., Cruz, J., Dong, J., & Zhang, D. (2005). Supply chain networks, electronic commerce, and supply side and demand side risk.European Journal of Operational Research, 164(1), 120-142.
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[22] Dong, J., Zhang, D., Yan, H., & Nagurney, A. (2005). Multitiered supply chain networks: multicriteria decision—making under uncertainty. Annals of Operations Research, 135(1), 155- 178.
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[23] Hamdouch, Y. (2011). Multi-period supply chain network equilibrium with capacity constraints and purchasing strategies. Transportation Research Part C: Emerging Technologies, 19(5), 803- 820.
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[24] Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194.
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25
ORIGINAL_ARTICLE
Classification and Comparison of the Hybrid Collaborative Filtering Systems
Recommender systems have become fundamental applications in overloaded information domains like e-commerce. These systems aim to provide users with suggestions about items that are likely to be of their interest. Collaborative Filtering (CF) is one of the most successful approaches in recommender systems. Regardless of its success in many application domains, CF has main limitations such as sparsity, cold start, gray sheep and scalability problems. In order to overcome these limitations, hybrid CF systems have been used which combine CF with other recommendation approaches. This paper provides a comprehensive survey of hybrid CF systems; it also provides a classification for these systems, explains their strengths or weaknesses and compares their performance in dealing with the main limitations of CF.
https://www.riejournal.com/article_49158_d142dd4df0a07272af6fe11cd4e7c20c.pdf
2017-06-01
129
148
10.22105/riej.2017.49158
Recommender systems
collaborative filtering
hybrid collaborative
filtering systems
F. S.
Gohari
fgohari@mail.kntu.ac.ir
1
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
M.J.
Tarokh
mjtarokh@kntu.ac.ir
2
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
AUTHOR
[1] Manber, U., Patel, A. and Robison, J. (2000). Yahoo!. Communications of the ACM, Vol. 43, No. 8, p. 35.
1
[2] Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, Vol. 12, No. 4, pp. 331–370.
2
[3] Schafer, J. B., Konstan, J. and Riedi, J. (1999). Recommender systems in e-commerce. In
3
Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166.
4
[4] Vozalis, M. G. and Margaritis, K. G. (2007). Using SVD and demographic data for the enhancement of generalized collaborative filtering. Information Sciences, Vol. 177, No. 15, pp. 3017– 3037.
5
[5] Choi, K. and Suh, Y. (2013). A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems, Vol. 37, pp. 146–153.
6
[6] Schafer, J. B., Frankowski, D., Herlocker, J. and Sen, S. (2007). Collaborative filtering recommender systems. The adaptive web. Springer Berlin Heidelberg, pp. 291-324.
7
[7] Lee, W. S. (2001). Collaborative learning for recommender systems. In MACHINE LEARNING- INTERNATIONAL WORKSHOP THEN CONFERENCE-, pp. 314–321.
8
[8] Guo, G. (2013, August). Improving the performance of recommender systems by alleviating the data sparsity and cold start problems. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, AAAI Press, pp. 3217-3218.
9
[9] Lops, P., Degemmis, M. and Semeraro, G. (2007). Improving social filtering techniques through WordNet-Based user profiles. User Modeling 2007. Springer Berlin Heidelberg, pp. 268-277.
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[10] Lika, B., Kolomvatsos, K. and Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, Vol. 41, No. 4, pp. 2065–2073.
11
[11] Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, Vol. 46, pp. 109–132.
12
[12] Liu, H., Hu, Z., Mian, A., Tian, H. and Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, Vol. 56, pp. 156–166.
13
[13] Sun, D., Luo, Z. and Zhang, F. (2011). A novel approach for collaborative filtering to alleviate the new item cold-start problem. In Communications and Information Technologies (ISCIT), 2011 11th International Symposium on, IEEE, pp. 402–406.
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[14] Ghazanfar, M. A. and Prügel-Bennett, A. (2014). Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Systems with Applications, Vol. 41, No. 7, pp. 3261–3275.
15
[15] Su, X. and Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques.
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Advances in Artificial Intelligence, p. 4.
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[16] Koenigstein, N. and Koren, Y. (2013). Towards scalable and accurate item-oriented recommendations. In Proceedings of the 7th ACM conference on Recommender systems, pp. 419–422.
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[17] Kumar, A. and Sharma, A. (2012). Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender Systems. In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA): Theory and Applications (FICTA), Springer, Vol. 199, p. 103.
