ORIGINAL_ARTICLE
A New Fuzzy Stabilizer Based on Online Learning Algorithm for Damping of Low-Frequency Oscillations
A multi objective Honey Bee Mating Optimization (HBMO) designed by online learning mechanism is proposed in this paper to optimize the double Fuzzy-Lead-Lag (FLL) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. The proposed double FLL stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the proposed multi objective optimization process. A multilayer adaptive network is employed to design the fuzzy logic controller with self-learning capability that does not require another controller to tune the fuzzy inference rules and membership functions. In the proposed online learning algorithm, two artificial neural networks are employed which this system makes the FLL stabilizer adaptive to changes in the operating conditions. Therefore, variation in the power system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional controller. The effectiveness of the proposed stabilizer has been employed by simulation studies. The effectiveness of the proposed stabilizer is demonstrated on Two-Area Four-Machine (TAFM) power system under different loading conditions.
http://www.qjie.ir/article_168_53c7cae8932e1f16c04f4333346b2768.pdf
2015-12-14
1
10
Online Learning algorithm
multi objective optimization
multi machine
small signal stability
HBMO
Fuzzy stabilizer
Ali
Ghasemi
ghasemi.agm@gmail.com
1
Instructor , Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
LEAD_AUTHOR
Mohammad Javad
Golkar
2
MSc, Department of Control, Imam Mohammad Bagher University, Sari, Iran
AUTHOR
Mohammad
Eslami
electric_sina@yahoo.com
3
MSc, Department of Electrical and Computer, College of Engineering, Khash branch, Islamic Azad University, Khash,
AUTHOR
Abd-Elazim S.M., Ali E.S., (2013) “A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design,” International Journal Electric Power Energy Syst, vol. 46, pp. 334–341.
1
Baek S. M., Park J. W., Venayagamoorthy G. K., (2008) “Power System Control With an Embedded Neural Network in Hybrid System Modeling,” IEEE Trans. Ind. Applications, vol. 44, no. 5, pp. 1458- 1465.
2
Dounis I. A., Kofinas P., Alafodimos C., Tseles D., (2013) “Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system,” Renewable Energy, vol. 60, pp. 202-214.
3
Ghasemi A., (2013) “A Fuzzified Multi Objective Interactive Honey Bee Mating Optimization for Environmental/Economic Power Dispatch with valve point effect,” Int J Elec Power Energy Syst, vol. 49, pp. 308–321.
4
Ghasemi A., Abido M.A., (2012) “Optimal Design of Power System Stabilizers: A PSO-IIW Procedure,” 27th International Power System Conference, pp. 1-11.
5
Ghasemi A., Shayeghi H., Alkhatib H., (2013) “Robust Design of Multimachine Power System Stabilizers using Fuzzy Gravitational Search Algorithm,” Int J Elec Power Energy Syst, vol. 51, pp. 190-200.
6
Gonzalez M. R., Malik O. P., (2008) “Power System Stabilizer Design Using an Online Adaptive Neurofuzzy Controller With Adaptive Input Link Weights,” IEEE Trans. Energy Conversion, vol. 23, no. 3, pp. 914- 922.
7
Javidan J., Ghasemi A., (2012) “Environmental/Economic Power Dispatch Using Multi-Objective Honey Bee Mating Optimization,” Int Review Elec Engineering, vol. 7, no. 1, pp. 3667-3675.
8
Lu C. F., Hsu C. H., Juang C. F., (2013) “Coordinated Control of Flexible AC Transmission System Devices Using an Evolutionary Fuzzy Lead-Lag Controller With Advanced Continuous Ant Colony Optimization,” IEEE Trans. Power Systems, vol. 28, no. 1, pp. 385-392.
9
Mishra S., Tripathy M., Nanda J., (2007) “Multi-machine power system stabilizer design by rule based bacteria foraging,” Electric Power Systems Research, vol. 77, pp. 1595–1607.
10
Mostafa H. E., El-Sharkawy M. A., Emary A. A., Yassin K., (2012) “Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system,” International Journal Electric Power Energy Syst, vol. 34, pp. 57–65.
11
Noshyar M., Shayeghi H., Talebi A., Ghasemi A., Tabatabaei N.M., (2013) “Robust fuzzy-PID controller to enhance low frequency oscillation using improved particle swarm optimization,” International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Vol.5, No. 1, pp. 17-23.
