ORIGINAL_ARTICLE
An Optimized Heat Sink for Thermophotovoltaic Panels
In the present work, an innovative hybrid solar panel is proposed, which can be used to pave floors or to cover roofs. A particular heat sink is employed, which gives robustness to the panel and provides a better heat transfer effectiveness with respect to tube heat exchangers. The geometry of the heat sink which is employed in the panel is optimized with the help of a numerical model and a genetic algorithm. Some optimization examples are shown. The velocity and temperature distributions on the heat sink cross section are also investigated. The presented hybrid panel allows till 20% increase in the electrical efficiency with respect to a simple photovoltaic panel. Moreover, it can be easily installed under every environmental condition due to its robustness and resistance to water infiltration.
https://www.journal-aprie.com/article_57657_9a8e2f896a40179a59edd3acb2c52050.pdf
2018-06-01
1
9
10.22105/jarie.2018.108774.1026
Solar panel
Heat sink
Thermal efficiency
Genetic Algorithm
Giampietro
Fabbri
giampietro.fabbri@unibo.it
1
D.I.N.,University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
LEAD_AUTHOR
Matteo
Greppi
matteo.greppi2@unibo.it
2
CIRI Aeronautica, University of Bologna, Via Fontanelle 40, 47121 Forli, Italy
AUTHOR
[1] Dupré, O., Vaillon, R., & Green, M. A. (2015). Physics of the temperature coefficients of solar cells. Solar energy materials and solar cells, 140, 92-100.
1
[2] Polman, A., & Atwater, H. A. (2012). Photonic design principles for ultrahigh-efficiency photovoltaics. Nature materials, 11(3), 174.
2
[3] Michael, J. J., Iniyan, S., & Goic, R. (2015). Flat plate solar photovoltaic–thermal (PV/T) systems: a reference guide. Renewable and sustainable energy reviews, 51, 62-88.
3
[4] Santbergen, R., & van Zolingen, R. C. (2008). The absorption factor of crystalline silicon PV cells: A numerical and experimental study. Solar energy materials and solar cells, 92(4), 432-444.
4
[5] Santbergen, R. (2008). Optical absorption factor of solar cells for PVT systems (Doctoral dissertation, Eindhoven University of Technology). Retrieved from https://research.tue.nl/en/publications/optical-absorption-factor-of-solar-cells-for-pvt-systems
5
[6] Tripanagnostopoulos, Y., Nousia, T. H., Souliotis, M., & Yianoulis, P. (2002). Hybrid photovoltaic/thermal solar systems. Solar energy, 72(3), 217-234.
6
[7] Charalambous, P. G., Kalogirou, S. A., Maidment, G. G., & Yiakoumetti, K. (2011). Optimization of the photovoltaic thermal (PV/T) collector absorber. Solar energy, 85(5), 871-880.
7
[8] Garg, H. P., & Agarwal, R. K. (1995). Some aspects of a PV/T collector/forced circulation flat plate solar water heater with solar cells. Energy conversion and management, 36(2), 87-99.
8
[9] Zondag, H. A., De Vries, D. W., Van Helden, W. G. J., Van Zolingen, R. J. C., & Van Steenhoven, A. A. (2003). The yield of different combined PV-thermal collector designs. Solar energy, 74(3), 253-269.
9
[10] Zondag, H. A., de Vries, D. D., Van Helden, W. G. J., Van Zolingen, R. J. C., & Van Steenhoven, A. A. (2002). The thermal and electrical yield of a PV-thermal collector. Solar energy, 72(2), 113-128.
10
[11] Tiwari, A., & Sodha, M. S. (2006). Performance evaluation of hybrid PV/thermal water/air heating system: a parametric study. Renewable energy, 31(15), 2460-2474.
11
[12] Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE transactions on systems, man, and cybernetics, 16(1), 122-128.
12
[13] Deb, K. (2014). Multi-objective optimization. Search methodologies (pp. 403-449). Springer, Boston, MA.