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[18] Herlocker, J. L., Konstan, J. A., Terveen, L. G. and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), Vol. 22, No. 1, pp. 5–53.
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[19] Perugini, S., Gonçalves, M. A. and Fox, E. A. (2004). Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, Vol. 23, No. 2, pp. 107–143.
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[20] Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions On, Vol. 17, No. 6, pp. 734–749.
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[21] Candillier, L., Meyer, F. and Boullé, M. (2007). Comparing state-of-the-art collaborative filtering systems. In Machine Learning and Data Mining in Pattern Recognition, Springer, pp. 548– 562.
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[22] Park, D. H., Kim, H. K., Choi, I. Y. and Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, Vol. 39, No. 11, pp. 10059–10072
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[23] Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K. and Zhou, T. (2012). Recommender systems. Physics Reports, Vol. 519, No. 1, pp. 1–49.
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108
ORIGINAL_ARTICLE
Dynamic Pricing Decisions for Substitutable Products
This paper considers a dynamic pricing decision problem, in which two different manufacturers compete to distribute substitutable products through a single retailer under two presented scenarios. In the first scenario, the pricing policy is determined via a centralized decision-making, while the second scenario manages the policy in a decentralized one. Utilizing the game-theory-based modeling approaches, the pricing decision problem is achieved under with two different structures. Numerical experiments are also given to examine the effects of the presented scenarios and provide further managerial insights on the solutions.
https://www.riejournal.com/article_49170_ef3b38c8916e65c7af62b93484127251.pdf
2017-06-01
149
160
10.22105/riej.2017.49170
game theory
Supply chain
pricing decisions
substitutable product
S.
Aliari-Kardehdeh
sanazaliari@gmail.com
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
S.
Ayazi-Yazdi
2
Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
AUTHOR
R.
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
H.
Farrokhi-Asl
hamed.farrokhi@ut.ac.ir
4
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Liu, G., Zhang, J., and Tang, W. (2015). "Strategic transfer pricing in a marketing- operations interface with quality level and advertising dependent goodwill," Omega, 56: 1-15.
1
[2] Huang, H., and Ke, H. (2014). "Pricing decision problem for substitutable products based on uncertainty theory," Journal of Intelligent Manufacturing, 28:1-12.
2
[3] Anderson E. J., and Bao, Y. (2010). "Price competition with integrated and decentralized supply chains," European Journal of Operational Research, 200: 227-234.
3
[4] Gao, J., Han, H., Hou, L., and Wang, H. (2015). "Pricing and effort decisions in a closed- loop supply chain under different channel power structures," Journal of Cleaner Production, Article in Press..
4
[5] Rodríguez B., and Aydın, G. (2015). "Pricing and assortment decisions for a manufacturer selling through dual channels," European Journal of Operational Research, 242: 901-909.
5
[6] Zhou, E., Zhang, J., Gou, Q., and Liang, L. (2015). "A two period pricing model for new fashion style launching strategy," International Journal of Production Economics, 160: 144-156.
6
[7] Jia J., and Zhang, J. (2013). "Dynamic ordering and pricing strategies in a two-tier multi- generation durable goods supply chain," International Journal of Production Economics, 144: 135-142.
7
[8] Chen J. M., and Chang, C. I. (2013). "Dynamic pricing for new and remanufactured products in a closed-loop supply chain," International Journal of Production Economics, 146: 153-160.
8
[9] Jia, J., and Hu, Q. (2011). "Dynamic ordering and pricing for a perishable goods supply chain," Computers & Industrial Engineering, 60: 302-309.
9
[10] Chen, Y. C., Fang, S. C., and Wen, U. P. (2013). "Pricing policies for substitutable products in a supply chain with Internet and traditional channels," European Journal of Operational Research, 224: 542-551.
10
ORIGINAL_ARTICLE
Non-Standard Finite Difference Schemes for Solving Singular Lane-Emden Equation
In this paper we construct Non-Standard finite difference schemes (NSFD) for numerical solution of nonlinear Lane-Emden type equations which are nonlinear ordinary dierential equations on semi-infinite domain. They are categorized as singular initial value problems. This equation describes a variety of phenomena in theoretical physics and astrophysics. The presented schemes are obtained by using the Non-Standard finite difference method. The use of NSFD method and its approximations play an important role for the formation of stable numerical methods. The main advantage of the schemes is that the algorithm is very simple and very easy to implement. Thus, this method may be applied as a simple and accurate solver for ODEs and PDEs and it can also be utilized as an accurate algorithm to solve linear and nonlinear equations arising in physics and other fields of applied mathematics. Illustrative examples have been discussed to demonstrate validity and applicability of the technique and the results have been compared with the exact solutions.
https://www.riejournal.com/article_50382_48c8abdb20347aa1aecccb32f92c955a.pdf
2017-06-01
161
171
10.22105/riej.2017.92303.1001
Non-Standard finite difference
Lane-Emden equation
Astrophysics
H.