12
Park J.-W., Venayagamoorthy G. K., Harley R. G., (2005) “MLP/RBF neural-networks-based online global model identification of synchronous generator,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 1685–1695.
13
Shabib G., (2012) “Implementation of a discrete fuzzy PID excitation controller for power system damping,” Ain Shams Engineering Journal, vol. 3, pp. 123-131.
14
Shayanfar H.A., Abedinia O., Mohammad. S. Naderi, Ghasemi A., (2011) “GSA to Tune Fuzzy Controller for Damping Power System Oscillation,” InProceedings of the international conference on artificial intelligence, Las Vegas, Nevada, pp: 713-719.
15
Shayeghi H., Ghasemi A., (2011) “Improved Time Variant PSO Based Design of Multiple Power System Stabilizer, “Int Review Elec Engineering, Vol. 6, No. 5, pp. 2490-2501, 2011.
16
Shayeghi H., Ghasemi A., (2011) “Multiple PSS Design Using an Improved Honey Bee Mating Optimization Algorithm to Enhance Low Frequency Oscillations, “Int Review Elec Engineering, vol. 6, no. 7, pp. 3122-3133.
17
Shayeghi H., Ghasemi A., (2012) “Optimal design of power system stabilizer using improved ABC algorithm,” Int J Technical Physical Prob Engineering, vol. 4, no. 3, pp. 24-31.
18
Shayeghi H., Ghasemi, A. (2014) “A multi objective vector evaluated improved honey bee mating optimization for optimal and robust design of power system stabilizers,” Electrical Power and Energy Systems, vol. 62, pp. 630–645.
19
Shayeghi H., Shayanfar H.A., Jalili A., Ghasemi A., (2010) “LFC design using HBMO technique in interconnected power system,” International Journal on “Int J Technical Physical Prob Engineering, vol. 2, no. 4, pp. 41-48.
20
Wang S. K., (2013) “A Novel Objective Function and Algorithm for Optimal PSS Parameter Design in a Multi-Machine Power System,” IEEE Trans. Power Systems, vol. 28, no. 1, pp. 522- 531.
21
Yassami H., Darabi A., Rafiei S.M.R., (2010) “Power system stabilizer design using Strength Pareto multi-objective optimization approach,” Electric Power Systems Research, vol. 80, pp. 838–846.
22
ORIGINAL_ARTICLE
Multimodal Transportation p-hub Location Routing Problem with Simultaneous Pick-ups and Deliveries
Centralizing and using proper transportation facilities cut down costs and traffic. Hub facilities concentrate on flows to cause economic advantage of scale and multimodal transportation helps use the advantage of another transporter. A distinctive feature of this paper is proposing a new mathematical formulation for a three-stage p-hub location routing problem with simultaneous pick-ups and deliveries on time. A few studies have been devoted to this problem; however, many people are still suffering from the problems of commuting in crowded cities. The proposed formulation controlled the tumult of each node by indirect fixed cost. Node-to-node traveling cost was followed by a vehicle routing problem between nodes of each hub. A couple of datasets were solved for small and medium scales by GAMS software. But, for large-scale instances, a meta-heuristic algorithm was proposed. To validate the model, datasets were used and the results demonstrated the performance suitability of the proposed algorithm.
http://www.qjie.ir/article_169_fc5f5e1e126c6e36bbd48d98fc977325.pdf
2015-12-14
11
20
hub location routing problem
multimodal transportation
economic optimal design
traffic optimal design
Genetic Algorithm
Saeed
Zameni
zameni@ut.ac.ir
1
MSc, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Jafar
Razmi
2
Professor, Faculty of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Alumur, S. A., Kara, B. Y., & Yaman, H. (2012a). Hierarchical multimodal hub location problem with time definite deliveries. Transportation Research Part E 48, 1107-1120.
1
Alumur, S., Kara, B. Y., & Karasan, O. E. (2012b). Multimodal hub location and hub network design. OMEGA 40, 927-939.
2
Alumur, S; Kara, B Y. (2008). Invented review network hub location problems: The state of the art. European journal of operational research 190, 1-21.
3
Camargo, R. S., Miranda, G. D., & Løkketangen, A. (2013). A new formulation and an exact approach for the many-to-many hub location routing problem. Applied Mathematical Modeling 37, 7465-7480.
