ORIGINAL_ARTICLE
Functional process capability indices for nonlinear profile
A profile is a relationship between a response variable and one or more independent variables being controlled during the time. Process Capability Indices (PCI) are measured to evaluate the performance of processes in producing conforming products. Despite frequent applications of profile and a variety of available methods to monitor its different types, little researches have been carried out on determining capability indices of profile process. PCIs such as and in profile state, are used to evaluate process capability in producing conforming profiles. This paper presents a functional approach for nonlinear profiles which usually expressed as nonlinear regression. Thus, functions such as pertaining technical specification limits, mean and natural tolerance limits are determined as nonlinear profiles and also functional limit is applied to determine Functional Capability Indices (FCI) and of functional nonlinear profile. Easy calculation and the ability to calculate FC in each period and at each point are from the advantages of this method over the other methods.
http://www.jise.ir/article_76523_3e55afbf2bdd03ca4df1bea75eb52575.pdf
2018-12-26T11:23:20
2020-09-26T11:23:20
1
14
profile monitoring
non-linear profile
functional process capability index
vertical density profile
Ahmad
Mohammad Pour Larimi
mpl.ahmad.ie@gmail.com
true
1
Department of Industrial Engineering, Mazandaran Institute of Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran Institute of Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran Institute of Technology, Babol, Iran
LEAD_AUTHOR
Ramezan
Nemati keshteli
r.nemati@guilan.ac.ir
true
2
Faculty of Engineering Eastern Guilan, Guilan University, Babol, Iran
Faculty of Engineering Eastern Guilan, Guilan University, Babol, Iran
Faculty of Engineering Eastern Guilan, Guilan University, Babol, Iran
AUTHOR
Abdul Sattar
Safaei
s.safaei@nit.ac.ir
true
3
Department of Industrial Engineering, Babol University of Technology, P.O. Box 47148 - 71167, Babol, Iran
Department of Industrial Engineering, Babol University of Technology, P.O. Box 47148 - 71167, Babol, Iran
Department of Industrial Engineering, Babol University of Technology, P.O. Box 47148 - 71167, Babol, Iran
AUTHOR
Aslam, M., Wang, F. K., Khan, N., & Jun, C. H., 2018. A multiple dependent state repetitive sampling plan for linear profiles. Journal of the Operational Research Society, 69(3), pp. 467-473.
1
Chiang, J.Y., Lio, Y.L. and Tsai, T.R., 2017. MEWMA Control Chart and Process Capability Indices for Simple Linear Profiles with Within‐profile Autocorrelation. Quality and Reliability Engineering International, 33(5), pp.1083-1094.
2
Clements, J.A., 1989. Process capability calculations for non-normal distributions. Quality progress, 22, pp.95-100.
3
Ebadi, M. and Amiri, A., 2012. Evaluation of process capability in multivariate simple linear profiles. Scientia Iranica, 19(6), pp.1960-1968.
4
Guevara, R.D. and Vargas, J.A., 2015. Process capability analysis for nonlinear profiles using depth functions. Quality and Reliability Engineering International, 31(3), pp.465-487.
5
Guevara, R.D. and Vargas, J.A., 2016. Evaluation of process capability in multivariate nonlinear profiles. Journal of Statistical Computation and Simulation,86(12), pp.2411-2428.
6
Hosseinifard, S.Z., Abbasi, B. and Abdollahian, M., 2011, April. Performance analysis in non-normal linear profiles using gamma distribution. In Information Technology: New Generations (ITNG), 2011 Eighth International Conference on (pp. 603-607). IEEE.
7
Hyndman, R.J., 1996. Computing and graphing highest density regions. The American Statistician, 50(2), pp.120-126.
8
Jensen, W.A. and Birch, J.B., 2009. Profile monitoring via nonlinear mixed models. Journal of Quality Technology, 41(1), p.18.
9
Kane, V.E., 1986. Process capability indices. Journal of quality technology, 18(1), pp.41-52.
10
Keshteli, R.N., Kazemzadeh, R.B., Amiri, A. and Noorossana, R., 2014. Developing functional process capability indices for simple linear profile. Scientia Iranica. Transaction E, Industrial Engineering, 21(3), p.1044.
11
Keshteli, R. N., Kazemzadeh, R.B., Amiri, A. and Noorossana, R., 2014. Functional process capability indices for circular profile. Quality and Reliability Engineering International, 30(5), pp.633-644.