13
[14] Deb, K., Anand, A., & Joshi, D. (2002). A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary computation, 10(4), 371-395.
14
[15] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
15
[16] Farina, M., Deb, K., & Amato, P. (2004). Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE transactions on evolutionary computation, 8(5), 425-442.
16
[17] Peigin, S., Epstein, B., & Gali, S. (2004). Multilevel parallelization strategy for optimization of aerodynamic shapes. Parallel computational fluid dynamics 2003, 505-512.
17
[18] Queipo, N., Devarakonda, R., & Humphrey, J. A. C. (1994). Genetic algorithms for thermosciences research: application to the optimized cooling of electronic components. International journal of heat and mass transfer, 37(6), 893-908.
18
[19] Peng, H., & Ling, X. (2008). Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms. Applied thermal engineering, 28(5-6), 642-650.
19
[20] Chow, T. T., Zhang, G. Q., Lin, Z., & Song, C. L. (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and buildings, 34(1), 103-109.
20
[21] Gosselin, L., Tye-Gingras, M., & Mathieu-Potvin, F. (2009). Review of utilization of genetic algorithms in heat transfer problems. International journal of heat and mass transfer, 52(9-10), 2169-2188.
21
[22] Azarkish, H., Sarvari, S. M. H., & Behzadmehr, A. (2010). Optimum design of a longitudinal fin array with convection and radiation heat transfer using a genetic algorithm. International journal of thermal sciences, 49(11), 2222-2229.
22
[23] Sanaye, S., & Hajabdollahi, H. (2010). Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm. Applied energy, 87(6), 1893-1902.
23
[24] Najafi, H., Najafi, B., & Hoseinpoori, P. (2011). Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm. Applied thermal engineering, 31(10), 1839-1847.
24
[25] Das, R. (2012). Application of genetic algorithm for unknown parameter estimations in cylindrical fin. Applied soft computing, 12(11), 3369-3378.
25
[26] Amini, M., & Bazargan, M. (2014). Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Applied thermal engineering, 69(1-2), 278-285.
26
[27] Patel, V. K., & Savsani, V. J. (2015). Heat transfer search (HTS): a novel optimization algorithm. Information sciences, 324, 217-246.
27
[28] Wen, J., Yang, H., Tong, X., Li, K., Wang, S., & Li, Y. (2016). Configuration parameters design and optimization for plate-fin heat exchangers with serrated fin by multi-objective genetic algorithm. Energy conversion and management, 117, 482-489.
28
[29] Biyanto, T. R., Gonawan, E. K., Nugroho,nG., Hantoro, R., Cordova, H., & Indrawati, K. (2016). Heat exchanger network retrofit throughout overall heat transfer coefficient by using genetic algorithm. Applied thermal engineering, 94, 274-281.
29
[30] Khan, T. A., & Li, W. (2017). Optimal design of plate-fin heat exchanger by combining multi-objective algorithms. International journal of heat and mass transfer, 108, 1560-1572.
30
[31] Fabbri, G. (1997). A genetic algorithm for fin profile optimization. International journal of heat and mass transfer, 40(9), 2165-2172.
31
[32] Fabbri, G. (1998). Optimization of heat transfer through finned dissipators cooled by laminar flow. International journal of heat and fluid flow, 19(6), 644-654.
32
[33] Fabbri, G., Greppi, M., & Lorenzini, M. (2012). Optimization with genetic algorithms of PVT system global efficiency. Journal of energy and power engineering, 6(7), 1035.