4
Eskigun, E., RehaUzsoy, Preckel, P. V., Beaujon, G., Krishnan, S., & Tew, J. D. (2005). Outbound supply chain network design with mode selection, lead times and capacitatedvehicle distribution centers. European Journal of Operational Research 165, 182–206.
5
Gelareh, S., Maculan, Philip, M. N., & NematianMonemi, R. (2013). Hub and spoke network design and fleet deployment for string planning of liner shipping. Applied mathematical modeling 37, 3307-3321.
6
Gupta, R., & Pirkul, H. (2000). Theory and Methodology Hybrid fiber co-axial CATV network design with variable capacity optical network units. European Journal of Operational Research 123, 73-85.
7
Hayuth, Y. (1987). Intermodality: Concept and Practice. London: Lloyds of London Press.
8
Julai, F., Razmi, J., & Rostami, N. K. (2011). A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem. Central European journal of operation research 19(4), 547-569.
9
Norouzi, N., Razmi, J., & Amalnik, S. (2012). Consume optimization of a vehicle routing problem with IPSO algorithm. University of Tehran journal of industrial engineering 47, 105-112.
10
Rabbani, M., Zameni, S., & Kazemi, S. M. (2013). Proposing a new mathematical formulation for modeling costs in a p-hub center problem. ICMSAO. Tunisia: IEEE.
11
Razmi, J., & Rahmanniya, F. (2013). Design of distribution network using hub location model with regard to capacity constraint and service level. International Journal of Logistics Systems and Management 16, 386-398.
12
Tancrez, J. S., lange, J. C., & Semal, P. (2012). A location-inventory model for large three-level supply chains. Transportation research part E 48, 485-502.
13
Van Schijndel, W. J., & Dinwoodie, J. (2000). Congestion and multimodal transport: a survey of cargo transport operators in the Netherlands. Transport Policy 7, 231-241.
14
Zhang, J., Liao, F., Arentze, T., & Timmermans, H. (2011). A multimodal transport network model for advanced traveler information systems. Procedia Computer Science 5, 912–919.
15
Zhi-Hua, H. (2011). A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Systems with Applications 38, 2632–2639.
16
ORIGINAL_ARTICLE
Developing a Permutation Method Using Tabu Search Algorithm: A Case Study of Ranking Some Countries of West Asia and North Africa Based on Important Development Criteria
The recent years have witnessed an increasing attention to the methods of multiple attribute decision making in solving the problems of the real world due to their shorter time of calculation and easy application. One of these methods is the ‘permutation method’ which has a strong logic in connection with ranking issues, but when the number of alternatives increases, solving problems through this method becomes NP-hard. So, meta-heuristic algorithm based on Tabu search is used to find optimum or near optimum solutions at a reasonable computational time for large size problems. This research is an attempt to apply the ‘permutation method’ to rank some countries of the West Asia and the North Africa based on the development criteria. Knowing the situation of each country as compared with other countries, particularly the respective neighbouring countries, is one of the most important standards for the assessment of performance and planning for the future activities.
http://www.qjie.ir/article_170_e4e160f3e9d111f35a6c82e5c4248c39.pdf
2015-12-14
21
30
Multiple Attribute Decision Making
Permutation Method
Tabu Search Algorithm
Countries Ranking
Combinatorial Problem
Javad
Rezaeian
j.rezaeian@ustmb.ac.ir
1
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Mazandaran University of Science and Technology, Babol, Iran
LEAD_AUTHOR
Keyvan
Shokoufi
k.shokoufi@ustmb.ac.ir
2
MSc, Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
Shahab
Poursafary
shahab_spt@yahoo.com
3
MSc, Department of Industrial Engineering, Faculty of Engineering, Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
Ahn, B.S., Park,K.S. (2008). Comparing methods for multiattribute decision making with ordinal weights. Computers & Operations Research. 35:1660-1670.
1
Bashiri, M., AliAskari, E. (2014). A permutation decision making method with multiple weighting vectors of criteria using NSGA-II and MOPSO. Decision Science Letters. 3(2): 197–208.
2
Bashiri, M., Jalili, M. (2010). Interactive permutation decision making based on genetic algorithm. Proceedings of the IEEE IEEM, 84-88.