12
Noorossana, R., Aminnayeri, M. and Izadbakhsh, H., 2013. Statistical monitoring of polytomous logistic profiles in phase II. Scientia Iranica, 20(3), pp.958-966.
13
Noorossana, R., Izadbakhsh, H. and Nayebpour, M.R., 2014. A Likelihood Ratio Test Approach to Profile Monitoring in Tourism Industry. Applications & Applied Mathematics, 9(2).
14
Noorossana, R., Saghaei, A. and Amiri, A., 2011. Statistical analysis of profile monitoring (Vol. 865). John Wiley & Sons.
15
Rezaye Abbasi Charkhi, M., Aminnayeri, M. and Amiri, A., 2016. Process capability indices for logistic regression profile. Quality and Reliability Engineering International, 32(5), pp.1655-1661.
16
Vaghefi, A., Tajbakhsh, S.D. and Noorossana, R., 2009. Phase II monitoring of nonlinear profiles. Communications in Statistics—Theory and Methods, 38(11), pp.1834-1851.
17
Walker, E. and Wright, S.P., 2002. Comparing curves using additive models. Journal of Quality Technology, 34(1), p.118.
18
Wang, F.K., 2014. Measuring the process yield for simple linear profiles with one‐sided specification. Quality and Reliability Engineering International, 30(8), pp.1145-1151.
19
Wang, F.K. and Guo, Y.C., 2014. Measuring process yield for nonlinear profiles. Quality and Reliability Engineering International, 30(8), pp.1333-1339.
20
Wang, F.K., 2016. Process yield analysis for multivariate linear profiles. Quality Technology & Quantitative Management, 13(2), pp.124-138.
21
Wang, F.K. and Tamirat, Y., 2016. Process Yield for Multivariate Linear Profiles with One‐sided Specification Limits. Quality and Reliability Engineering International, 32(4), pp.1281-1293.
22
Wang, F.K., Huang, C.Y. and Tamirat, Y., 2017. Implementing EWMA Yield Index for Simple Linear Profiles with One‐sided Specifications in Product Acceptance Determination. Quality and Reliability Engineering International, 33(2), pp.401-412.
23
Williams, J.D., Woodall, W.H. and Birch, J.B., 2007. Statistical monitoring of nonlinear product and process quality profiles. Quality and Reliability Engineering International, 23(8), pp.925-941.
24
Young, T.M., Winistorfer, P.M. and Wang, S., 1999. Multivariate control charts of MDF and OSB vertical density profile attributes. Forest Products Journal, 49(5), p.79.
25
Zahra Hosseinifard, S. and Abbasi, B., 2012. Evaluation of process capability indices of linear profiles. International Journal of Quality & Reliability Management, 29(2), pp.162-176.
26
ORIGINAL_ARTICLE
New phase II control chart for monitoring ordinal contingency table based processes
In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ordinal log-linear model based processes in Phase II. Performance of the proposed control chart is evaluated through simulation studies and a real numerical example. In addition, to show the efficiency of ordinal-normal control chart, performance of the proposed control chart is compared with an existing Generalized-p chart. Results show the better performance of the proposed control chart in detecting the out-of-control condition.
http://www.jise.ir/article_78697_2c3d1b734442798b92cd933c2a47e0dd.pdf
2019-01-21T11:23:20
2020-09-26T11:23:20
15
34
Statistical process monitoring
ordinal contingency table
ordinal-normal control chart
phase
Ahmad
Hakimi
a.hakimi@eng.uok.ac.ir
true
1
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Hiwa
Farughi
h.farughi@uok.ac.ir
true
2
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Amirhossein
Amiri
amiri@shahed.ac.ir
true
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
AUTHOR
Jamal
Arkat
j.arkat@uok.ac.ir
true
4
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Agresti, A. (2003). Categorical data analysis (Vol. 482). John Wiley & Sons.
1
Agresti, A. (2010). Analysis of ordinal categorical data (Vol. 656). John Wiley & Sons.
2
Armitage, P. (1955). Tests for linear trends in proportions and frequencies. Biometrics, 11(3), 375-386.
3
Beh, E.J., & Davy, P.J. (1998). Theory & Methods: Partitioning Pearson’s Chi‐Squared Statistic for a Completely Ordered Three‐Way Contingency Table. Australian & New Zealand Journal of Statistics, 40(4), 465-477.