33
ORIGINAL_ARTICLE
Reduction of Rework in Bearing End Plate Using Six Sigma Methodology: A Case Study
Six Sigma is a structured and systematic approach to performance and quality improvement. Six Sigma is a rigorous methodology consists of five major phases, namely definition, measure, analysis, improvement, and control for problem solving. A lot of case studies have been published and many large organizations have reported financial benefits by the application of Six Sigma methodology. This paper is a case study on reducing the bearing end plate reworks in a machining process through the application of Six Sigma methodology. The study focuses on reducing the rework due to thickness and diameter variation. From the list of identified potential causes, two causes, namely tool type and coolant pH are shortlisted as root causes. The optimum values of tool type and coolant pH, which would simultaneously optimize the diameter and thickness, are identified using the design of experiments and Taguchi's loss function approach. The implementation of optimum settings shows that the capability of the machining process to meet the customer requirements on thickness and diameter has substantially improved and rework has reduced.
https://www.journal-aprie.com/article_61055_b5d43510a74f7e80e308871a92f25007.pdf
2018-06-01
10
26
10.22105/jarie.2018.120059.1033
Six Sigma
DMAIC
Analysis of variance
Simultaneous optimization of multiple characteristics
Taguchi's loss function
Bearing end plate
Boby
John
boby@isibang.ac.in
1
SQC &, OR Unit, Indian Statistical Institute, Bangalore, India.
LEAD_AUTHOR
Abdulrahiman
Areshankar
a.rahiman@gmail.com
2
Manzusha Machinery Works, Bangalore, India.
AUTHOR
[1] Antony, J. (2006). Six sigma for service processes. Business process management journal, 12(2), 234-248.
1
[2] GE Annual Report. (2002). Retrieved from https://www.ge.com/ar2002/index_noflash.jsp
2
[3] Honeywell. (2002). Honeywell Annual Report. Retrieved from http://investor.honeywell.com/file/4121346/Index?KeyFile=1500081332
3
[4] 3M science to applied to life. (2003). 3M Annual Report. Retrieved from http://investors.3m.com/financials/sec-filings/2003/default.aspx
4
[5] Arndt, M., & Brady, D. (2004). 3M's Rising Star; Jim McNerney is racking up quite a record at 3M. Now, can he rev up its innovation machine?. Business week, (3878), 62-62.
5
[6] Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring business excellence, 6(4), 20-27.
6
[7] Antony, J. (2004). Six Sigma in the UK service organisations: results from a pilot survey. Managerial auditing journal, 19(8), 1006-1013.
7
[8] Brun, A. (2011). Critical success factors of Six Sigma implementations in Italian companies. International journal of production economics, 131(1), 158-164.
8
[9] Ashok Sarkar, S., Ranjan Mukhopadhyay, A., & Ghosh, S. K. (2013). Improvement of claim processing cycle time through Lean Six Sigma methodology. International journal of lean six sigma, 4(2), 171-183.
9
[10] Antony, J., Kumar, M., & Madu, C. N. (2005). Six sigma in small-and medium-sized UK manufacturing enterprises: Some empirical observations. International journal of quality & reliability management, 22(8), 860-874.
10
[11] Chakrabarty, A., & Chuan Tan, K. (2007). The current state of six sigma application in services. Managing service quality: An international journal, 17(2), 194-208.
11
[12] Kwak, Y. H., & Anbari, F. T. (2006). Benefits, obstacles, and future of six sigma approach. Technovation, 26(5-6), 708-715.
12
[13] Motwani, J., Kumar, A., & Antony, J. (2004). A business process change framework for examining the implementation of Six Sigma: a case study of Dow Chemicals. The TQM magazine, 16(4), 273-283.
13
[14] Karunakaran, S. (2016). Innovative application of LSS in aircraft maintenance environment. International journal of lean six sigma, 7(1), 85-108.
14
[15] Bharathi, K. S., Vinodh, S., Devarapu, S., & Siddhamshetty, G. (2017). Application of Lean approach for reducing weld defects in a valve component: a case study. International journal of lean six sigma, 8(2), 181-209.
15
[16] Panat*, R., Dimitrova, V., Selvy Selvamuniandy, T., Ishiko, K., & Sun, D. (2014). The application of Lean Six Sigma to the configuration control in Intel’s manufacturing R&D environment. International journal of lean six sigma, 5(4), 444-459.
