2015
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A genetic algorithm approach for a dynamic cell formation problem considering machine breakdown and buffer storage
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2
Cell formation problem mainly address how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand change in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customer’s requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage is considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NPhard class of problem. A numerical example is used both, for smallsized and largesized instances to show the validity and efficiency of the method in finding near optimal solution.
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18


Masoud
Rabbani
School of Industrial Engineering, College of Engineering, University of Tehran
School of Industrial Engineering, College
Iran
mrabani@ut.ac.ir


S
Elahi
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering,
Iran
s_elahi@ut.ac.ir


Hamed
Rafiei
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering,
Iran
hrafiei@ut.ac.ir


Amir
FarshbafGeranmayeh
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering,
Iran
afarshbaf@ut.ac.ir
Buffer storage
Dynamic cell formation problem
genetic algorithm
Machine breakdown and Mathematical programming
[1. Ahkioon, S., Bulgak, A.A. & Bektas, T. (2009a). Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration. International Journal of Production Research, 47, 1573–1600. ##2. Ahkioon, S., Bulgak, A.A. & Bektas, T. (2009b). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European Journal of Operational Research, 192, 414 – 428. ##3. Aramoon Bajestani, M. Rabbani, M. RahimiVahed, A.R. & Baharian, G.K. (2009). A multiobjective scatter search for a dynamic cell formation problem. Computers & Operations Research, 36, 777 – 794. ##4. Arkat, J., Naseri, F. & Ahmadizar, F. (2011). A stochastic model for the generalized cell formation problem considering machine reliability. International Journal of Computer Integrated Manufacturing, 24, 1095 – 1102. ##5. Balakrishnan, J. & Cheng, C. H. (2007). Multiperiod planning and uncertainty issues in cellular manufacturing: a review and future directions. European Journal of Operational Research, 177, 281309. ##6. Chang, C. C., Wu, T. H. & Wu, C. W. (2013). An efficient approach to determine cell formation, cell Layout and intracellular machine sequence in cellular manufacturing systems. Computers & Industrial Engineering, 66, 438 – 450. ##7. Carlos, A. & Sebastia´n, L. (2006). A particle swarm optimization algorithm for part–machine grouping. ##Robotics and ComputerIntegrated Manufacturing, 22, 468 – 474. ##8. Chung, S.H. & Chang, C.C. (2010). An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Computers & Industrial Engineering, 60, 715. ##9. Das, K., Lashkari, R. S. & Sengupta, S. (2007a). Reliability consideration in the design and analysis of cellular manufacturing systems. International Journal of Production Economics, 105, 243262 ##10. Das, K., Lashkari, R. S. & Sengupta, S. (2007b). Machine reliability and preventive maintenance planning for cellular manufacturing systems. European Journal of Operational Research, 183, 162180. ##11. Defersha, F. H. & Chen, M. (2008). A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. European Journal of Operational Research, 187, 4669. ##12. Diallo, M., Pierreval, H. & Quilliot, A. (2001). Manufacturing cells design with flexible routing capability in presence of unreliable machines. International Journal of production economics, 74, 175 – 182. ##13. Ghezavati, V. R., & SaidiMehrabad, M. (2011). An efficient hybrid selflearning method for stochastic cellular manufacturing problem: A queuingbased analysis. Expert Systems with Applications, 38, 1326 – 1335. ##14. Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Metaheuristics in cellular manufacturing: A stateoftheart review. International Journal of Industrial Engineering Computations, 2, 87122. ##15. JabalAmeli, M.S., Arkat, J. & Barzinpour, F. (2008). Modelling the effects of machine breakdowns in the generalized cell formation problem. International Journal of Advanced Manufacturing Technology, 39, 838 – 850. ##16. JabalAmeli, M.S. & Arkat, J. (2008). Cell formation with alternative process routings and machine reliability consideration. International Journal of Advanced Manufacturing Technology, 35, 761 – 768. ##17. Kusiak, A. & Chow, W.S. (1988). Decomposition of manufacturing systems. IEEE Journal of Robotics and Automation, 4, 457 – 471. ##18. Lee, S.D. (2000). Buffer sizing in complex cellular manufacturing systems. International Journal of Systems Science, 31, 937 – 948. ##19. Mahdavi, I., Paydar, M. M., Solimanpur, M., & Heidarzade, A. (2009). Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Systems with Applications, 36, 6598 – 6604. ##20. Mansouri, S.A., Husseini, S.M.M. & Newman, S.T. (2000). A review of modern approaches to multicriteria cell design. ##International Journal of Production Research, 38, 1201 – 1218. ##21. Nsakanda, A. L., Diaby, M. & Price, W. L. (2006). Hybrid genetic approach for solving largescale capacitated cell formation problems with multiple routings. European Journal of Operational Research, 171, 1051 – 1070. ##22. Nouri, H., & Hong, T. S. (2013). Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations. Journal of Manufacturing Systems, 32, 20 – 31. ##23. Papaioannou, G. & Wilson, J. M. (2010). The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research. European Journal of Operational Research, 206, 509 – 521. ##24. Rafiei, H., & Ghodsi, R. (2013). A biobjective mathematical model toward dynamic cell formation considering labor utilization. Applied Mathematical Modelling, 37, 2308 – 2316. ##25. Renna, P., & Ambrico, M. (2015). Design and reconfiguration models for dynamic cellular manufacturing to handle market changes. International Journal of Computer Integrated Manufacturing, 28, 170 – 186. ##26. Safaei, N., SaidiMehrabad, M., TavakkoliMoghaddam, R. & Sassani, F. (2008a). A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets and Systems, 159, 215 – 236. ##27. Safaei, N., SaidiMehrabad, M. & JabalAmeli, M. S. (2008b). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research, 185, 563 – 592. ##28. SaidiMehrabad, M. & Safaei, N. (2007). A new model of dynamic cell formation by a neural approach. International Journal of Advanced Manufacturing Technology, 33, 1001 – 1009. ##29. Sakhaii, M., TavakkoliMoghaddam, R., Bagheri, M., & Vatani, B. (2015). A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, doi:10.1016/j.apm.2015.05.005. ##30. Saxena, L.K. & Jain, P.K. (2011). Dynamic cellular manufacturing systems design—a comprehensive model. International Journal of Advanced Manufacturing Technology, 53, 11 – 34. ##31. Uddin, M. K., & Shanker, K. (2002). Grouping of parts and machines in presence of alternative process routes by genetic algorithm. International Journal of Production Economics, 76, 219 – 228. ##32. Venugopal, V., & Narendran, T. T. (1992). A genetic algorithm approach to the machinecomponent grouping problem with multiple objectives. Computers & Industrial Engineering, 22, 469 – 480. ##33. Wemmerlov, U. & Hyer, N.L. (1986). Procedures for the part family machine group identification problem in cellular manufacturing. Journal of Operations Management, 6, 125 – 147. ##34. Won, Y. & Currie, K.R. (2006). An Effective Pmedian Model Considering Production Factors in Machine Cell/Part Family Formation. Journal of Manufacturing Systems, 25, 58 – 64. ##]
Coordinating a decentralized supply chain with a stochastic demand using quantity flexibility contract: a gametheoretic approach
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Supply chain includes two or more parties linked by flow of goods, information, and funds. In a decentralized system, supply chain members make decision regardless of their decision's effects on the performance of the other members and the entire supply chain. This is the key issue in supply chain management, that the mechanism should be developed in which different objectives should be aligned, and integrate their activities to optimize the entire system. Therefore, a coordination mechanism could be necessary to motivate members to achieve coordination. The contracts help the supply chain members to achieve coordination that will lead to improved supply chain performance. This paper analyzes a quantityflexibility (QF) contract. The objective of this paper is to explore the applicability and benefits of the contracts, so to realize the importance of coordination by contracts, two cases have been studied. The first case is "no coordination" and the other case is "coordination with QF contract". Utilizing differential game theory, this paper formulates the optimal decisions of the supplier and the retailer in two different game scenarios: Nash equilibrium and cooperative game.It is expected that by designing the contracts as per the requirements of the supply chain members as well as the whole supply chain, supply chain performance can be improved.
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32