Saberi Najafi
hashemsaberinajafi@yahoo.com
1
Department of Applied Mathematics, School of Mathematical Sciences, University of Guilan, Rasht, Iran.
AUTHOR
A.
Yaghoubi
abyaghoobi@phd.guilan.ac.ir
2
Department of Applied Mathematics, School of Mathematical Sciences, University of Guilan, University Campus2, Rasht, Iran.
LEAD_AUTHOR
[1] H. Aminikhah, S. Moradian, (2013). Numerical Solution of Singular Lane-Emden Equation. Hindawi Publishing Corporation ISRN Mathematical Physics, 2013, 1-9.
1
[2] S. Liao, (2003). a new analytic algorithm of Lane-Emden type equations. Applied Mathematics and Computation, 142 (1), 1–16.
2
[3] J. H. He, (2003). Variational approach to the Lane-Emden equation. Applied Mathematics and Computation, 143 (2), 539–541.
3
[4] C. M. Bender, K. A. Milton, S. S. Pinsky, and L. M. Simmons, Jr., (1989). A new perturbative approach to nonlinear problems. Journal of Mathematical Physics, Vol. 30 (7), 1447–1455.
4
[5] N. T. Shawagfeh, (1993). Non-perturbative approximate solution for Lane-Emden equation. Journal of Mathematical Physics, 34 (9), 4364–4369.
5
[6] A. M. Wazwaz, (2001). a new algorithm for solving differential equations of Lane-Emden type. Applied Mathematics and Computation, 118 (2), 287–310.
6
[7] A. M. Wazwaz, (2002). a new method for solving singular initial value problems in the second-order ordinary differential equations. Applied Mathematics and Computation, 128 (1), 45–57.
7
[8] J. I. Ramos, (2008). Series approach to the Lane-Emden equation and comparison with the homotopy perturbation method. Chaos, Solitons and Fractals, 38 (2), 400–408.
8
[9] K. Parand and M. Razzaghi, (2004). Rational Chebyshev tau method for solving higher-order ordinary differential equations. International Journal of Computer Mathematics, Vol. 81 (1), 73–80.
9
[10] K. Parand and M. Razzaghi, (2004). Rational Legendre approximation for solving some physical problems on semi-infinite intervals. Physica Scripta, 69, 353–357.
10
[11] K. Parand, M. Shahini, and M. Dehghan, (2009). Rational Legendre pseudospectral approach for solving nonlinear differential equations of Lane-Emden type. Journal of Computational Physics, 228 (23), 8830– 8840.
11
[12] K. Parand, M. Dehghan, A. R. Rezaei, and S. M. Ghaderi, (2010). an approximation algorithm for the solution of the nonlinear Lane-Emden type equations arising in astrophysics using Hermite functions collocation method. Computer Physics Communications, 181 (6), 1096–1108.
12
[13] M. El-Gebeily and D. O’Regan, (2007). a quasilinearization method for a class of second order singular nonlinear differential equations with nonlinear boundary conditions. Nonlinear Analysis: Real World Applications, 8, 174–186.
13
[14] V. B. Mandelzweig and F. Tabakin, (2001). Quasi linearization approach to nonlinear problems in physics with application to nonlinear ODEs. Computer Physics Communications, 141 (2), 268–281.
14
[15] J. I. Ramos, (2003). Linearization methods in classical and quantum mechanics. Computer Physics Communications, 153 (2), 199–208.
15
[16] Y. Bozhkov and A. C. Gilli Martins, (2004). Lie point symmetries and exact solutions of quasilinear differential equations with critical exponents. Nonlinear Analysis: Theory, Methods & Applications, 57 (5), 773–793.
16
[17] E. Momoniat and C. Harley, (2006). Approximate implicit solution of a Lane-Emden equation. New Astronomy, 11, 520–526.