16
[17] Jirasukprasert, P., Arturo Garza-Reyes, J., Kumar, V., & K. Lim, M. (2014). A Six Sigma and DMAIC application for the reduction of defects in a rubber gloves manufacturing process. International journal of lean six sigma, 5(1), 2-21.
17
[18] Adikorley, R. D., Rothenberg, L., & Guillory, A. (2017). Lean Six Sigma applications in the textile industry: a case study. International journal of lean six sigma, 8(2), 210-224.
18
[19] Desai, D., & Prajapati, B. N. (2017). Competitive advantage through Six Sigma at plastic injection molded parts manufacturing unit: A case study. International journal of lean six sigma, 8(4), 411-435.
19
[20] Swarnakar, V., & Vinodh, S. (2016). Deploying Lean Six Sigma framework in an automotive component manufacturing organization. International journal of lean six sigma, 7(3), 267-293.
20
[21] Noori, B., & Latifi, M. (2018). Development of Six Sigma methodology to improve grinding processes: a change management approach. International journal of lean six sigma, 9(1), 50-63.
21
[22] Biju, S., & Nair, N. S. (2017). Measuring academic quality: a three-dimensional approach for internal audit using DMAIC. International journal of six sigma and competitive advantage, 10(3-4), 236-257.
22
[23] Isack, H. D., Mutingi, M., Kandjeke, H., Vashishth, A., & Chakraborty, A. (2018). Exploring the adoption of Lean principles in medical laboratory industry: Empirical evidences from Namibia. International journal of lean six sigma, 9(1), 133-155.
23
[24] Dale, B. G., Van der Wiele, T., & Van Iwaarden, J. (2007). Managing quality. John Wiley & Sons.
24
[25] Gijo, E. V., Scaria, J., & Antony, J. (2011). Application of Six Sigma methodology to reduce defects of a grinding process. Quality and reliability engineering international, 27(8), 1221-1234.
25
[26] Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The six sigma way: How GE, Motorola, and other top companies are honing their performance. McGraw-Hill (New York).
26
[27] Stamatis, D.H. (2004). Six Sigma fundamentals: A complete guide to the system, methods and tools. Productivity Press, New York, NY.
27
[28] Omachonu, V. K., & Ross, J. E. (2004). Principles of total quality. CRC Press.
28
[29] Adams, C., Gupta, P., & Wilson, C. (2007). Six sigma deployment. Routledge.
29
[30] Garza-Reyes, J.A., Oraifige, I., Soriano-Meier, H., Harmanto, D.,& Rocha-Lona, L. (2010). An empirical application of Six Sigma and DMAIC methodology for business process improvement. Proceedings of the 20th international conference on flexible automation and intelligent manufacturing (FAIM), 92-100. San Francisco, CA.
30
[31] Montgomery, D.C. (2009).Statistical Quality Control: A Modern Introduction. Wiley India.
31
[32] John, B., Kadadevaramath, R., & Edinbarough, I. (2016). Application of multistage process control methodology for software quality management. Journal of project management, 1(2), 55-66.
32
[33] Kapuria, T. K., Rahman, M., & Haldar, S. (2017). Root Cause Analysis and Productivity Improvement of An Apparel Industry In Bangladesh Through Kaizen Implementation. Journal of applied research on industrial engineering, 4(4), 227-239.
33
[34] Breyfogle III, F. W., Cupello, J. M., & Meadows, B. (2000). Managing six sigma: A practical guide to understanding, assessing, and implementing the strategy that yields bottom-line success. John Wiley & Sons.
34
[35] John, B. (2015). A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process. International journal of industrial engineering computations, 6(4), 469-480.
35
[36] Jafari, H., & Hajikhani, A. (2016). Multi Objective Decision Making for Impregnability of Needle Mat Using Design of Experiment Technique and Respond Surface Methodology. Journal of applied research on industrial engineering, 3(1), 30-38.
