An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty

Azam Moradi; Jafar Razmi; Reza Babazadeh; Ali Sabbaghnia

doi:10.3934/jimo.2018074

Article Contents

2019, Volume 15, Issue 2: 855-879. Doi: 10.3934/jimo.2018074

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An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty

1.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2.
Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran

* Corresponding author: Tel.: +98 441 2972854; Fax: +98 441 2773591, Email address: r.babazadeh@urmia.ac.ir
* Corresponding author: Tel.: +98 441 2972854; Fax: +98 441 2773591, Email address: r.babazadeh@urmia.ac.ir

Received: January 2018

Revised: January 2018

Early access: June 2018

Published: January 2019

The authors are supported by INSF grant (Iran National Science Foundation).

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Abstract

The design of agile supply chain networks has attracted more attention in recent years according to the competitive business environment. Further, due to high degree of uncertainty in agile supply chains (SCs), developing robust and efficient decision making tools are of interest. In this study, an integrated approach based on principal component analysis (PCA) and multi-objective possibilistic mixed integer programming (MOPMIP) approaches is proposed to optimally design agile supply chain network under uncertainty. The PCA method is used for ranking and filtering the suppliers, constituting the first layer of the supply chain, based on agility criteria. The proposed MOPMIP model is employed to construct the agile supply chain network under uncertainty. In the proposed MOPMIP model, three objective functions including 1) total costs minimization, 2) total delivery time minimization and 3) maximization of flexibility are considered. An interactive fuzzy solution approach is used to solve the proposed MOPMILP model. Two numerical examples, is conducted to evaluate the performance and efficiency of the proposed integrated approach for agile supply chain network design under uncertainty.

Keywords:

Mathematics Subject Classification: Primary: 35Q90, 97N60, 65K05; Secondary: 90C70, 90C10, 90C29.

Citation:

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Figure 1. Here is the Caption of your figure

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Figure 2. Eigenvalue scree plot

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Figure 3. The Pareto optimal solutions for normalized values of first test problem

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Table 1. The primary defined input attributes for suppliers selection

Row	Input attribute	Row	Input attribute
1	Number of facilities	21	Working hours
2	Staff training	22	Bureaucratic
3	Education managers	23	Defective products
4	Standard simple mentation in organizations	24	Material requirements planning
5	In Stock	25	Distribution plan
6	Product price	26	The geographical location of the factory
7	Product variety	27	The geographical area covered
8	Transportation	28	The political situation in the regions covered
9	Waste	29	Infrastructure
10	Market share	30	After Sales Service
11	Career Opportunities	31	Technical Support
12	The use of new technology	32	Management
13	Production Volume	33	Response to Customer Request
14	Automation	34	E-commerce Capability
15	Communication System	35	JIT
16	Delivery	36	Packing Ability
17	Time of preparation	37	Position in the industry
18	Lot Size	38	Product appearance
19	Work in process (WIP)	39	Quality
20	Specialist operators

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Table 2. The most important agility criteria filtered through conducting brain storming meeting

Input indicators	Row
1	Specialist operators
2	The use of new technology
3	Material requirements planning
4	Distribution plan
5	Response to Customer Request
6	Technical Support
7	E-commerce Capability
8	Product variety
9	Production Volume
10	Transportation
11	After Sales Service
12	Automation
13	Communication System
14	JIT
15	Quality
16	The geographical area covered

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Table 3. The performance indicators of suppliers

Output indicators	Row
1	Product price
2	Bureaucratic
3	Delivery time
4	Work in Process (WIP)

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Table 4. Total Variance of components

Component	Initial Eigenvalue
Component	Total	Percentage of Variance	Cumulative Percentage
1	2.074	12.965	12.965
2	1.803	11.267	24.232
3	1.575	9.843	34.075
4	1.380	8.625	42.700
5	1.334	8.336	51.036
6	1.181	7.383	58.419
7	1.027	6.418	64.836
8	0.935	5.841	70.677
9	0.884	5.523	76.200
10	0.793	4.959	81.159
11	0.720	4.498	85.656
12	0.624	3.898	89.554
13	0.580	3.624	93.178
14	0.423	2.642	95.820
15	0.362	2.265	98.086
16	0.306	1.914	100.000

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Table 5. Total Variance of components

Component	Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Component	Total	Percentage of Variance	Cumulative Percentage	Total	Percentage of Variance	Cumulative Percentage
1	2.074	12.965	12.965	1.548	9.675	9.675
2	1.803	11.267	24.232	1.547	9.672	19.347
3	1.575	9.843	34.075	1.510	9.440	28.787
4	1.380	8.625	42.700	1.506	9.410	38.197
5	1.334	8.336	51.036	1.460	9.126	47.324
6	1.181	7.383	58.419	1.401	8.757	56.081
7	1.027	6.418	64.836	1.401	8.755	64.836