4
Brzezińska, J. (2016). Ordinal Log-Linear Models for Contingency Tables. Folia Oeconomica Stetinensia, 16(1), 264-273.
5
Hakimi, A., Farughi, H., & Amiri, A. (2018). Phase II Monitoring of the Ordinal Multivariate Categorical Processes. Submitted to Communications in Statistics-Simulation and Computation.
6
Kamranrad, R., Amiri, A., & Niaki, S.T.A. (2017a). New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II. Quality and Reliability Engineering International, 33(5), 1105-1129.
7
Kamranrad, R., Amiri, A., & Niaki, S.T.A. (2017b). Phase II monitoring and diagnosing of multivariate categorical processes using generalized linear test-based control charts, Communications in Statistics-Simulation and Computation, 46(8), 5951-5980.
8
Kieffer, D., Bianchetti, L., & Poch, O. (2012). Wicker N. Perfect sampling on 2×⋯× 2× K contingency tables with an application to SAGE data. Journal of Statistical Planning and Inference, 142(4), 896-901.
9
Kijima, S., & Matsui, T. (2006). Polynomial time perfect sampling algorithm for two‐rowed contingency tables. Random Structures & Algorithms, 29(2), 243-256.
10
Li, J., Tsung, F., & Zou, C. (2012). Directional control schemes for multivariate categorical processes. Journal of Quality Technology, 44(2), 136-154.
11
Li, Z., Zou, C., Wang, Z., & Huwang, L. (2013). A multivariate sign chart for monitoring process shape parameters. Journal of Quality Technology, 45(2), 149-165.
12
Li, J., Tsung, F., & Zou, C. (2014a). Multivariate binomial/multinomial control chart. IIE Transactions, 46(5), 526-542.
13
Li, J., Tsung, F., & Zou, C. (2014b). A simple categorical chart for detecting location shifts with ordinal information. International Journal of Production Research, 52(2), 550-562.
14
Lumley, T. (1996). Generalized estimating equations for ordinal data: A note on working
15
correlation structures. Biometrics, 52(1), 354-361.
16
Subramanyam, K., & Rao M.B. (1989). Analysis of odds ratios in 2×n ordinal contingency tables. Multivariate Statistics and Probability, 27(1), 505-520.
17
Yashchin, E. (2012). On detection of changes in categorical data. Quality Technology & Quantitative Management 9(1),79-96.
18
Zafar, S., Cheema, S.A., Beh, E.J., Hudson, I.L., Hudson, S.A., & Abell, A.D. (2013). Linking ordinal log-linear models with Correspondence Analysis: an application to estimating drug-likeness in the drug discovery process. 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1941-1951.
19
Zhen, X., & Basawa, I.V. (2009). Categorical time series models for contingency tables. Statistics & Probability Letters, 79(10), 1331-1336.
20
Zou, C., & Tsung, F. (2011). A multivariate sign EWMA control chart. Technometrics, 53(1), 84-97.
21
ORIGINAL_ARTICLE
An economic-statistical model for production and maintenance planning under adaptive non-central chi-square control chart
Most of the inventory control models assume that quality defect never happens, which means production process is perfect. However, in real manufacturing processes, the production process starts its operation in the in-control state; but after a period of time, shifts to the out-of-control state because of occurrence of some disturbances. In this paper, in order to approach the model to real manufacturing conditions, a process is considered in which quality defect and machine deterioration may occur. Since the adaptive control charts detect the occurrence of assignable cause quicker than the traditional control charts, an adaptive non-central chi-square control chart is designed, which monitors the process mean and variance, simultaneously. In addition, to reduce the failure rate of the machine, two types of maintenance policies consisting of reactive and preventive are planned. Then, the particle swarm optimization algorithm is employed to minimize the overall cost per cycle involving inventory cost, quality loss cost, inspection cost and maintenance cost subject to statistical quality constraints. Finally, to demonstrate the effectiveness of the suggested approach, two comparative studies are presented. The first one confirms that integration of production planning, maintenance policy and statistical process monitoring leads to a significant increase in the cost savings. The second one indicates superiority of the developed adaptive control chart in comparison with the control chart with the fixed parameters.