36
[37] Gupta, A. K., & Kabe, D. G. (2013). Design and Analysis of Experiments. World Scientific Publishing Company.
37
[38] John, B., Kadadevaramath, R. S., & EDINBAROU6H, I. A. (2017). Designing Software Development Processes to Optimize Multiple Output Performance Characteristics. Software quality professional, 19(4).
38
[39] Fowleks, W.Y., & Creveling, C. M. (1998). Engineering methods for robust product design: using Taguchi methods in technology and product development. Addison-Wesley Longman, USA.
39
[40] John, B. (2012). Simultaneous optimization of multiple performance characteristics of carbonitrided pellets: a case study. The international journal of advanced manufacturing technology, 61(5-8), 585-594.
40
[41] Breyfogle, F. W. (1999). Implementing six sigma: Smarter solutions using statistical methods. John Wiley, New York.
41
[42] Benbow, D.W., &Kubiak, T.M. (2005). The certified Six Sigma black belt handbook. ASQ Quality Press, Milwaukee.
42
ORIGINAL_ARTICLE
Residual Lifetime Prediction for Multi-State System Using Control Charts to Monitor Affecting Noise Factor on Deterioration Process
In this research, multi-state complex systems are analyzed in order to measure reliability and predict residual of systems’ lifetime under the effect of an out of control noise factor. Hence, the analytic method helps us to estimate multi-state system reliability, and then means residual lifetime that is calculated under normal conditions. Finally, the calculation is updated for out of control noisy condition using the accelerated method. To reveal the applied results, the proposed policy is implemented in a case study in a molding machine on SNJ Co. at Isfahan.
https://www.journal-aprie.com/article_58075_69eca85d99747a81241afbbdde1e5401.pdf
2018-06-01
27
38
10.22105/jarie.2018.111526.1027
Mean residual lifetime
Multi-state system
Noise factor
System reliability
Control chart
Neda
Gol-Ahmadi
neda.gol.67@gmail.com
1
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
LEAD_AUTHOR
Sadigh
Raissi
raissi@azad.ac.ir
2
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
AUTHOR
[1] Das, S., & Nanda, A. K. (2013). Some stochastic orders of dynamic additive mean residual life model. Journal of statistical planning and inference, 143(2), 400-407.
1
[2] Deloux, E., Castanier, B., Yeung, T., & Bérenguer, C. (2006, November). A Predictive Maintenance Policy Combining Statistical Process Control and Condition-Based Approaches. 31 ESReDA seminar on ageing (pp. 210-220).
2
[3] Dieulle, L., Bérenguer, C., Grall, A., & Roussignol, M. (2003). Sequential condition-based maintenance scheduling for a deteriorating system. European journal of operational research, 150(2), 451-461.
3
[4] Eryilmaz, S. (2017). Computing optimal replacement time and mean residual life in reliability shock models. Computers & industrial engineering, 103, 40-45.
4
[5] Huang, J., & Zuo, M. J. (2004). Dominant multi-state systems. IEEE transactions on reliability, 53(3), 362-368.
5
[6] Huang, W., Zhou, J., & Ning, J. (2016). A Condition Based Maintenance for System Subject to Competing Failure due to Degradation and Shock. International journal of applied mathematics, 46(2).
6
[7] Kurniati, N., Yeh, R. H., & Lin, J. J. (2015). Quality inspection and maintenance: the framework of interaction. Procedia manufacturing, 4, 244-251.
7
[8] Li, W., & Pham, H. (2005). Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE transactions on reliability, 54(2), 297-303.
8
[9] Yingkui, G., & Jing, L. (2012). Multi-state system reliability: a new and systematic review. Procedia engineering, 29, 531-536.
9
[10] Zhao, M., Jiang, H., & Liu, X. (2013). A note on estimation of the mean residual life function with left-truncated and right-censored data. Statistics & probability letters, 83(10), 2332-2336.