mona
taheri
Kurdestan university
Kurdestan university
Iran
t_mona68@yahoo.com


sina
sedghi
Isfahan university
Isfahan university
Iran
sina_sedghi_ie@yahoo.com


farid
khoshalhan
KNT university
KNT university
Iran
khoshalhan@knt.ac.ir
Decentralized supply chain
Supply chain coordination
quantity flexibility contract
game theory
[ 1. Arshinder, A. K. (2009). A framework for evaluation of coordination by contracts: A case of twolevel supply chains . ##Computers & Industrial Engineering, 56, 11771191. ##2. Chen, J, Bell, P. (2011). Coordinating a decentralized supply chain with customer returns and pricedependent stochastic demand using a buyback policy, European Journal of Operational Research, 212, 293–300. ##3. Connors, A. F., et al. (1995). A controlled trial to improve care for seriously iII hospitalized patients: The study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). Jama, 274(20), 15911598. ##4. Emmons, H., (1998), The role of returns policies in pricing and inventory decisions for catalogue goods ##Management Science, 44 , 276283. ##5. Gan, X., Sethi, S. P., Yan, H. (2005). Channel coordination with a risk‐neutral supplier and a downside‐risk‐averse retailer. Production and Operations Management, 14(1), 8089. ##6. Kim, W. (2011). Order quantity flexibility as a form of customer service in a supply chain contract model. Flexible Service and Manufacturing Journal, 23, 290–315. ##7. Knoblich K. , Heavey, C., Williams, P., . (2015). Quantitative analysis of semiconductor supply chain contracts with order flexibility under demand uncertainty: A case study, Computers & Industrial Engineering, 87, 394–406. ##8. Lovejoy, W.S., Tsay, A. (1999). Quantity flexibility contracts and supply chain performance. Manufacturing & Service Operations Management, 1(2), 89111. ##9. Mahajan, S, (2010). A quantity flexibility contract in a supply chain with price dependent demand. IIMB working paper, 304323. ##10. Padmanabhan, V., Png, I.P.L.(1995). Returns policies: Make money by making good. Sloan Management Review Fall, 65–72. ##11. Pasternack, B., (1985). Optimal pricing and returns policies for perishable commodities. Marketing Science, 4, 166–176. ##12. SeyedEsfahani, M. M., Biazaran, M., Gharakhani, M. (2011). A game theoretic approach to coordinate pricing and vertical coop advertising in manufacturer–retailer supply chains. European Journal of Operational Research, 211(2), 263273. ##13. TibbenLembke, R.S. (2004). < i> N</i>period contracts with ordering constraints and total minimum commitments: Optimal and heuristic solutions. European, Journal of Operational Research, 156(2), 353374. ##14. Tsay, A. (1999). Quantity–flexibility contract and supplier–customer incentives. Management Science, 45, 1339–1358. ##15. Wang, Tie. (2007). Coordination mechanisms of supply chain systems. European journal of operational research, 179(1), 116. ##16. Wang, Tie, Hu, Qiying. (2010), Coordination of supply chain with advertisesetting newsvendor, Management Science, 51, 3044 ##]
Robust Optimal Desirability Approach for Multiple Responses Optimization with Multiple Productions Scenarios
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An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted response models are obtained for each response according to their related conditions. In order to consider uncertainty in these models, confidence interval of fitted responses has been defined in the proposed method. This method uses all values in the confidence region of model outputs to define the robustness measure. This method has been applied on the traditional desirability function of each scenario in order to get the best setting of controllable variables for all scenarios simultaneously. To achieve this, the Imperialist Competitive Algorithm has been used to find the robust optimal controllable factors setting. The reported results and analysis of the proposed method confirm efficiency of the proposed approach in a dynamic environment.
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44