3
Bashiri, M., Koosha, M., Karimi, H. (2012). Permutation based decision making under fuzzy environment using Tabu search. International Journal of Industrial Engineering Computations. 3: 301–312.
4
Blair, R., Karnisky, W. (1994). Distribution-free statistical analysis of surface and volumetric maps. Brain Topography. 6: 19–28.
5
Bohn, H. (1998). Why do we have nominal government debt?. Journal of Monetary Economics. 21(1): 127-140.
6
Brown, A.J.G., Orszag, J.M., Snower, D.J. (2008). Unemployment accounts and employment incentives. European journal of political economy. 24(3):587-604.
7
Chinn, M.D., Ito, H. (2007). Current account balances, financial development and institutions: Assaying the world “saving glut”. Journal of International Money and Finance. 26(4):546-569.
8
Costamagna, E., Fanni, A., Giacinto, G. (1998). A Tabu Search Algorithm for the Optimization of Telecommunication Networks. EJOR 106: 357-372.
9
European Journal of Operational Research. 63:361-375.
10
Fawcett, S.E., Cooper, M.B. (1998). Logistics performance measurement and customer success. Industrial Marketing Management. 27(4):341-357.
11
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers& Operations Research. 13(5): 533-549.
12
Havrilesky, T. (1967). A test of monetary policy action, The Journal of Political Economy. JSTOR. 75(3): 299-304.
13
Hwang, C.L., Yoon, K.S. (1981). Multiple attribute decision making. Methods and applications: A state of the art survey (Berlin: Springer-Verlag).
14
Karimi, H., Rezaeinia, A. (2011) Adjusted permutation method for multiple attribute decision making with meta-heuristic solution approaches. International Journal of Industrial Engineering Computations. 2:369-384.
15
Korhonen, P., Moskowitz, H., Wallenius, J. (1992). Multiple Criteria Decision Support: A review.
16
Lim, S.H. (2008). How investment promotion affects attracting foreign direct investment: Analytical argument and empirical analyses. International Business Review. 17(1):39-53.
17
Marattin, L., Salotti, S. (2001).Productivity and per capita GDP growth: The role of the forgotten factors. Economic Modelling. 28(3):1219-1225.
18
Moulton, B.R., Stewart, K.J. (1999). An Overview of Experimental U.S. Consumer Price Index. Journal of Business & Economic Statistics. 17:141–151.
19
Paelinck, J. (1977). Qualitative multiple criteria analysis: an application to airport location. En'liironment and Planning. 9: 893–695.
20
Pantazis, D., Nichols, T., Baillet, S., Leahy, R. (2003). Spatiotemporal localization of significant activation in MEG using permutation tests. 18th Conference on Information Processing in Medical Imaging. 512– 523.
21
Pinheiro-Alves, R., Zambujal-Oliveira, J. (2012). The Ease of Doing Business Index as a tool for investment location decisions. Economics Letters. 117(1):66-70.
22
Rinnooy, K. (1976). Machine Scheduling Problems: Classification, Complexity, and Computations, Nijhoff, The Hague.
23
Sterzik, S., Kopfer, H. (2013).A Tabu Search Heuristic for the Inland Container Transportation Problem. Computers & Operations Research. 40:953-962.
24
Tavana, M., Zandi, F. (2012). Applying fuzzy bi-dimensional scenario-based model to the assessment of Mars mission architecture scenarios. Journal of Advances in Space Research. 49:629–647.
25
The World Bank, Connecting to Compete : Trade Logistics in the Global Economy . Washington DC; 2010.
26
Turskis, Z. (2008). Multi-Attribute Contractors Ranking Method by Applying Ordering of Feasible Alternatives of Solutions in Terms of Preferability Technique. Technologic and Economic Development. 14: 224–239.
27
UNIDO, Industrial energy efficiency for sustainable wealth creation: Capturing environmental, economic and social dividends. Connecting to Compete: Trade Logistics in the Global Economy, Industrial Development Report 2011, UNIDO-United Nation Industrial Development Organization; 2011.
28
Zavadskas, E.K., Turskis, Z., Tamosaitiene, J. (2011). Selection of construction enterprises management strategy based on the SWOT and multi-criteria analysis. Archives of Civil and Mechanical Engineering. 11:1063-1082.