10
[11] Madsen, H. O. (1985, May). Random fatigue crack growth and inspection. Proceeding of 4th international conference on structural safety and reliability (ICOSSAR’85). Kobe, Japan.
11
[12] Wu, S., & Wang, W. (2011). Optimal inspection policy for three-state systems monitored by control charts. Applied mathematics and computation, 217(23), 9810-9819.
12
ORIGINAL_ARTICLE
Resource Constrained Project Scheduling with Stochastic Resources
Despite the dynamic nature of real life scheduling problems, few studies focus on stochastic resource constrained project scheduling problem and its variants. In this study, we consider stochastic resource possibilities and propose a new chance constraint, piecewise-linear and mixed integer programming model. Model is tested and verified with known project instances. One of the main strengths of the proposed model is it can be used to construct baseline schedules with a predetermined confidence interval. This gives scheduler an opportunity to construct proactive actions in order to minimize disruptions.
https://www.journal-aprie.com/article_55713_dacdb3737e10572343c991a0f8b22b9c.pdf
2018-06-01
39
49
10.22105/jarie.2018.98906.1019
Stochastic resources
resource demand
resource constrained scheduling
Furkan
Uysal
furkanuysal@gmail.com
1
Ministry of Development, Turkey.
LEAD_AUTHOR
Selçuk Kürşat
Işleyen
isleyens@gazi.edu.tr
2
Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey.
AUTHOR
Cihan
Cetinkaya
cihancetinkaya@gmail.com
3
Department of Industrial Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, Turkey.
AUTHOR
[1] Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics, 5(1), 11-24.
1
[2] Ballestin, F., & Leus, R. (2009). Resource‐Constrained Project Scheduling for Timely Project Completion with Stochastic Activity Durations. Production and operations management, 18(4), 459-474.
2
[3] Tavakkoli-Moghaddam, R., Jolai, F., Vaziri, F., Ahmed, P. K., & Azaron, A. (2005). A hybrid method for solving stochastic job shop scheduling problems. Applied mathematics and computation, 170(1), 185-206.
3
[4] Wang, Y., He, Z., Kerkhove, L. P., & Vanhoucke, M. (2017). On the performance of priority rules for the stochastic resource constrained multi-project scheduling problem. Computers & industrial engineering, 114, 223-234.
4
[5] Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2008). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of scheduling, 11(2), 121-136.
5
[6] Valls, V., Laguna, M., Lino, P., Pérez, A., & Quintanilla, S. (1999). Project scheduling with stochastic activity interruptions. Project scheduling (pp. 333-353). Springer, Boston, MA.
6
[7] Wang, L., Huang, H., & Ke, H. (2015). Chance-constrained model for RCPSP with uncertain durations. Journal of uncertainty analysis and applications, 3(1), 12.
7
[8] Uysal, F., Isleyen, S., Cetinkaya, C., & Celik, N. (2015). Resource Constrained Project Scheduling Problem: An Analytical Approach. V international conference industrial engineering and environmental protection, 357-362.
8
[9] Hartmann, S., & Briskorn, D. (2008). A survey of deterministic modeling approaches for project scheduling under resource constraints. European journal of operational research, 207, 1-14.
9
[10] Kolisch, R., & Sprecher, A. (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European journal of operational research, 96(1), 205-216.
10
[11] Dadfar, H., & Gustavsson, P. (1992). Competition by effective management of cultural diversity: the case of international construction projects. International studies of management & organization, 22(4), 81-92.
11
[12] Williams, T. M. (1992). Practical use of distributions in network analysis. Journal of the operational research society, 43(3), 265-270.
12
[13] Golenko-Ginzburg, D., & Gonik, A. (1997). Stochastic network project scheduling with non-consumable limited resources. International journal of production economics, 48(1), 29-37.
13
[14] Shou, Y., & Wang, W. (2012). Robust optimization-based genetic algorithm for project scheduling with stochastic activity durations. International information institute (Tokyo), 15(10), 4049- 4064.
