ORIGINAL_ARTICLE
A genetic algorithm approach for a dynamic cell formation problem considering machine breakdown and buffer storage
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 NP-hard class of problem. A numerical example is used both, for small-sized and large-sized instances to show the validity and efficiency of the method in finding near optimal solution.
http://jqepo.shahed.ac.ir/article_347_ccc12450a2ecc4eec145e744a44ba8ff.pdf
2015-07-01T11:23:20
2018-08-20T11:23:20
1
18
10.22070/jqepo.2015.347
Buffer storage
Dynamic cell formation problem
genetic algorithm
Machine breakdown and Mathematical programming
Masoud
Rabbani
mrabani@ut.ac.ir
true
1
School of Industrial Engineering, College of Engineering, University of Tehran
School of Industrial Engineering, College of Engineering, University of Tehran
School of Industrial Engineering, College of Engineering, University of Tehran
LEAD_AUTHOR
S
Elahi
s_elahi@ut.ac.ir
true
2
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
AUTHOR
Hamed
Rafiei
hrafiei@ut.ac.ir
true
3
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
AUTHOR
Amir
Farshbaf-Geranmayeh
afarshbaf@ut.ac.ir
true
4
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
School of Industrial and System Engineering, College of Engineering, University of Tehran
AUTHOR
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.
1
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.
2
3. Aramoon Bajestani, M. Rabbani, M. Rahimi-Vahed, A.R. & Baharian, G.K. (2009). A multi-objective scatter search for a dynamic cell formation problem. Computers & Operations Research, 36, 777 – 794.
3
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.
4
5. Balakrishnan, J. & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: a review and future directions. European Journal of Operational Research, 177, 281-309.
5
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.
6
7. Carlos, A. & Sebastia´n, L. (2006). A particle swarm optimization algorithm for part–machine grouping.
7
Robotics and Computer-Integrated Manufacturing, 22, 468 – 474.
8
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, 7-15.
9
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, 243-262
10
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, 162-180.
11
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, 46-69.
12
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
13. Ghezavati, V. R., & 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.
14
14. Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2, 87-122.
15
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
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
17. Kusiak, A. & Chow, W.S. (1988). Decomposition of manufacturing systems. IEEE Journal of Robotics and Automation, 4, 457 – 471.
18
18. Lee, S.D. (2000). Buffer sizing in complex cellular manufacturing systems. International Journal of Systems Science, 31, 937 – 948.
19
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
20. Mansouri, S.A., Husseini, S.M.M. & Newman, S.T. (2000). A review of modern approaches to multi-criteria cell design.
21
International Journal of Production Research, 38, 1201 – 1218.
22
21. Nsakanda, A. L., Diaby, M. & Price, W. L. (2006). Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings. European Journal of Operational Research, 171, 1051 – 1070.
23
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.
24
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.
25
24. Rafiei, H., & Ghodsi, R. (2013). A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Applied Mathematical Modelling, 37, 2308 – 2316.
26
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.
27
26. Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, 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.
28
27. Safaei, N., Saidi-Mehrabad, 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.
29
28. Saidi-Mehrabad, M. & Safaei, N. (2007). A new model of dynamic cell formation by a neural approach. International Journal of Advanced Manufacturing Technology, 33, 1001 – 1009.
30
29. Sakhaii, M., Tavakkoli-Moghaddam, 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.
31
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.
32
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.
33
32. Venugopal, V., & Narendran, T. T. (1992). A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Computers & Industrial Engineering, 22, 469 – 480.
34
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.
35
34. Won, Y. & Currie, K.R. (2006). An Effective P-median Model Considering Production Factors in Machine Cell/Part Family Formation. Journal of Manufacturing Systems, 25, 58 – 64.