http://www.jise.ir/article_78698_0ab00ffd24a3e5b4ff1cc5a0f4f7f9be.pdf
2019-01-26T11:23:20
2020-09-26T11:23:20
35
65
Production Planning
maintenance policy
Economic-Statistical Design
non-central chi-square chart
adaptive control chart
ali
salmasnia
ali.salmasnia.85@gmail.com
true
1
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
LEAD_AUTHOR
farzaneh
Soltany
farzane.soltany@gmail.com
true
2
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
AUTHOR
maryam
noroozi
maryam_noroozi74@yahoo.com
true
3
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
AUTHOR
behnam
Abdzadeh
abedbehnam@yahoo.com
true
4
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
AUTHOR
Alamri, A. A., Harris, I., & Syntetos, A. A. (2016). Efficient inventory control for imperfect quality items. European Journal of Operational Research,254(1), 92-104.
1
Alinaghian, M., Ghazanfari, M., & Hamedani, S. G. (2017). A new bi-objective periodic vehicle routing problem with maximization market share in an uncertain competitive environment. Computational and Applied Mathematics, 1-23.
2
Ardakan, M. A., Hamadani, A. Z., Sima, M., & Reihaneh, M. (2016). A hybrid model for economic design of MEWMA control chart under maintenance policies. The International Journal of Advanced Manufacturing Technology, 83(9), 2101-2110.
3
Askari, E. A., & Bashiri, M. (2017). Design of a public bicycle-sharing system with safety. Computational and Applied Mathematics, 36(2), 2023-2041.
4
Azimifar, A., & Payan, S. (2016). Enhancement of heat transfer of confined enclosures with free convection using blocks with PSO algorithm. Applied Thermal Engineering, 101, 79-91.
5
Ben-Daya, M., & Makhdoum, M. (1998). Integrated production and quality model under various preventive maintenance policies. Journal of the Operational Research Society, 840-853.
6
Ben-Daya, M., & Rahim, M. A. (2000). Effect of maintenance on the economic design of x-control chart. European Journal of Operational Research, 120(1), 131-143.
7
Boukas, E. K., & Liu, Z. K. (2001). Production and maintenance control for manufacturing systems. Automatic Control, IEEE Transactions on, 46(9), 1455-1460.
8
Bouslah, B., Gharbi, A., & Pellerin, R. (2016). Joint economic design of production, continuous sampling inspection and preventive maintenance of a deteriorating production system. International Journal of Production Economics, 173, 184-198.
9
Clempner, J. B., & Poznyak, A. S. (2016). Constructing the Pareto front for multi-objective Markov chains handling a strong Pareto policy approach. Computational and Applied Mathematics, 1-25.
10
Costa, A. F. (1993). Joint economic design of X and R control charts for processes subject to two independent assignable causes. IIE transactions,25(6), 27-33.
11
Costa, A. F. (1994). charts with variable sample size. Journal of Quality Technology, 26(3), 155-163.
12
Costa, A. F. (1998). Joint X and R charts with variable parameters. IIE transactions, 30(6), 505-514.
13
Costa, A. F. (1999). Joint X and R charts with variable sample sizes and sampling intervals. Journal of Quality Technology, 31(4), 387.
14
Costa, A. F. (1999b). charts with variable parameters. Journal of Quality Technology, 31(4), 408-416.
15
Costa, A. F., & De Magalhaes, M. S. (2007). An adaptive chart for monitoring the process mean and variance. Quality and Reliability Engineering International, 23(7), 821-831.
16
Costa, A. F. B., & Rahim, M. A. (2004). Monitoring process mean and variability with one non-central chi-square chart. Journal of Applied Statistics, 31(10), 1171-1183.
17
Foumani, M., Gunawan, I., & Smith-Miles, K. (2015). Resolution of deadlocks in a robotic cell scheduling problem with post-process inspection system: Avoidance and recovery scenarios. IEEE International Conference on Industrial Engineering and Engineering Management, pages: 1107-1111, Bangkok, Thailand.
18
Foumani, M., Smith-Miles, K., & Gunawan, I. (2017). Scheduling of two-machine robotic rework cells: In-process, post-process and in-line inspection scenarios. Robotics and Autonomous Systems, 91, 210-225.
19
Foumani, M., Smith-Miles, K., Gunawan, I., & Moeini, A. (2017). A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system. Computers & Industrial Engineering, 112, 492-502.