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Table 6. Rotated components

Variables	Components
Variables	1	2	3	4	5	6	7
X1	0.042	-0.192	0.615	-0.211	0.159	-0.273	-0.029
X2	-0.175	0.176	-0.015	-0.065	-0.709	-0.101	-0.061
X3	-0.083	-0.096	-0.145	0.479	-0.440	0.219	-0.067
X4	0.120	0.177	-0.220	0.609	0.221	-0.251	-0.108
X5	-0.208	-0.827	0.065	0.139	0.048	0.263	0.109
X6	-0.223	0.571	-0.207	0.099	0.256	0.182	0.418
X7	0.133	-0.134	0.205	0.753	-0.070	0.073	0.037
X8	-0.073	0.004	-0.090	-0.018	-0.016	-0.729	-0.123
X9	0.841	0.035	0.135	0.022	0.083	-0.011	0.014
X10	0.551	-0.016	-0.100	0.309	-0.035	0.370	-0.010
X11	-0.110	0.228	0.695	0.221	0.190	0.141	-0.097
X12	-0.258	0.541	0.155	0.034	-0.283	0.292	0.244
X13	0.175	-0.166	-0.147	-0.281	0.151	0.506	-0.591
X14	-0.177	0.115	-0.637	0.014	0.262	-0.176	-0.120
X15	-0.464	0.159	0.031	-0.086	0.629	-0.029	-0.048
X16	0.111	0.005	-0.030	-0.163	0.072	0.191	0.861

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Table 7. Suppliers scores driven out via PCA approach

Supplier	Score	Supplier	Score	Supplier	Score	Supplier	Score
1	-0.44143	16	-0.45093	31	-0.70673	46	0.22046
2	-0.39948	17	-0.16193	32	0.52357	47	0.646209
3	0.079517	18	0.41908	33	0.574339	48	-0.2407
4	-0.01854	19	-0.05046	34	0.054961	49	0.430716
5	0.06819	20	0.010264	35	-0.3108	50	0.175306
6	-0.16339	21	-0.29805	36	-0.29681	51	-0.12202
7	-0.47472	22	0.283236	37	0.379933	52	0.065461
8	-0.26409	23	-0.10214	38	0.083386	53	-0.80726
9	0.359434	24	-0.3874	39	-0.11785	54	-0.19505
10	-0.48728	25	0.278027	40	0.092893	55	0.258526
11	0.044161	26	0.196247	41	-0.19233	56	0.570286
12	0.50004	27	0.194767	42	0.121639	57	0.265066
13	1.064131	28	0.009783	43	-0.39538	58	0.098087
14	-0.72305	29	-0.08439	44	0.676464	59	-0.50213
15	-0.19635	30	0.266653	45	-0.02721	60	-0.39293

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Table 8. The sources of random generation of the most likely values

Parameter	Value	Parameter	Value
$\tilde fs_{st}$	$ \sim Uniform(1800000, 4300000)$	$\tilde Tr4_{idclt}$	$ \sim Uniform(80, 100)$
$\tilde fp_{pt}$	$ \sim Uniform(1700000, 4000000)$	$\tilde Cost_{mspt}$	$ \sim Uniform(20, 40)$
$\tilde fd_{dt}$	$ \sim Uniform(1600000, 3000000)$	$\tilde MI_{id}$	$ \sim Uniform(100, 120)$
$\tilde fse_{st}$	$ \sim Uniform(180000, 480000)$	$\tilde CPA1_{pft}$	$ \sim Uniform(50000, 60000)$
$\tilde fpe_{pt}$	$ \sim Uniform(170000, 400000)$	$\tilde CPA2_{djt}$	$ \sim Uniform(30000, 35000)$
$\tilde fde_{dt}$	$ \sim Uniform(160000, 300000)$	$\tilde CPA_{pdt}$	$ \sim Uniform(60000, 70000)$
$\tilde de_{ict}$	$ \sim Uniform(100, 200)$	$\tilde td_{pclt}$	$ \sim Uniform(16, 20)$
$\tilde vp_{ipt}$	$ \sim Uniform(120, 150)$	$\tilde tc_{dclt}$	$ \sim Uniform(2, 4)$
$\tilde Hd_{idt}$	$ \sim Uniform(10, 30)$	$\tilde te_{pdlt}$	$ \sim Uniform(10, 16)$
$\tilde Hp_{ipt}$	$ \sim Uniform(15, 40)$	$\tilde dis1_{pcl}$	$ \sim Uniform(200, 600)$
$\tilde Tr1_{isplt}$	$ \sim Uniform(80, 100)$	$\tilde dis2_{dcl}$	$ \sim Uniform(300, 500)$
$\tilde Tr2_{ipdlt}$	$ \sim Uniform(40, 60)$	$\tilde dis3_{pdl}$	$ \sim Uniform(100, 200)$
$\tilde Tr3_{ipclt}$	$ \sim Uniform(110, 200)$