36
ORIGINAL_ARTICLE
Coordinating a decentralized supply chain with a stochastic demand using quantity flexibility contract: a game-theoretic approach
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 quantity-flexibility (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.
http://jqepo.shahed.ac.ir/article_273_41559ac815252bdb00f8d847e83a5dbf.pdf
2015-10-25T11:23:20
2018-08-20T11:23:20
19
32
10.22070/jqepo.2015.273
Decentralized supply chain
supply chain coordination
quantity flexibility contract
game theory
mona
taheri
t_mona68@yahoo.com
true
1
Kurdestan university
Kurdestan university
Kurdestan university
LEAD_AUTHOR
sina
sedghi
sina_sedghi_ie@yahoo.com
true
2
Isfahan university
Isfahan university
Isfahan university
AUTHOR
farid
khoshalhan
khoshalhan@knt.ac.ir
true
3
KNT university
KNT university
KNT university
AUTHOR
1. Arshinder, A. K. (2009). A framework for evaluation of coordination by contracts: A case of two-level supply chains .
1
Computers & Industrial Engineering, 56, 1177-1191.
2
2. Chen, J, Bell, P. (2011). Coordinating a decentralized supply chain with customer returns and price-dependent stochastic demand using a buyback policy, European Journal of Operational Research, 212, 293–300.
3
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), 1591-1598.
4
4. Emmons, H., (1998), The role of returns policies in pricing and inventory decisions for catalogue goods
5
Management Science, 44 , 276-283.
6
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), 80-89.
7
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.
8
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.
9
8. Lovejoy, W.S., Tsay, A. (1999). Quantity flexibility contracts and supply chain performance. Manufacturing & Service Operations Management, 1(2), 89-111.
10
9. Mahajan, S, (2010). A quantity flexibility contract in a supply chain with price dependent demand. IIMB working paper, 304-323.
11
10. Padmanabhan, V., Png, I.P.L.(1995). Returns policies: Make money by making good. Sloan Management Review Fall, 65–72.
12
11. Pasternack, B., (1985). Optimal pricing and returns policies for perishable commodities. Marketing Science, 4, 166–176.
13
12. SeyedEsfahani, M. M., Biazaran, M., Gharakhani, M. (2011). A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer–retailer supply chains. European Journal of Operational Research, 211(2), 263-273.
14
13. Tibben-Lembke, 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), 353-374.
15
14. Tsay, A. (1999). Quantity–flexibility contract and supplier–customer incentives. Management Science, 45, 1339–1358.
16
15. Wang, Tie. (2007). Coordination mechanisms of supply chain systems. European journal of operational research, 179(1), 1-16.
17
16. Wang, Tie, Hu, Qiying. (2010), Coordination of supply chain with advertise-setting newsvendor, Management Science, 51, 30-44
18
ORIGINAL_ARTICLE
Robust Optimal Desirability Approach for Multiple Responses Optimization with Multiple Productions Scenarios
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.
http://jqepo.shahed.ac.ir/article_274_2e0a46dffa6246d156862c9262d66961.pdf
2015-10-25T11:23:20
2018-08-20T11:23:20
33
44
10.22070/jqepo.2015.274
Multiple production scenarios
Robustness
Desirability function
Uncertainty
Imperialist Competitive Algorithm
yalda
esmizadeh
y.esmizade@gmail.com
true
1
shahed university
shahed university
shahed university
AUTHOR
mehdi
bashiri
bashiri.m@gmail.com
true
2
shahed university
shahed university
shahed university
LEAD_AUTHOR
amirhossein
parsamanesh
parsamanesha@gmail.com
true
3
shahed university
shahed university
shahed university
AUTHOR
1. Atashpaz-Gargari, E. & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Evolutionary Computation. CEC. IEEE Congress.
1
2. Bashiri, M. & Bagheri, M. (2013). "Using Imperialist competitive algorithm optimization in multi-response nonlinear programming."International Journal of Industiral Engineering & Producion Research, 24(3) 229-235.
2
3. Ch'ng, C., S. Quah & H. Low. (2005). "A new approach for multiple-response optimization."Quality Engineering, 17(4): 621-626.
3
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) 234-244.
4
5. Das, P. & S. Sengupta. (2010). "Composite desirability index in cases of negative and zero desirability."Journal of Management Research, 10(1): 25-38.
5
6. Derringer, G. (1980). "Simultaneous optimization of several response variables."Journal of Quality Technology, 12(4): 214-219.