yalda
esmizadeh
shahed university
shahed university
Iran
y.esmizade@gmail.com


mehdi
bashiri
shahed university
shahed university
Iran
bashiri.m@gmail.com


amirhossein
parsamanesh
shahed university
shahed university
Iran
parsamanesha@gmail.com
Multiple production scenarios
Robustness
Desirability function
Uncertainty
Imperialist Competitive Algorithm
[1. AtashpazGargari, E. & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Evolutionary Computation. CEC. IEEE Congress. ##2. Bashiri, M. & Bagheri, M. (2013). "Using Imperialist competitive algorithm optimization in multiresponse nonlinear programming."International Journal of Industiral Engineering & Producion Research, 24(3) 229235. ##3. Ch'ng, C., S. Quah & H. Low. (2005). "A new approach for multipleresponse optimization."Quality Engineering, 17(4): 621626. ##4. Costa, N. R., J. Lourenço & Z. L. Pereira (2011). "Desirability function approach: A review and performance evaluation in adverse conditions."Chemometrics and Intelligent Laboratory Systems, 107(2) 234244. ##5. Das, P. & S. Sengupta. (2010). "Composite desirability index in cases of negative and zero desirability."Journal of Management Research, 10(1): 2538. ##6. Derringer, G. (1980). "Simultaneous optimization of several response variables."Journal of Quality Technology, 12(4): 214219. ##7. Derringer, G.C. (1994). "A balancing actoptimizing a products properties."Quality Progress, 27(6) 5158. ##8. Ghasemi, M., S. Ghavidel, M. M. Ghanbarian, H. R. Massrur & M. Gharibzadeh. (2014). "Application of imperialist competitive algorithm with its modified techniques for multiobjective optimal power flow problem: A comparative study." ##Information Sciences, 281: 225247. ##9. He, Z., J. Wang, J. Oh & S. H. Park (2010). "Robust optimization for multiple responses using response surface methodology."Applied Stochastic Models in Business and Industry, 26(2) 157171. ##10. He, Z., P.F. Zhu & S.H. Park. (2012). "A robust desirability function method for multiresponse surface optimization considering model uncertainty."European Journal of Operational Research, 221(1) 241247. ##11. Jeong, I.J. & K.J. Kim. (2003). "Interactive desirability function approach to multiresponse surface optimization." ##International Journal of Reliability, Quality and Safety Engineering, 10(02) 205217. ##12. Jeong, I.J. & K.J. Kim. (2009). "An interactive desirability function method to multiresponse optimization."European Journal of Operational Research, 195(2) 412426. ##13. Kim, K.J. & D. K. Lin. (2000). "Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions."Journal of the Royal Statistical Society.Series C(Applied Statistics), 49(3) 311325. ##14. Lee, M.S. & K.J. Kim. (2007). "Expected desirability function: consideration of both location and dispersion effects in desirability function approach."Quality Technology & Quantitative Management, 4(3) 365377. ##15. Montgomery, D. C. (2005). Design and Analysis of Experiments, sixth ed. Wiley, New and York. ##16. Ortiz, F., J. R. Simpson, J. J. Pignatiello & A. HerediaLangner (2004). "A genetic algorithm approach to multipleresponse optimization."Journal of Quality Technology, 36(4) 432450. ##17. Ribardo, C. & T. T. Allen (2003). "An alternative desirability function for achieving ‘six sigma’quality."Quality and Reliability Engineering International 19(3): 227240. ##18. Wu, F.C. (2004). "Optimization of correlated multiple quality characteristics using desirability function."Quality Engineering, 17(1) 119126. ##]
Design of Economic Optimal Double Sampling Design with Zero Acceptance Numbers
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2
In zero acceptance number sampling plans, the sample items of an incoming lot are inspected one by one. The proposed method in this research follows these rules: if the number of nonconforming items in the first sample is equal to zero, the lot is accepted but if the number of nonconforming items is equal to one, then second sample is taken and the policy of zero acceptance number would be applied for the second sample. In this paper, a mathematical model is developed to design single stage and double stage sampling plans. Proposed model can be used to determine the optimal tolerance limits and sample size. In addition, a sensitivity analysis is done to illustrate the effect of some important parameters on the objective function. The results show that the proposed two stage sampling plan has better performance than single stage sampling plan in terms of total loss function, sample size and robustness.
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45
56


Mohammad Saber
Fallahnezhad
university of yazd
university of yazd
Iran
fallahnezhad@yazd.ac.ir


ahmad
ahmadi yazdi
Yazd University
Yazd University
Iran
ahmad_ahmadi_yazdi@yahoo.com