17
[18] T. Özis and A. Yildirim, (2007). Solutions of singular IVP’s of Lane-Emden type by homotopy pertutbation method. Physics Letters A, 369, 70–76.
18
[19] T. Özis and A. Yildirim, (2009). Solutions of singular IVPs of Lane-Emden type by the variational iteration method. Nonlinear Analysis: Theory, Methods & Applications, 70, 6, 2480–2484.
19
[20] Ronald E. Mickens, (2001). A Non-Standard Finite Difference Scheme for a Nonlinear PDE Having Diffusive Shock Wave Solutions. Mathematics and Computers in Simulation, 55, 549-555.
20
[21] Ronald E. Mickens, (2003). A Non-Standard Finite Difference Scheme for a Fisher PDE Having Nonlinear Diffusion, Diffusion. Comput. Math. App, 145, 429-436.
21
[22] Ronald E. Mickens, (2005). A Non-Standard Finite Difference Scheme for a PDE Modeling Combustion with Nonlinear Advection and Diffusion. Mathematics and Computers in Simulation, 69, 439-446.
22
[23] Ronald E. Mickens, (2005). Advances in the Applications of Non-Standard Finite Difference Schemes. World Scientific, Singapore.
23
[24] Ronald E. Mickens, (2006). Calculation of Denominator Functions for Non-Standard Finite Difference Schemes for Differential Equations Satisfying a Positivity Condition. Wiley Inter Science, 23, 672-628.
24
[25] Ronald E. Mickens, (2007). Determination of Denominator Functions for a NSFD Scheme for the Fisher PDE with Linear Advection. Mathematics and Computers in Simulation, 74, 127-195.
25
[26] Alvarez-Ramirez, J. Valdes, (2009). Non-Standard Finite Differences schemes for Generalized Reaction- Diffusion Equations. Computational and Applied Mathematics, 228, 334-343.
26
[27] Benito M. Chen-Charpentier, Dobromir T. Dimitrov, Hristo V. Kojouharov, (2006). Combined Non- Standard Numerical Methods for ODEs with Polynomial Right-Hand Sides. Mathematics and Computers in Simulation,73, 105-113.
27
[28] Elizeo Hernandez-Martinez, Francisco J. Valdes-Prada, Jose Alvarez-Ramirez, (2011). A Greens Function Formulation of Nonlocal Finite-Difference Schemes for Reaction-Diffusion Equations. Computational and Applied Mathematics, 235, 3096-3103.
28
[29] K. Moaddy, S. Momani, I. Hashim, (2011). The Non-Standard Finite Difference Scheme for linear Fractional PDEs in Fluid Mechanics. Computers and Mathematics with Applications, 61, 1209-1216.
29
ORIGINAL_ARTICLE
Solving Monetary (MIU) models with Linearized Euler Equations: Method of Undetermined Coefficients
This paper attempts to solve a benchmark money in utility model by first order Taylor approximation to the policy function. After a brief summary of recent development in first order Taylor approximation in solving dynamic stochastic general equilibrium models, we choose Sidrauski’s Money in utility model as a standard model and follow the approach proposed by Uhlig [1] to solve for the recursive law of motion at first order.
https://www.riejournal.com/article_50102_bf9ec25631c2f41f99defade31d3ffe5.pdf
2017-06-01
172
183
10.22105/riej.2017.94115.1005
DSGE Model
Calibration
monetary models
S.F.
Fakhrehosseini
f_fkm21@yahoo.com
1
Department of Business Management, Tonekabon Branch, Islamic Azad University, Iran
LEAD_AUTHOR
Meysam
kaviani
meysamkaviani@gmail.com
2
Department of Management, Aliabad katoul branch, Islamic Azad University, Iran
AUTHOR
[1] Uhlig, H. (1995). A toolkit for analyzing nonlinear dynamic stochastic models easily.
1
[2] Blanchard, O. J., & Kahn, C. M. (1980). The solution of linear difference models under rational expectations. Econometrica: Journal of the Econometric Society, 1305-1311.
2
[3] Walsh, C. E. (2003). Labor market search and monetary shocks. Elements of Dynamic Macroeconomic Analysis, 451-486.
3
[4] Ruge-Murcia, F. J. (2007). Methods to estimate dynamic stochastic general equilibrium models. Journal of Economic Dynamics and Control, 31(8), 2599-2636.
4
[5] TAEI, H. (2007). An Estimation of Labour Supply Function Using the Iranian Micro Data.
5
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