6
7. Derringer, G.C. (1994). "A balancing act-optimizing a products properties."Quality Progress, 27(6) 51-58.
7
8. Ghasemi, M., S. Ghavidel, M. M. Ghanbarian, H. R. Massrur & M. Gharibzadeh. (2014). "Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: A comparative study."
8
Information Sciences, 281: 225-247.
9
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) 157-171.
10
10. He, Z., P.F. Zhu & S.H. Park. (2012). "A robust desirability function method for multi-response surface optimization considering model uncertainty."European Journal of Operational Research, 221(1) 241-247.
11
11. Jeong, I.J. & K.J. Kim. (2003). "Interactive desirability function approach to multi-response surface optimization."
12
International Journal of Reliability, Quality and Safety Engineering, 10(02) 205-217.
13
12. Jeong, I.J. & K.J. Kim. (2009). "An interactive desirability function method to multiresponse optimization."European Journal of Operational Research, 195(2) 412-426.
14
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) 311-325.
15
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) 365-377.
16
15. Montgomery, D. C. (2005). Design and Analysis of Experiments, sixth ed. Wiley, New and York.
17
16. Ortiz, F., J. R. Simpson, J. J. Pignatiello & A. Heredia-Langner (2004). "A genetic algorithm approach to multiple-response optimization."Journal of Quality Technology, 36(4) 432-450.
18
17. Ribardo, C. & T. T. Allen (2003). "An alternative desirability function for achieving ‘six sigma’quality."Quality and Reliability Engineering International 19(3): 227-240.
19
18. Wu, F.C. (2004). "Optimization of correlated multiple quality characteristics using desirability function."Quality Engineering, 17(1) 119-126.
20
ORIGINAL_ARTICLE
Design of Economic Optimal Double Sampling Design with Zero Acceptance Numbers
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.
http://jqepo.shahed.ac.ir/article_275_424bfa9ec01e8524a3179f47b9364056.pdf
2015-10-25T11:23:20
2018-08-20T11:23:20
45
56
10.22070/jqepo.2015.275
Quality control
Acceptance sampling
Optimal design
Loss Function
Mohammad Saber
Fallahnezhad
fallahnezhad@yazd.ac.ir
true
1
university of yazd
university of yazd
university of yazd
LEAD_AUTHOR
ahmad
ahmadi yazdi
ahmad_ahmadi_yazdi@yahoo.com
true
2
Yazd University
Yazd University
Yazd University
AUTHOR
parvin
abdollahi
abdollahi4@gmail.com
true
3
Yazd university
Yazd university
Yazd university
AUTHOR
Muhammad
Aslam
aslam_ravian@hotmail.com
true
4
Department of Statistics, Forman Christian College University Lahore 54000, Pakistan
Department of Statistics, Forman Christian College University Lahore 54000, Pakistan
Department of Statistics, Forman Christian College University Lahore 54000, Pakistan
AUTHOR
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. 591-603.
1
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. 191-206.
2
3. Aslam, M. & Jun, C.H. (2010)."A double acceptance sampling plan for generalized log-logistic distributions with known shape parameters",Journal of Applied Statistics, 37(3) pp. 405-414.
3
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.1010-1017.
4
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.879-884.
5
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. 873-887.
6
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. 45-54.
7
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.187-195.
8
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. 143-150.
9
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. 1-8.
10
11. Ferrell, W. G.,& Chhoker, Jr. A. (2002). "Design of economically optimal acceptance sampling plans with inspection error",Computers & Operations Research, 29(1) pp. 1283-1300.
11
12. Govindaraju, K. (2005). "Design of minimum average total inspection sampling plans",Communications in Statistics - Simulation and Computation, 34(2) pp. 485-493
12
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. 105-109.
13
14. Hailey W.A. (1980). "Minimum sample size single sampling plans: a computerized approach",Journal of Quality Technology, 12(4) pp. 230–5.
14
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. 1115-1132.
15
16. Moskowitz, H. & Tang, K. (1992). "Bayesian variables acceptance-sampling plans: quadratic loss function and step loss function",Technometrics, 34(3) pp. 340-347.
16
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. 19-25.
17
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.