parvin
abdollahi
Yazd university
Yazd university
Iran
abdollahi4@gmail.com


Muhammad
Aslam
Department of Statistics, Forman Christian College University Lahore 54000, Pakistan
Department of Statistics, Forman Christian
Iran
aslam_ravian@hotmail.com
Quality control
Acceptance sampling
Optimal design
Loss Function
[1. Arizono, I., Kanagawa, A., Ohta H., Watakabe K., & Tateishi K. (1997). "Variable sampling plans for normal distribution indexed by Taguchi's loss function",Naval Research Logistics, 44(6) pp. 591603. ##2. Aslam, M., Jun, C.H,& Ahmad, M. (2009). "Double acceptance sampling plans based on truncated life tests in the weibull model"Journal of Statistical Theory and Applications, 8(2) pp. 191206. ##3. Aslam, M. & Jun, C.H. (2010)."A double acceptance sampling plan for generalized loglogistic distributions with known shape parameters",Journal of Applied Statistics, 37(3) pp. 405414. ##4. Aslam, M., Yasir, M., Lio, Y.L., Tsai, T.R.,& Khan, M.A. (2011). "Double acceptance sampling plans for burr type XII distribution percentiles under the truncated life test",Journal of the Operational Research Society, 63(7) pp.10101017. ##5. Aslam, M., Niaki, S.T.A.., Rasool, M.,& Fallahnezhad, M.S. (2012). "Decision rule of repetitive acceptance sampling plans assuring percentile life",Scientia Iranica, 19(3) pp.879884. ##6. Elsayed, E. A. & Chen, A. (1994). "An economic design of control chart using quadratic loss function",International Journal of Production Research, 32(4) pp. 873887. ##7. Fallahnezhad, M.S., Niaki, S.T.A.,& VahdatZad, M.A. (2012). "A new acceptance sampling design using bayesian modeling and backwards induction",International Journal of Engineering, Transactions C: Aspects, 25(1) pp. 4554. ##8. Fallahnezhad, M.S.,& Aslam, M. (2013). "A new economical design of acceptance sampling models using bayesian inference",Accreditation and Quality Assurance, 18(3) pp.187195. ##9. Fallahnezhad, M.S.,& HosseiniNasab, H. (2011). "Designing a single stage acceptance sampling plan based on the control threshold policy",International Journal of Industrial Engineering & Production Research, 22(3) pp. 143150. ##10. Fallahnezhad, M.S.,& Ahmadi Yazdi, A. (2015). "Economic design of acceptance sampling plans based on conforming run lengths using loss functions",Journal of Testing and Evaluation, 44(1) pp. 18. ##11. Ferrell, W. G.,& Chhoker, Jr. A. (2002). "Design of economically optimal acceptance sampling plans with inspection error",Computers & Operations Research, 29(1) pp. 12831300. ##12. Govindaraju, K. (2005). "Design of minimum average total inspection sampling plans",Communications in Statistics  Simulation and Computation, 34(2) pp. 485493 ##13. Guenther, W. C. (1969). "Use of the binomial, hyper geometric and Poisson tables to obtain Sampling plans",Journal of Quality Technology, 1(2) pp. 105109. ##14. Hailey W.A. (1980). "Minimum sample size single sampling plans: a computerized approach",Journal of Quality Technology, 12(4) pp. 230–5. ##15. Kobayashia, J., Arizonoa, I. & Takemotoa, Y. (2003), "Economical operation of control chart indexed by Taguchi's loss function",International Journal of Production Research, 41(6) pp. 11151132. ##16. Moskowitz, H. & Tang, K. (1992). "Bayesian variables acceptancesampling plans: quadratic loss function and step loss function",Technometrics, 34(3) pp. 340347. ##17. Niaki, S.T.A.,& Fallahnezhad, M.S. (2009). "Designing an optimum acceptance plan using bayesian inference and stochastic dynamic programming",Scientia Iranica, 16(1) pp. 1925. ##18. Pearn, W.L.,& Wu. C.W. (2006). "Critical acceptance values and sample sizes of a variables sampling plan for very low fraction of nonconforming",Omega, 34(1) pp. 90 – 101. ##19. Stephens, K. S. (2001). "The hand book of applied acceptance samplingplans, principles, and procedures",American Society for Quality, Milwaukee, Wisconsin: ASQ Quality Press. ##20. Squeglia, N. L. (1994). "Zero acceptance number sampling plans",American Society for Quality, Milwaukee, Wisconsin: ASQ Quality Press. ##21. Wu, Z., Shamsuzzamana, M. & Panb., E. S. (2004). "Optimization design of control charts based on Taguchi's loss function and random process shifts",International Journal of Production Research, 42(2) pp. 379390. ##]
A New Uncertain Modeling of Production Project Time and Cost Based on Atanassov Fuzzy Sets
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2
Uncertainty plays a major role in any project evaluation and management process. One of the trickiest parts of any production project work is its cost and time forecasting. Since in the initial phases of production projects uncertainty is at its highest level, a reliable method of project scheduling and cash flow generation is vital to help the managers reach successful implementation of the project. In the recent years, some scholars have tried to address uncertainty of projects in time and cost by using basic uncertainty modeling tools such as fuzzy sets theory. In this paper, a new approach is introduced to model project cash flow under uncertain environments using Atanassov fuzzy sets or intuitionistic fuzzy sets (IFSs). The IFSs are presented to calculate project scheduling and cash flow generation. This modern approach enhances the ability of managers to use their intuition and lack of knowledge in their decisionmakings. Moreover, unlike the recent studies in this area, this model uses a more sophisticated tool of uncertain modeling which is highly practical in real production project environments. Furthermore, a new effective IFSranking method is introduced. The methodology is exemplified by estimating the working capital requirements in an activity network. The proposed model could be useful for both project proposal evaluation during feasibility studies and for performing earned value analysis for project monitoring and control.
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57
70