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Table 9. The Pareto optimal solution for different values of importance coefficients

r1	r2	r3	Problem No.	Obj1	Obj2	Obj3	CPU time (Sec)
1	0	0	1	3.69E+11	1.38E+08	0.00E+00	134
1	0	0	2	3.82E+12	1.26E+09	0.00E+00	646
0	1	0	1	6.61E+12	2.44E+07	0.00E+00	164
0	1	0	2	2.50E+15	2.53E+08	0.00E+00	655
0	0	1	1	6.61E+12	1.38E+08	7.46E+07	132
0	0	1	2	2.50E+15	1.26E+09	3.74E+11	692
0.45	0.45	0.1	1	3.39E+12	7.89E+07	1.42E+07	210
0.45	0.45	0.1	2	1.13E+15	6.58E+08	8.33E+10	512
0.35	0.35	0.3	1	3.99E+12	9.30E+07	2.76E+07	167
0.35	0.35	0.3	2	1.30E+15	7.29E+08	1.46E+11	472
0.25	0.25	0.5	1	4.55E+12	1.06E+08	4.70E+07	173
0.25	0.25	0.5	2	1.58E+15	9.41E+08	2.31E+11	627
0.15	0.15	0.7	1	5.50E+12	1.16E+08	5.67E+07	159
0.15	0.15	0.7	2	1.80E+15	9.91E+08	3.07E+11	679
0.05	0.05	0.9	1	6.20E+12	1.25E+08	7.02E+07	134
0.05	0.05	0.9	2	2.25E+15	1.13E+09	3.48E+11	646
0.2	0.3	0.5	1	5.20E+12	9.83E+07	4.78E+07	164
0.2	0.3	0.5	2	1.68E+15	8.90E+08	2.25E+11	655
0.2	0.5	0.3	1	4.93E+12	7.21E+07	2.76E+07	132
0.2	0.5	0.3	2	1.70E+15	6.48E+08	1.23E+11	690
0.3	0.2	0.5	1	4.38E+12	1.13E+08	4.63E+07	210
0.3	0.2	0.5	2	1.53E+15	9.81E+08	2.31E+11	512
0.3	0.5	0.2	1	4.24E+12	6.65E+07	1.72E+07	167
0.3	0.5	0.2	2	1.53E+15	6.38E+08	8.61E+10	472
0.5	0.2	0.3	1	3.12E+12	1.08E+08	2.84E+07	173
0.5	0.2	0.3	2	9.27E+14	9.84E+08	1.12E+11	627
0.5	0.3	0.2	1	2.93E+12	9.59E+07	1.79E+07	159
0.5	0.3	0.2	2	9.47E+14	8.90E+08	9.36E+10	679

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Table 10. The studies mentioning the agility indicators in the literature

Index	Razmi et al. (2011)	Yauch (2011)	Ghodsypour and O'Brien (1998)	Min and Shin (2008)	Weber et al. (1991)	Abratt and Kleyn (2012)	Dickson (1996)	Prater et al. (2001)	Kassaee et al. (2014)	Dahmardeh et al. (2010)	Kumar et al. (2011)	Aktepe et al. (1999)	Lin (2009)	Chan and Thong (2009)	This study
1	*	*	*	*	*	*	*	*		*					*
2	*	*	*	*	*	*	*	*							*
3	*	*	*	*	*	*	*	*							*
4	*	*	*	*	*	*	*	*							*
5	*							*			*				*
6	*	*	*	*	*	*	*	*		*	*	*		*	*
7	*	*	*	*	*	*	*	*							*
8	*							*			*				*
9	*	*	*	*	*	*	*	*							*
10	*	*	*	*		*	*	*							*
11	*	*	*	*		*	*	*							*
12	*	*	*	*	*	*	*	*		*					*
13	*	*	*	*	*	*	*	*							*
14	*	*	*	*	*	*	*	*							*
15	*	*	*	*	*	*	*	*		*			*	*	*
16	*		*	*	*		*			*	*	*		*	*
17	*	*	*	*	*	*	*								*
18	*	*	*	*	*	*	*	*							*
19	*	*	*	*	*	*	*	*							*
20	*		*	*	*	*	*	*							*
21	*		*	*	*	*	*	*							*
22	*	*	*	*	*	*	*	*							*
23	*	*	*	*	*	*	*	*							*
24	*	*	*	*	*	*	*	*							*
25		*						*							*
26		*			*		*	*		*				*	*
27		*			*		*	*		*				*	*
28	*	*												*	*
29															*
30	*	*	*	*	*	*	*	*		*	*			*	*
31	*	*	*	*	*	*	*	*		*	*			*	*
32	*	*	*	*		*	*	*							*
33	*	*	*	*		*	*	*							*
34	*	*		*	*	*	*	*							*
35
36	*	*	*	*	*	*	*	*							*
37	*	*	*	*	*	*	*	*				*	*	*	*
38	*	*	*	*	*	*	*	*							*
39	*	*	*	*	*	*	*	*		*	*	*		*	*