18
19. Stephens, K. S. (2001). "The hand book of applied acceptance sampling-plans, principles, and procedures",American Society for Quality, Milwaukee, Wisconsin: ASQ Quality Press.
19
20. Squeglia, N. L. (1994). "Zero acceptance number sampling plans",American Society for Quality, Milwaukee, Wisconsin: ASQ Quality Press.
20
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. 379-390.
21
ORIGINAL_ARTICLE
A New Uncertain Modeling of Production Project Time and Cost Based on Atanassov Fuzzy Sets
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 decision-makings. 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 IFS-ranking 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.
http://jqepo.shahed.ac.ir/article_335_15d6ce2de5cfd24ac9211c59f76a79c4.pdf
2015-10-25T11:23:20
2018-08-20T11:23:20
57
70
10.22070/jqepo.2015.335
Production projects
Atanassov fuzzy sets
Intuitionistic fuzzy project scheduling
Intuitionistic fuzzy cost flow
S. Meysam
Mousavi
smemusavi@yahoo.com
true
1
Shahed University
Shahed University
Shahed University
LEAD_AUTHOR
V.
Mohagheghi
v.mohagheghi@gmail.com
true
2
Shahed University
Shahed University
Shahed University
AUTHOR
B.
Vahdani
b.vahdani@gmail.com
true
3
Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University
Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University
Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University
AUTHOR
1. Atanassov, K. T. (2008). My personal view on intuitionistic fuzzy sets theory. InFuzzy Sets and Their Extensions: Representation, Aggregation & Models (pp. 23-43). Springer Berlin Heidelberg.
1
2. Atanassov, K. T. Intuitionistic fuzzy sets. Central Tech Library, Bulgarian Academy Science, Sofia, Bulgaria, 1983.
2
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), 687-698.
3
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), 469-479.
4
5. Bhattacharyya, R. (2015). A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection.
5
Fuzzy Information and Engineering, 7(2), 211-225.
6
6. Blyth, K. & Kaka, A. (2006). A novel multiple linear regression model for forecasting S-curves, Engineering, Construction and Architectural Management, 13(1): 82–95.
7
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), 81-90.
8
8. Boussabaine A.H. & Kaka, A. (1998). A neural networks approach for cost-flow forecasting. Construction Management and Economics Journal, 16, 471-479.
9
9. Caron, F., & Comandulli, M. (2014). A cash flow-based approach for assessing expansion options stemming from project modularity. International Journal of Project Organization and Management, 6(1-2), 157-178.
10
10. Chai, J., Liu, J. N., & Xu, Z. (2012). A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 20(3), 451-471.
11
11. Chanas, S., & Kamburowski, J. (1981). The use of fuzzy variables in PERT. Fuzzy sets and systems, 5(1), 11-19.
12
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. 2954-2959).
13
13. Chen, H. L., Chen, C. I., Liu, C. H., & Wei, N. C. (2013). Estimating a project's profitability: A longitudinal approach.
14
International Journal of Project Management, 31(3), 400-410.
15
14. Cheng, M. Y., & Roy, A. F. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56-65.
16
15. Cheng, M. Y., Hoang, N. D., and Wu, Y. W. (2015). Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model. Journal of Civil Engineering and Management, 21(6), 679-688.
17
16. Cioffi, D.F., (2005). A tool for managing projects: an analytic parameterization of the S-curve. International Journal of Project Management, 23(3), 215–222.
18
17. Cooke, B., & Jepson, W. B. (1979). Cost and financial control for construction firms. Macmillan.
19
18. Deng, H. (2014). Comparing and ranking fuzzy numbers using ideal solutions. Applied Mathematical Modelling, 38(5), 1638-1646.
20
19. Duong, A. N. (2011). Rate-decline analysis for fracture-dominated shale reservoirs. SPE Reservoir Evaluation and Engineering, 14(3), 377.
21
20. Gerogiannis, V. C., Fitsilis, P., & Kameas, A. D. (2011). Using a combined intuitionistic fuzzy set-TOPSIS method for evaluating project and portfolio management information systems. In Artificial Intelligence Applications and Innovations (pp. 67-81), Springer Berlin Heidelberg.