29
ORIGINAL_ARTICLE
DEA with Missing Data: An Interval Data Assignment Approach
In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the proposed approach suggests using an acceptable range for missing inputs and outputs, which is determined by the decision maker (DM). Then, applying the least favourable bounds of missing data along with using the proposed range is suggested in estimating the production frontier. A data set is used to illustrate the approach.
http://www.qjie.ir/article_171_31fbbfc8fc432bbba3f210f829603715.pdf
2015-12-14
31
36
Data envelopment analysis
Missing inputs
Missing outputs
Range
Reza
Kazemi Matin
ekmatin@kiau.ac.ir
1
Associate Professor, Department of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran
LEAD_AUTHOR
Roza
Azizi
2
MSc, Department of Mathematics, Islamic Azad University, Karaj Branch, Karaj , Iran
AUTHOR
Azizi H. (2013), “A note on data envelopment analysis with missing values: an interval DEA approach”, The International Journal of Advanced Manufacturing Technology, 66(9-12), 1817-1823.
1
Banker, RD, Charnes, A., Cooper, WW. (1984), “Models for Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis”, Management Sience, 30, 1078-1092.
2
Charnes A, Cooper WW. (1962), “Programming with linear fractional functional”, Naval Research Logistics Quarterly, 9, 181-185.
3
Charnes A, Cooper WW, Rhodes E. (1978), “Measuring the efficiency of decision making units”, European journal of operational research, 2(4), 429 - 444.
4
Cooper WW, Seiford LM, Tone K. (2000), Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, Boston.
5
Farrell MJ. (1957), “The Measurement of Productive Efficiency”, Journal of Royal Statistical Society, 120(3), 253-281.
6
Kao C, Liu ST, (2000), “Data envelopment analysis with missing data: An application to University Libraries in Taiwan”, Journal of the Operational Research Society, 51 (8), 897–905.
7
Kuosmanen, T. (2009), Data envelopment analysis with missing data, Journal of the Operational Research Society, 60, 1767-1774.
8
Neal, PVO, Ozcan, YA, Yanqiang, M. (2002), “Benchmarking mechanical ventilation services in teaching hospitals”, Journal of Medical Systems. 26(3), 227–240.
9
Smirlis YG, Maragos EK, Despotis DK. (2006), “Data envelopment analysis with missing values: An interval DEA approach”. Applied Mathematics and Computation, 177(1), 1-10.
10
Zha Y, Song A, Xu Ch, Yang H. (2013), “Dealing with missing data based on data envelopment analysis and halo effect”, Applied Mathematical Modelling, 37(9), 6135–6145.
11
ORIGINAL_ARTICLE
A Bendersï¿½ Decomposition Approach for Dynamic Cellular Manufacturing System in the Presence of Unreliable Machines
In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a dynamic environment is proposed. Also because of the complexity of the given problem, a Benders’ decomposition approach is applied to solve the problem efficiently. In order to verify the performance of proposed approach, some numerical examples are generated randomly in hypothetical limits and solved by the proposed solution approach. The comparison of the implemented solution algorithm with the conventional mixed integer linear and mixed integer non linear models verifies the efficiency of Benders’ decomposition approach especially in terms of computational time.
http://www.qjie.ir/article_172_13dc260c119599b1978739a1ed5b6142.pdf
2015-12-14
37
49
Cellular manufacturing system
Bendersï¿½ decomposition approach
Machine reliability
Machine utilization factor
Masoud
Bagheri
masoud.86.68@gmail.com
1
Ph. D Student, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Saeed
Sadeghi
saeed_sadeghi68@yahoo.com
2
MSc, Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Mohammad
Saidi-Mehrabad
mehrabad@iust.ac.ir
3
Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ameli, M. S. and Arkat, J. (2008). Cell formation with alternative process routings and machine reliability consideration. International Journal of Advanced Manufactirong Technology, 35, 761–768.
1
Aryanezhad, M. B., Deljoo, V. and Mirzapour Al-e-hashem, S. M. (2009). Dynamic cell formation and the worker assignment problem: a new model. International Journal of Advanced Manufacturing Technology, 41, 329–342.
2
Bagheri, M., Bashiri, M. (2014). A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment. Applied Mathematical Modeling, 38, 1237-1254.