S. Meysam
Mousavi
Shahed University
Shahed University
Iran
smemusavi@yahoo.com


V.
Mohagheghi
Shahed University
Shahed University
Iran
v.mohagheghi@gmail.com


B.
Vahdani
Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University
Faculty of Industrial & Mechanical
Iran
b.vahdani@gmail.com
Production projects
Atanassov fuzzy sets
Intuitionistic fuzzy project scheduling
Intuitionistic fuzzy cost flow
[ 1. Atanassov, K. T. (2008). My personal view on intuitionistic fuzzy sets theory. InFuzzy Sets and Their Extensions: Representation, Aggregation & Models (pp. 2343). Springer Berlin Heidelberg. ##2. Atanassov, K. T. Intuitionistic fuzzy sets. Central Tech Library, Bulgarian Academy Science, Sofia, Bulgaria, 1983. ##3. Atkinson, R., Crawford, L., & Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International journal of project management, 24(8), 687698. ##4. Barbosa, P. S., & Pimentel, P. R. (2001). A linear programming model for cash flow management in the Brazilian construction industry. Construction management and Economics, 19(5), 469479. ##5. Bhattacharyya, R. (2015). A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection. ##Fuzzy Information and Engineering, 7(2), 211225. ##6. Blyth, K. & Kaka, A. (2006). A novel multiple linear regression model for forecasting Scurves, Engineering, Construction and Architectural Management, 13(1): 82–95. ##7. Boran, F. E., Boran, K., & Menlik, T. (2012). The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources, Part B: Economics, Planning, and Policy, 7(1), 8190. ##8. Boussabaine A.H. & Kaka, A. (1998). A neural networks approach for costflow forecasting. Construction Management and Economics Journal, 16, 471479. ##9. Caron, F., & Comandulli, M. (2014). A cash flowbased approach for assessing expansion options stemming from project modularity. International Journal of Project Organization and Management, 6(12), 157178. ##10. Chai, J., Liu, J. N., & Xu, Z. (2012). A new rulebased SIR approach to supplier selection under intuitionistic fuzzy environments. International Journal of Uncertainty, Fuzziness and Knowledgebased Systems, 20(3), 451471. ##11. Chanas, S., & Kamburowski, J. (1981). The use of fuzzy variables in PERT. Fuzzy sets and systems, 5(1), 1119. ##12. Chen, C. C., & Zhang, Q. (2014). Applying quality function deployment techniques in lead production project selection and assignment. In Advanced Materials Research (Vol. 945, pp. 29542959). ##13. Chen, H. L., Chen, C. I., Liu, C. H., & Wei, N. C. (2013). Estimating a project's profitability: A longitudinal approach. ##International Journal of Project Management, 31(3), 400410. ##14. Cheng, M. Y., & Roy, A. F. (2011). Evolutionary fuzzy decision model for cash flow prediction using timedependent support vector machines. International Journal of Project Management, 29(1), 5665. ##15. Cheng, M. Y., Hoang, N. D., and Wu, Y. W. (2015). Cash flow prediction for construction project using a novel adaptive timedependent least squares support vector machine inference model. Journal of Civil Engineering and Management, 21(6), 679688. ##16. Cioffi, D.F., (2005). A tool for managing projects: an analytic parameterization of the Scurve. International Journal of Project Management, 23(3), 215–222. ##17. Cooke, B., & Jepson, W. B. (1979). Cost and financial control for construction firms. Macmillan. ##18. Deng, H. (2014). Comparing and ranking fuzzy numbers using ideal solutions. Applied Mathematical Modelling, 38(5), 16381646. ##19. Duong, A. N. (2011). Ratedecline analysis for fracturedominated shale reservoirs. SPE Reservoir Evaluation and Engineering, 14(3), 377. ##20. Gerogiannis, V. C., Fitsilis, P., & Kameas, A. D. (2011). Using a combined intuitionistic