14
[15] Yang, I. T., & Chang, C. Y. (2005). Stochastic resource-constrained scheduling for repetitive construction projects with uncertain supply of resources and funding. International journal of project management, 23(7), 546-553.
15
[16] Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European journal of operational research, 165(2), 289-306.
16
[17] Kis, T. (2005). A branch-and-cut algorithm for scheduling of projects with variable-intensity activities. Mathematical programming, 103(3), 515-539.
17
[18] Long, L. D., & Ohsato, A. (2008). Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International journal of project management, 26(6), 688-698.
18
[19] Schonberger, R. J. (1981). Why projects are “always” late: a rationale based on manual simulation of a PERT/CPM network. Interfaces, 11(5), 66-70.
19
[20] Golenko-Ginzburg, D., Gonik, A., & Laslo, Z. (2003). Resource constrained scheduling simulation model for alternative stochastic network projects. Mathematics and computers in simulation, 63(2), 105-117.
20
[21] Li, S., Jia, Y., & Wang, J. (2012). A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling. The international journal of advanced manufacturing technology, 63(1-4), 65-76.
21
[22] Erdogan, S. A., & Denton, B. (2013). Dynamic appointment scheduling of a stochastic server with uncertain demand. INFORMS journal on computing, 25(1), 116-132.
22
[23] Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science, 6(1), 73-79.
23
[24] Charnes, A., & Cooper, W. W. (1962). Chance constraints and normal deviates. Journal of the American statistical association, 57(297), 134-148.
24
[25] Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisficing under chance constraints. Operations research, 11(1), 18-39.
25
[26] Williams, H. P. (2013). Model building in mathematical programming. John Wiley & Sons.
26
ORIGINAL_ARTICLE
A Solution Approach for Solving Fully Fuzzy Quadratic Programming Problems
Quadratic Programming has been widely applied to solve real-world problems. This paper describes a solution method for solving a special class of fuzzy quadratic programming problems with fuzziness in relations. Then the method is generalized to a more general fuzzy quadratic programming problem, where the cost coefficients, the matrix of the quadratic form, constraints coefficients, and the right-hand sides are all fuzzy numbers. Finally, some examples are taken to the utility of our proposed method.
https://www.journal-aprie.com/article_58085_0c95c44e9ded4db197679120182ff092.pdf
2018-06-01
50
61
10.22105/jarie.2018.111797.1028
Fuzzy number
Quadric Programming
membership function
Fuzzy arithmetic
fuzzy constraint
Nemat Allah
Taghi-Nezhad
ntaghinezhad@gonbad.ac.ir
1
Department of Mathematics, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad, Iran.
AUTHOR
Fatemeh
Taleshian
fatemeh.taleshian@yahoo.com
2
Department of Mathematics, Shahrood University of Technology, Shahrood, Iran.
LEAD_AUTHOR
[1] Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2013). Nonlinear programming: theory and algorithms. John Wiley & Sons.
1
[2] Beck, A., & Teboulle, M. (2000). Global Optimality conditions for quadratic optimization problems with binary constraints. SIAM journal on optimization, 11(1), 179-188.
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22
ORIGINAL_ARTICLE
Prioritization of the Advertising Activities of Tehran Stock Exchange Investment Companies based on Investors' Financial Literacy using Step-by-Step ANP Approach
Understanding financial literacy and knowing the financial skills that an investor can invest in Tehran Stock Exchange are very important in order to reduce investor's concerns and worries. Hence, investment companies that invest on behalf of their stockholders can reduce these concerns and worries. Therefore, the advertising activities of these companies are important in attracting investors, as well as providing company activities, etc. In this paper, the appropriate criteria and desired advertising activities that are obtained by financial literacy for investment companies, which are invested in Tehran Stock Exchange, are outlined. These advertising activities are ranked by using the step-by-step approach of analytic network process which is a multi-criteria decision-making method. According to its results, advertising activities using TV has the most weight among other advertising activities. This indicates that the use of television for advertising in company investments in Tehran Stock Exchange will be very influential. Also, social networks are ranked second, and in the end, two activities of billboards and magazines with a very small difference are placed in the fifth and sixth rankings. These two advertising activities have less effect than other ones in Tehran Stock Exchange investment companies.