3
Bagheri, M., Bashiri, M. (2014). A hybrid Genetic and Imperialist Competitive Algorithms (GICA) approach to dynamic Cellular Manufacturing System . Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(3), 2014, 458-470.
4
Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4, 238–252.
Dimopoulos, C., Zalzala, A. (2000). Recent developments in evolutionary computations for manufacturing optimization: problems, solutions, and comparisons. IEEE Transactions on Evolutionary Computation, 4, 93–113.
5
Ghezavati, V. R., and Saidi-Mehrabad, M. (2011). An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis. Expert Systems with Applications, 38, 1326-1335.
6
Ghotboddini, M., Rabbani, M., and Rahimian, H. (2011). A comprehensive dynamic cell formation design: Benders’ decomposition approach. Expert Systems with Applications, 38, 2478–2488.
7
Jolai, F., Tavakkoli-mogaddam, R., Golmohammadi, A. and Javadi, B. (2011). An Electromagnetism-like algorithm for cell formation and layout problem. Expert System with Application, 39, 2172-2182.
8
Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M. and Khorrami, J. (2012). Solving a group layout design model of a dynamic cellula rmanufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Computers & Operations Research, 39, 2642-2658.
9
kioon, S. A., Bulgak, A. A. and Bektas, T. (2009). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European Journal of Operational Research, 192, 414–428.
10
Krishnan, K. k., Mirzaei, S., Venkatasamy, V., and Pillai, V. M. (2012). A comprehensive approach to facility layout design and cell formation. International Journal of Advanced Manufacturing Technology , 59, 737-753.
11
Mahdavi, I., Aalaei, A., Paydar, M. M. and Solimanpur, M. (2010). Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment. Computers and Mathematics with Applications, 60, 1014-1025.
12
Onwubolu, G.C., and Mutingi, M. (2001). A genetic algorithm approach to cellular manufacturing Systems. Computers & industrial engineering, 39, 125-144.
13
Purcheck, G.F.K. (1974). A mathematical classification as a basis for the design of group technology production cells. Production Engineer, 54, 35–48.
14
Saidi-Mehrabad, M., and Mirnezami-ziabari, S. M. (2011). Developing a Multi-objective Mathematical Model for Dynamic Cellular Manufacturing Systems. Journal of Optimization in Industrial Engineering , 7, 1-9.
15
Satuglu, S. I. and Suresh, N. C. (2009). A goal-programming approach for design of hybrid cellular manufacturing systems in dual resource constrainted environment. Computers & Industrial Engineering, 56, 560-575.
16
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N. and Azaron, A. (2005). Solving a dynamic cell formation problem using meta-heuristics. Applied Mathematics and Computation, 170, 761–780.
17
Tavakkoli-mogaddam, R., Javadian, N., Javadi, B. and Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematical Computions, 184, 721-728.
18
Wu, H., Chung, S-H. Chang, C-C. (2010). A water flow-like algorithm for manufacturing cell formation problems. European Journal of Operations research, 205, 346-360.
19
ORIGINAL_ARTICLE
Design of Hï¿½ Congestion Controller for TCP Networks Based on LMI Formulation
In this paper, a state feedback H¥ controller is proposed in order to design an active queue management (AQM) system based on congestion control algorithm for networks supporting TCP protocols. In this approach, the available link bandwidth is modeled as a time-variant disturbance. The purpose of this paper is to design a controller which is capable of achieving the queue size and can guarantee asymptotic stability in the presence of disturbance. An important feature of the proposed approach is that the performance of system, including the disturbance rejection and stability of closed-loop system, are guaranteed for all round-trip times that are less than a known value. The controller design is formulated in the form of some linear matrix inequalities, which can be efficiently solved numerically. The simulation results demonstrate the effectiveness of the proposed methods in comparison with the conventional methods.
http://www.qjie.ir/article_173_85f6391ddf55dc4b5fa507435a16632d.pdf
2015-03-01
51
56
TCP
AQM
Time delay
H?
LMI
stability
Disturbance rejection
Ahmad
Fakharian
1
Assistant Professor, Faculty of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
Amir
Abbasi
2
MSc, Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Bertsekas, D. and Gallager, R.G. (1992). Data Networks. Upper Saddle River, NJ, Prentice-Hall.