20
Gharbi, A., & Kenne, J. P. (2003). Optimal production control problem in stochastic multiple-product multiple-machine manufacturing systems. IIE transactions, 35(10), 941-952.
21
Jiang Y, Chen M, Zhou D (2015). Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory. Journal of Manufacturing Systems;35:191–205
22
Jones, L. L., & Case, K. E. (1981). Economic Design of a Joint X-and R-Control Chart. IIE transactions, 13(2), 182-195.
23
Kenndy, J., & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks 1942-1948.
24
Lakhbab, H., & El Bernoussi, S. (2016). Hybrid nonmonotone spectral gradient method for the unconstrained minimization problem. Computational and Applied Mathematics, 1-10.
25
Lam, K. K., & Rahim, M. A. (2002). A sensitivity analysis of an integrated model for joint determination of economic design of control charts, economic production quantity and production run length for a deteriorating production system. Quality and Reliability Engineering International, 18(4), 305-320.
26
Mohamadi, M., Foumani, M., & Abbasi, B. (2011). Process Capability Analysis in the Presence of Autocorrelation. Journal of Optimization in Industrial Engineering, 15-20.
27
Nourelfath, M., Nahas, N., & Ben-Daya, M. (2016). Integrated preventive maintenance and production decisions for imperfect processes. Reliability Engineering & System Safety, 148, 21-31.
28
Ohta, H., Kimura, A., & Rahim, A. (2002). An economic model for (X) over-bar and R charts with time-varying parameters. Quality and Reliability Engineering International, 18(2), 131-139.
29
Pan, E. S., Jin, Y., & Wang, Y. (2011). Integration of economic production quantity in optimization design of control chart based on loss function and random process shift. Journal of Manufacturing Technology Management,22(7), 929-946.
30
Pan, E., Jin, Y., Wang, S., & Cang, T. (2012). An integrated EPQ model based on a control chart for an imperfect production process. International Journal of Production Research, 50(23), 6999-7011.
31
Porteus, E. L. (1986). Optimal lot sizing, process quality improvement and setup cost reduction. Operations research, 34(1), 137-144.
32
Prabhu, S. S., Runger, G. C., & Keats, J. B. (1993). X chart with adaptive sample sizes. International Journal of Production Research, 31(12), 2895-2909.
33
Prabhu, S. S., Montgomery, D. C., & Runger, G. C. (1994). A combined adaptive sample size and sampling interval X control scheme. Journal of Quality Technology, 26(3), 164-176.
34
Rahim, M. A. (1989). Determination of optimal design parameters of joint X and R charts. Journal of Quality Technology, 21(1), 65-70.
35
Rahim, M. A. (1994). Joint determination of production quantity, inspection schedule, and control chart design. IIE transactions, 26(6), 2-11.
36
Rahim, M. A., & Ben-Daya, M. (1998). A generalized economic model for joint determination of production run, inspection schedule and control chart design. International Journal of Production Research, 36(1), 277-289.
37
Rahim, M. A., & Costa, A. F. (2000). Joint economic design of X and R charts under Weibull shock models. International Journal of Production Research, 38(13), 2871-2889.
38
Rahim, M. A., & Ohta, H. (2005). An integrated economic model for inventory and quality control problems. Engineering Optimization, 37(1), 65-81.
39
Reynolds, M. R., Amin, R. W., Arnold, J. C., & Nachlas, J. A. (1988). Charts with variable sampling intervals. Technometrics, 30(2), 181-192.
40
Rivera-Gómez, H., Gharbi, A., & Kenné, J. P. (2013). Joint production and major maintenance planning policy of a manufacturing system with deteriorating quality. International Journal of Production Economics, 146(2), 575-587.
41
Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycles with imperfect production processes. IIE transactions, 18(1), 48-55.
42
Salmasnia, A., Abdzadeh, B., Namdar, M.R. (2017). A joint design of production run length, maintenance policy and control chart with multiple assignable causes. Journal of Manufacturing Systems, 42(1), 44-56.
43
Saniga, E. M. (1989). Economic statistical control-chart designs with an application to and R charts. Technometrics, 31(3), 313-320.
44
Tagaras, G. (1988). An integrated cost model for the joint optimization of process control and maintenance. Journal of the Operational Research Society, 757-766.