https://www.journal-aprie.com/article_55712_04e0a0cbba626838b872fd15ade5e592.pdf
2018-06-01
62
80
10.22105/jarie.2018.99396.1020
Advertising activities
Tehran Stock Exchange Investment
Literacy
Network analysis process
Ali
Montazeri
ali_montazeri1991@yahoo.com
1
Department of Industrial Engineering, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran.
AUTHOR
Javid
Jouzdani
jouzdani@gut.ac.ir
2
Department of Industrial Engineering, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran.
LEAD_AUTHOR
[1] Dianti Deilami, Z., & Hanifafzadeh, M. (2015). Surveying the Level of Financial Literacy of Tehran Families and its Related Factors. Scientific information database of university jihad, 115-139.
1
[2] Kefela, G. T. (2010). Promoting access to finance by empowering consumers-Financial literacy in developing countries. Educational research and reviews, 5(5), 205.
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[4] Jafar Tehrani, M. (2008). The Identification of Internet-based Advertising Methods and Website Users and Managers' Prioritization in Selecting Advertising Tools (Master's thesis, Al-Zahra University- Faculty of Economic and Social Sciences).
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[6] Tabriz, A., Bagherzadeh, A., & Azar, M. (2009), A Combined Model of Hierarchy Analysis Process - An Ideal Planning for Measurement of the Structure of Quality Control Systems in Service Organizations. Transformation management research.
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[7] Jha, P. C., Aggarwal, R., & Gupta, A. (2011). Optimal media planning for multi-products in segmented market. Applied mathematics and computation, 217(16), 6802-6818.
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11
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17
ORIGINAL_ARTICLE
Prioritization of Different Solvency Monitoring Systems of Iran Insurance Company Using Combined Approach of Analytic Network Process and DEMATEL
The Bankruptcy of insurance companies compared to companies in other industries can have a more devastating impact on the customers of this industry and on society as a whole; because the insurance company's bankruptcy is equal to the risk of the economic presence of the insurer and third parties. Severe outcomes of insurance company's bankruptcy have made financial monitoring institutions to design systems for evaluation and supervision of insurance companies’ solvency in order to reduce bankruptcy risk of these companies. Inappropriate design of these systems may transmit incorrect signals to the insurance companies, lawmakers, and insurers and would have irreversible effects. Thus, the current research aims at prioritizing different solvency monitoring systems of Iran Insurance Company using Analytic Network Process (ANP). This is an applied research in terms of purpose, and it is descriptive - analytical research in terms of data collection method. The identified indexes for evaluation and monitoring the insurance companies’ solvency include the quantitative computational aspects, flexibility, and qualitative aspects. Following data collection using the network analysis software, which was used for advanced hierarchy analysis and DEMATEL, which was used for ranking qualitative, quantitative aspects, and flexibility, it is concluded that the quantitative computational index has the highest impact, and the qualitative index has the lowest impact on the solvency of Iran Insurance Company.
https://www.journal-aprie.com/article_65389_7b59f5a8781e8ebb827c1ad2b0c438ee.pdf
2018-06-01
81
96
10.22105/jarie.2018.128535.1036
Different solvency monitoring systems
Iran Insurance Company
Analytic Network Process (ANP)
DEMATEL
Hamzeh
Amin-Tahmasbi
amintahmasbi@guilan.ac.ir
1
Department of Industrial Engineering, Faculty of Technology and Engineering, East of Guilan, University of Guilan, Iran.
LEAD_AUTHOR
Seyedeh Samaneh
Shariatmadari
samaneh_shariatmadari@yahoo.com
2
Department of Industrial Management-Finance, Islamic Azad University of Anzali-International Center, Iran.
AUTHOR
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