1
Cavendish, D., Gerla, M., and Mascolo, S. (2004). A control theoretical approach to congestion control in packet networks. IEEE/ACM Transactions on Networking, 893-906.
2
Chen, Q. and Yang, O.W. (2005). Design of AQM controller for IP routers based on H1 S/U MSP. Proceeding of IEEE intern. conf. on communications, 340-344.
3
Chen, Q., and Yang, O.W. (2007). Robust controller design for AQM router. IEEE Transactions on Automatic Control, 938-943.
4
Clark, DD. and Fang, W. (1998). Explicit allocation of best effort packet delivery service. IEEE/ACM Transactions on Networking, 362-73.
5
Fan, X., Arcak, M., and Wen, J.T. (2004). Robustness of network flow control against disturbances and time-delay. Systems and Control Letters, 13-29.
6
Floyd, S. and Jacobson, V. (1997). Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking, 1-22.
7
Fridman, E. and Shaked, U. (2001). New bounded real lemma representations for timedelay systems and their applications. IEEE Transactions on Automatic Control, 1973-1979.
8
Hollot, C.V., Misra, V., Towsley, D. and Gong, W.B. (2001). On designing improved controllers for AQM routers supporting TCP lows. Proceedings of the IEEE INFOCOM, Alaska, USA, 1726-1734.
9
Hollot, C.V., Misra, V., Towsley, D. and Gong, W.B. (2002). Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Transactions on Automatic Control, 945-959.
10
Kim, K.B. (2006). Design of feedback controls supporting TCP based on the state-space approach. IEEE Transactions on Automatic Control, 1086-99.
11
Lee, Y.S., Kwon, W.H. and Park, P.G. (2007). Authors reply: Comments on delay-dependent robust H1 control for uncertain systems with a state-delay. Automatica, 572-573.
12
Lin, D. and Morris, R. (1997). Dynamics of random early detection. Proceedings of the ACM SIGCOM’97, Cannes, 127-137.
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Misra, V., Gong, W.B. and Towsley, D. (2000). Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED. Proceedings of the ACM/SIGCOM, Stockholm, 151-160.
15
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Ren, F.Y., Lin, C. and Yin, X.H. (2005). Design a congestion controller based on sliding mode variable structure control. Computer Communications, 1050-1061.
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19
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20
ORIGINAL_ARTICLE
A Grey-Based Fuzzy ELECTRE Model for Project Selection
Project selection is considered as an important problem in project management. It is multi-criteria in nature and is based on various quantitative and qualitative factors. The main purpose of this paper is to present a new rank-based method for project selection in outranking relation. According to this approach, decision alternatives were clustered in the concordance matrix and the discordance matrix through the ELECTRE model based on intuitionistic trapezoidal fuzzy numbers. Then, the two matrices were integrated and ranked using grey relational coefficients and the Minkowski space distance. The results of the model were compared with grey relational projection method with intuitionistic trapezoidal fuzzy number. To illustrate the proposed methodology, a case study was conducted to select National Iranian Oil Company projects.
http://www.qjie.ir/article_174_b6c14b687e4d7ebefa7fe522cafd90db.pdf
2015-12-14
57
66
Fuzzy GRA
Fuzzy ELECTRE
GRA based FELECTRE
Project selection
Farshad
Faezy Razi
f.faezi@semnaniau.ac.ir
1
Assistant Professor, Department of Industrial Management, Semnan Branch, Islamic Azad University, Semnan, Iran
LEAD_AUTHOR
Ballestero, Enrique, & Romero, Carlos. (1998). Multiple criteria decision making and its applications to economic problems: Kluwer Academic Publishers Boston.
1
Bashiri, Mahdi, Badri, Hossein, & Talebi, Jafar. (2011). A new fuzzy approach for project selection with outsourcing viewpoint. International Journal of Innovation and Technology Management, 8(02), 227-251.
2
Belton, Valerie, & Stewart, Theodor. (2002). Multiple criteria decision analysis: an integrated approach: Springer.
3
Brans, Jean-Pierre, & Mareschal, Bertrand. (2005). PROMETHEE methods Multiple criteria decision analysis: state of the art surveys (pp. 163-186): Springer.
4
Brans, Jean-Pierre, Vincke, Ph, & Mareschal, Bertrand. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2), 228-238.