fuzzy setTOPSIS method for evaluating project and portfolio management information systems. In Artificial Intelligence Applications and Innovations (pp. 6781), Springer Berlin Heidelberg. ##21. Gormley, F.M., & Meade, N., 2007. The utility of cash flow forecasts in the management of corporate cash balances. ##European Journal of Operational Research 182(2), 923–935 . ##22. Hsu, K. (2003). Estimation of a double Scurve model, AACE International Transactions IT13.1– IT13.5. ##23. Hwee, N. G. & Tiong, R. L. K., (2002). Model on cash flow forecasting and risk analysis for contracting firms, International Journal of Project Management, 20, 351363. ##24. Jarrah, R., Kulkarni, D., & O’Connor, J.T., (2007). Cash flow projections for selected TxDoT highway projects. Journal of Construction Engineering and Management, 133(3), 235–241. ##25. Jiang, A., Issa, R. R., & Malek, M. (2011). Construction project cash flow planning using the Pareto optimality efficiency network model. Journal of Civil Engineering and Management, 17(4), 510519. ##26. Khosrowshahi, F., & Kaka, A. P. (2007). A decision support model for construction cash flow management. Computer‐Aided Civil and Infrastructure Engineering, 22(7), 527539. ##27. Kumar, V. S., Hanna, A. S., & Adams, T. (2000). Assessment of working capital requirements by fuzzy set theory. ##Engineering, Construction and Architectural Management, 7(1), 93103. ##28. Lam, K. C., et al. (2001). An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decisionmaking. Construction Management and Economics, 19(1), 6376. ##29. Lawson, C. P., Longhurst, P. J., & Ivey, P. C. (2006). The application of a new research and development project selection model in SMEs. Technovation, 26(2), 242250. ##30. Lee F. (1998). Fuzzy information processing system. Peking University Press Inc., 118–132. 31. Li, H., & Yen, V. C. (1995). Fuzzy sets and fuzzy decisionmaking. CRC press. ##32. Liang, C., Zhao, S., & Zhang, J. (2014). Aggregation Operators on Triangular Intuitionistic Fuzzy Numbers and its Application to MultiCriteria Decision Making Problems. Foundations of Computing and Decision Sciences, 39(3), 189208. ##33. Maravas, A., & Pantouvakis, J. P. (2012). 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Improving envelopment in data envelopment analysis by means of unobserved DMUs: an application of banking industry
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In data envelopment analysis, the relative efficiency of a decision making unit (DMU) is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the DMUs to freely allocate weights to their inputs/outputs. However, this measure may not reflect a the true efficiency of a DMU because some of its inputs/outputs may not contribute reasonably in computing the efficiency measure. Traditionally, to overcome this problem weights restrictions have been imposed. But an approach for solving this problem by inclusion of some unobserved DMUs, obtained via a process with four steps, has been proposed in 2004. These unobserved DMUs are created by adjusting the output levels of certain observed relatively efficient DMUs. The method used in this research is for DMUs that are operating under a constant return to scale (CRS) technology with a single input multioutput context. This method is implemented for 47 branches of bank Maskan in northeast of Tehran and the results will be analysed.
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71
80


fatemeh
rakhshan
student
student
Iran
rakhshan@mathdep.iust.ac.ir


Mohammad Reza
Alirezaee
master
master
Iran
mralirez@iust.ac.ir
data envelopment analysis
linear programming applications
value judgments
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