22
21. Gormley, F.M., & Meade, N., 2007. The utility of cash flow forecasts in the management of corporate cash balances.
23
European Journal of Operational Research 182(2), 923–935 .
24
22. Hsu, K. (2003). Estimation of a double S-curve model, AACE International Transactions IT13.1– IT13.5.
25
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, 351-363.
26
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.
27
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), 510-519.
28
26. Khosrowshahi, F., & Kaka, A. P. (2007). A decision support model for construction cash flow management. Computer‐Aided Civil and Infrastructure Engineering, 22(7), 527-539.
29
27. Kumar, V. S., Hanna, A. S., & Adams, T. (2000). Assessment of working capital requirements by fuzzy set theory.
30
Engineering, Construction and Architectural Management, 7(1), 93-103.
31
28. Lam, K. C., et al. (2001). An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making. Construction Management and Economics, 19(1), 63-76.
32
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), 242-250.
33
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 decision-making. CRC press.
34
32. Liang, C., Zhao, S., & Zhang, J. (2014). Aggregation Operators on Triangular Intuitionistic Fuzzy Numbers and its Application to Multi-Criteria Decision Making Problems. Foundations of Computing and Decision Sciences, 39(3), 189-208.
35
33. Maravas, A., & Pantouvakis, J. P. (2012). Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management, 30(3), 374-384.
36
34. McCahon, C. S., & Lee, E. S. (1988). Project network analysis with fuzzy activity times. Computers & Mathematics with applications, 15(10), 829-838.
37
35. Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2015). A new optimization model for project portfolio selection under interval-valued fuzzy environment. Arabian Journal for Science and Engineering, 40, 3351–3361.
38
36. Mousavi, S. M., Jolai, F., & Tavakkoli-Moghaddam, R. (2013). A fuzzy stochastic multi-attribute group decision-making approach for selection problems. Group Decision and Negotiation, 22(2), 207-233.
39
37. Neog, T. J., & Sut, D. K. (2011). An application of fuzzy soft sets in medical diagnosis using fuzzy soft complement.
40
International Journal of Computer Applications, 33(9).
41
38. Ning, X., Lam, K. C., & Lam, M. C. K. (2011). A decision-making system for construction site layout planning. Automation in Construction, 20(4), 459-473.
42
39. Prade, H. (1979). Using fuzzy set theory in a scheduling problem: a case study. Fuzzy sets and systems, 2(2), 153-165.
43
40. Rostamy, A. A., Takanlou, F., & AnvaryRostamy, A. (2013). A fuzzy statistical expert system for cash flow analysis and management under uncertainty. Advances in Economics and Business, 1(2), 89-102.
44
41. Santamaría, L., Barge-Gil, A., & Modrego, A. (2010). Public selection and financing of R&D cooperative projects: Credit versus subsidy funding. Research Policy, 39(4), 549-563.
45
42. Shu, M. H., Cheng, C. H., & Chang, J. R. (2006). Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly. Microelectronics Reliability, 46(12), 2139-2148.
46
43. Szmidt, E., & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. In Computational Intelligence. Theory and Applications (pp. 148-151). Springer Berlin Heidelberg.
47
44. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2014). How to measure the amount of knowledge conveyed by Atanassov’s intuitionistic fuzzy sets. Information Sciences, 257, 276-285.
48
45. Touran, A., Atgun, M., & Bhurisith, I., (2004). Analysis of the United States department of transportation prompt pay provisions. Journal of Construction Engineering and Management, 130(5), 719–725.
49
46. Ungureanu, D., & Vernic, R. (2014). On a fuzzy cash flow model with insurance applications. Decisions in Economics and Finance, 1-16.
50
47. Wang, Y. (2012). An Approach to Software Selection with Triangular Intuitionistic Fuzzy Information. International Journal of Advancements in Computing Technology, 4(2).
51
48. Xu, Z., & Liao, H. (2014). Intuitionistic Fuzzy Analytic Hierarchy Process, IEEE Transactions on Fuzzy Systems, 22(4),749-761.