45
Xiang, Y. (2013). Joint optimization of control chart and preventive maintenance policies: A discrete-time Markov chain approach. European Journal of Operational Research, 229(2), 382-390.
46
ORIGINAL_ARTICLE
Measuring gas demand security using Principal Component Analysis (PCA): A case study
Safeguarding the energy security is an important energy policy goal of every country. Assuring sufficient and reliable resources of energy at affordable prices is the main objective of energy security. Due to such reasons as special geopolitical position, terrorist attacks and other unrest in the Middle East, securing Iran’s energy demand and increasing her natural gas exports have turned into a critical issue. The aim of this paper is to develop a composite index for evaluating the gas demand security. To this purpose, six individual indices (i.e. gas exports dependency, transportation cost, economic dependency, political stability of the exporting country, political stability of the importing country, and the purchasing power of the importing country) are identified and the Principle Component Analysis (PCA) method is employed to weigh and combine the indices into GDSI (Gas Demand Security Index). The results show an interesting counter-intuitive phenomenon that the political stability of the importing and exporting countries have respectively the most and the least effects on the obtained composite index.
http://www.jise.ir/article_81504_b77e3913e142804243d76a299e710c11.pdf
2019-03-08T11:23:20
2020-09-26T11:23:20
66
80
Gas demand security index
natural gas export
Principal component analysis (PCA)
Pourya
Souri
p.souri96@aut.ac.ir
true
1
School of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran
School of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran
School of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Hadi
Sahebi
hadi_sahebi@iust.ac.ir
true
2
School of Industrial Engineering, Iran University of science Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science Technology, Tehran, Iran
AUTHOR
Alhajji, A. F. (2007) ‘The impact of Iran’s nuclear standoff on world energy security’, Energy & Environment. SAGE Publications Sage UK: London, England, 18(5), pp. 549–564.
1
Biresselioglu, M. E., Yelkenci, T. and Oz, I. O. (2015) ‘Investigating the natural gas supply security: A new perspective’, Energy. Elsevier, 80, pp. 168–176.
2
BP Statistical Review of World Energy June 2016 (2016) BP Statistical Review. Available at: https://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-full-report.pdf.
3
British Petroleum (BP). BP statistical review of world energy. 2002-2014. http://www.bp.com/statisticalreview. (2014). Available at: http://www.bp.com/statisticalreview.
4
Le Coq, C. and Paltseva, E. (2009) ‘Measuring the security of external energy supply in the European Union’, Energy Policy. Elsevier, 37(11), pp. 4474–4481.
5
Dastan, S. A. and Selcuk, O. (2016) ‘Review of the security of supply in Turkish energy markets: Lessons from the winter shortages’, Renewable and Sustainable Energy Reviews. Elsevier, 59, pp. 958–971.
6
Dike, J. C. (2013) ‘Measuring the security of energy exports demand in OPEC economies’, Energy policy. Elsevier, 60, pp. 594–600.
7
Energy Information Administration (EIA). (2005) International Energy Outlook, US Department of Energy, p. 37 /http://www.eia.doe.gov/oiaf/ieo/nat_gas.htmlS.
8
Erahman, Q. F., Purwanto, W. W., Sudibandriyo, M. and Hidayatno, A. (2016) ‘An assessment of Indonesia’s energy security index and comparison with seventy countries’, Energy. Elsevier, 111, pp. 364–376.
9
Gas 2017, Analysis and Forecasts to 2022 Market Report Series, Executive Summary (2017). Available at: https://www.iea.org/Textbase/npsum/gas2017MRSsum.pdf.
10
Gupta, E. (2008) ‘Oil vulnerability index of oil-importing countries’, Energy policy. Elsevier, 36(3), pp. 1195–1211.
11
Howell, L. D. (2011) ‘International country risk guide methodology’, East Syracuse, NY: PRS Group.
12
International Energy Agency (IEA). (2014) Natural Gas Information.
13
Jalilvand, D. R. (2013) Iran’s Gas Exports: Can Past Failure Become Future Success?. Oxford Institute for Energy Studies. Available at: http://www.oxfordenergy.org/wpcms/wp-content/uploads/2013/06/NG-78.pdf.
14
Koyama, K. and Kutani, I. (2012) ‘Study on the Development of an Energy Security Index and an Assessment of Energy Security for East Asian Countries’, Books. Economic Research Institute for ASEAN and East Asia (ERIA).
15
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