5
CAO, Wei, NIU, Chonghuai, & FAN, Yanping. (2013). A Grey Relational Projection Method for Multi-attribute Decision Making Based on Interval-valued Intuitionistic Trapezoidal Fuzzy Number. Journal of Taiyuan University of Technology, 2, 026.
6
Chan, Joseph WK, & Tong, Thomas KL. (2007). Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Materials & Design, 28(5), 1539-1546.
7
Chen, Chung-Yang, Liu, Heng-An, & Song, Je-Yi. (2013). Integrated projects planning in IS departments: A multi-period multi-project selection and assignment approach with a computerized implementation. European Journal of Operational Research, 229(3), 683-694.
8
Daneshvar Rouyendegh, Babak, & Erol, Serpil. (2012). Selecting the best project using the Fuzzy ELECTRE Method. Mathematical Problems in Engineering, 2012.
9
Das, Suman Kalyan, & Sahoo, Prasanta. (2011). Tribological characteristics of electroless Ni–B coating and optimization of coating parameters using Taguchi based grey relational analysis. Materials & Design, 32(4), 2228-2238.
10
Dutra, Camila Costa, Ribeiro, José Luis Duarte, & de Carvalho, Marly Monteiro. (2014). An economic–probabilistic model for project selection and prioritization. International Journal of Project Management, 32(6), 1042-1055.
11
Faezy Razi, Farshad, Eshlaghy, Abbas Toloie, Nazemi, Jamshid, Alborzi, Mahmood, & Pourebrahimi, Alireza. (2014). A Hybrid Grey Based KOHONEN Model and Biogeography-Based Optimization for Project Portfolio Selection. Journal of Applied Mathematics, 2014.
12
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14
Hassanzadeh, Farhad, Nemati, Hamid, & Sun, Minghe. (2014). Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection. European Journal of Operational Research, 238(1), 41-53.
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Li, Guo-Dong, Yamaguchi, Daisuke, Lin, Hui-Shan, Wen, Kun-Li, & Nagai, Masatake. (2006). A grey-based rough set approach to suppliers selection problem. Paper presented at the Rough Sets and Current Trends in Computing.
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Rogers, M, Bruen, M, & Maystre, L. (2000). ELECTRE and decision support. Methods and applications in engineering and infrastructure investment. Kluwer Academic Publishing. Dordrecht (NL).
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Tonchia, Stefano. (2008). Industrial Project Management: Springer.
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Tzeng, Gwo-Hshiung, & Huang, Jih-Jeng. (2011). Multiple attribute decision making: methods and applications: CRC Press.
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Vahdani, Behnam, Mousavi, S Meysam, Hashemi, H, Mousakhani, M, & Ebrahimnejad, S. (2014). A New Hybrid Model Based on Least Squares Support Vector Machine for Project Selection Problem in Construction Industry. Arabian Journal for Science and Engineering, 39(5), 4301-4314
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33
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34
ORIGINAL_ARTICLE
Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters
Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assumption that the failure rate depends on the number of the active elements. The purpose of this model is to maximize the reliability of the system. Internal connection costs, which are the most common costs in electronic systems, are used in this model in order to reach the real-world conditions. To get the results from this model, we used meta-heuristic algorithms such as genetic algorithm and simulation annealing after optimizing their operators’ rates by using response surface methodology.
http://www.qjie.ir/article_175_2964b80c3864a149e2498c30dc26ebc1.pdf
2015-12-14
67
77
reliability
Redundancy allocation problem
Genetic Algorithm
simulated annealing
Response surface methodology
Mani
Sharifi
qjmd@qiau.ac.ir
1
Assistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
Mohsen
Yaghoubizadeh
2
MSc, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
Arulmozhi G, (2002), “Exact equation and an algorithm for reliability for evaluation of K-out-of-N system,” Reliability Engineering and System Safety, vol. 78, pp. 87-91.
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Coit, D.W. and Konak, A., (2006), “Multiple Weighted Objectives Heuristic for the Redundancy Allocation Problem”, IEEE Transactions on Reliability, Vol. 55, No. 3, pp. 551-558.
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Coit, D.W. and Smith, A., (1996), “Penalty Guided Genetic Search for Reliability Design Optimization”, Computers & Industrial Engineering, Vol. 30, pp. 895-904.15
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35