52
49. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
53
50. Zimmermann, H. J. (2001).Fuzzy set theory—and its applications. Springer Science & Business Media.
54
ORIGINAL_ARTICLE
Improving envelopment in data envelopment analysis by means of unobserved DMUs: an application of banking industry
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 multi-output context. This method is implemented for 47 branches of bank Maskan in northeast of Tehran and the results will be analysed.
http://jqepo.shahed.ac.ir/article_276_7f2b16eff6fae909d55890a260adf738.pdf
2015-10-25T11:23:20
2018-08-20T11:23:20
71
80
10.22070/jqepo.2015.276
data envelopment analysis
linear programming applications
value judgments
fatemeh
rakhshan
rakhshan@mathdep.iust.ac.ir
true
1
student
student
student
LEAD_AUTHOR
Mohammad Reza
Alirezaee
mralirez@iust.ac.ir
true
2
master
master
master
AUTHOR
1. Allen, R., Anthanasopoulos, A., Dyson, R., Thanassoulis, E. (1997). Weight restrictions and value judgments in data envelopment analysis: Evolution, development and future directions. Annals of Operations Research, 73, 13-34.
1
2. Allen, R., Thanassoulis, E. (2004). Improving envelopment in data envelopment analysis. European Journal of Operational
2
Research,154, 363-379.
3
3. Andersen, P., Petersen, N.C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39 (10) 1261-1264.
4
4. Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of
5
Operational Research, 2, 429-444.
6
5. Charnes, A., Cooper, W.W., Huang, Z.M., Sun, D.B. (1990). Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of Econometrics, 46, 73-91.
7
6. Charnes, A., Cooper, W.W., Thrall, R.M. (1991). A structure for classifying and characterizing efficiency and inefficiency in data envelopment analysis, Journal of Productivity Analysis, 2(3) 197-237.
8
7. Dyson, R.G., Thanassoulis, E. (1988). Reducing weight flexibility in DEA. Journal of Operational Research Society, 39(6) 563-576.
9
8. Farrell, M.J., The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, 125, 252-267.
10
9. Jahanshahloo, G.R., Soleimani-Damaneh, M. (2005). A note on simulating weights restrictions in DEA: An improvement of Thanassoulis and Allen's method. Computers & Operation Research, 32, 1037-1044.
11
10. Kuosmanen, T. (2005). Weak disposability in nonparametric production analysis with undesirable outputs. American Journal of Agricultural Economics, 87(4) 1077-1082.
12
11. Kuosmanen, T., Podinovski, V.V. (2009). Weak disposablity in nonparametric production analysis: reply to Fare and Grosskopf. American Journal of Agricultural Economics, 91(2) 539-545.
13
12. Olesen, O.B., Petersen, N.C. (1996). Indicators of ill-conditioned data sets and model misspecification in data envelopment analysis: An extended facet approach. Management Science, 42(2) 205-219.
14
13. Podinovski, V.V., Kuosmanen, T. (2011). Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions. European Journal of Operational Research, 211(3) 577-585.
15
14. Roll, Y., Golany, B. (1993). Alternate methods treating factor weights in DEA. OMEGA: The International Journal of Management Science, 21(1) 99-109.
16
15. Roll, Y., Cook, W.D., Golany, B. (1991). Controlling factor weights in DEA. IIE Transactions, 23(1) 2-9.
17
16. Sowlati, T., Paradi, J. (2004). Establishing the ''practical frontier'' in data envelopment analysis. Omega, 32(4) 261-271.
18
17. Thanassoulis, E., Allen, R. (1998). Simulating weight restrictions in data envelopment analysis by means of unobserved DMUs. Management Science, 44(4) 586-594.
19
18. Thanassoulis, E. Kortelainen, M., Allen, R. (2012). Improving envelopment in data envelopment analysis under variable returns to scale. European Journal of Operational Research, 218, 175-185.
20
19. Thompson, R.G., Langemeier, L.N. Lee, C-H., Lee, E., Thrall, R.M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of Econometrics, 46, 93-108.
21
20. Wong, Y., -H.B., Beasley, J.E. (1990). Restricting weight flexibility in DEA. Journal of Operational Society, 41(9) 829-835.
22