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Table 11. Agile suppliers attributes

No.	In1	In2	In3	In4	In5	In6	In7	In8	In9	In10	In11	In12	In13	In14	In15	In16
1	0.54	0.82	0.5	0.62	0.75	0.45	0.62	0.64	0.71	0.6	0.5	0.57	0.52	0.35	0.23	0.6
2	0.5	0.04	0.63	0.63	0.95	0.46	0.6	0.76	0.4	0.77	0.5	0.6	0.8	0.53	0.38	0.47
3	0.27	0.66	0.72	0.97	0.93	0.48	0.78	0.12	0.37	0.78	0.68	0.63	0.8	0.35	0.12	0.33
4	0.43	0.34	0.71	0.75	0.92	0.56	0.72	0.44	0.53	0.68	0.64	0.68	0.72	0.37	0.32	0.3
5	0.72	0.13	0.54	0.73	0.68	0.5	0.52	0.65	0.71	0.79	0.62	0.61	0.54	0.55	0.19	0.35
6	0.71	0.45	0.81	0.52	0.9	0.52	0.55	0.02	0.31	0.72	0.7	0.65	0.7	0.34	0.1	0.34
7	0.5	0.43	0.89	0.94	0.85	0.41	0.58	0.5	0.58	0.61	0.63	0.53	0.76	0.52	0.76	0.34
8	0.34	0.77	0.74	0.55	0.74	0.42	0.7	0.06	0.8	0.76	0.4	0.63	0.79	0.44	0.24	0.49
9	0.52	0.29	0.86	0.57	0.73	0.62	0.59	0.31	0.71	0.71	0.65	0.65	0.77	0.46	0.74	0.69
10	0.39	0.58	0.65	0.76	0.67	0.66	0.62	0.4	0.21	0.61	0.42	0.6	0.52	0.56	0.94	0.36
11	0.4	0.53	0.97	0.81	0.96	0.47	0.76	0.17	0.69	0.77	0.42	0.74	0.67	0.24	0.24	0.51
12	0.58	0.07	0.75	0.54	0.65	0.6	0.53	0.1	0.86	0.72	0.63	0.74	0.7	0.3	0.06	0.55
13	0.4	0.12	0.62	0.87	0.62	0.7	0.73	0.04	0.62	0.79	0.6	0.8	0.79	0.21	0.59	0.64
14	0.34	0.81	0.87	0.86	0.63	0.67	0.52	0.94	0.2	0.73	0.49	0.52	0.77	0.5	0.22	0.32
15	0.8	0.73	0.89	0.5	0.8	0.59	0.58	0.94	0.26	0.74	0.65	0.62	0.57	0.1	0.44	0.52
16	0.62	0.27	0.57	0.89	1.00	0.44	0.7	0.67	0.2	0.62	0.44	0.56	0.59	0.41	0.86	0.65
17	0.26	0.01	0.86	0.71	0.91	0.51	0.54	0.61	0.37	0.8	0.42	0.64	0.65	0.29	0.37	0.66
18	0.33	0.63	0.96	0.93	0.61	0.65	0.73	0.23	0.44	0.71	0.7	0.64	0.54	0.52	0.17	0.63
19	0.5	0.54	0.91	0.86	0.86	0.53	0.74	0.61	0.65	0.71	0.57	0.61	0.74	0.55	0.94	0.48
21	0.33	0.89	0.68	0.62	0.81	0.59	0.51	0.38	0.51	0.75	0.51	0.77	0.79	0.41	0.86	0.35
22	0.61	0.4	0.56	0.54	0.83	0.7	0.53	0.37	0.63	0.7	0.56	0.64	0.58	0.33	0.97	0.67
23	0.68	0.84	0.98	0.9	0.88	0.44	0.69	0.03	0.6	0.74	0.43	0.59	0.72	0.14	0.25	0.61
24	0.41	0.81	0.81	0.95	0.73	0.46	0.77	0.66	0.5	0.68	0.47	0.63	0.69	0.23	0.31	0.3
25	0.27	0.44	0.63	0.55	0.94	0.69	0.72	0.18	0.28	0.74	0.61	0.71	0.58	0.21	0.00	0.59
26	0.28	0.37	0.53	0.55	0.85	0.47	0.68	0.33	0.75	0.78	0.46	0.65	0.67	0.18	0.35	0.7
27	0.64	0.79	0.63	0.7	0.89	0.45	0.57	0.15	0.69	0.77	0.66	0.71	0.65	0.25	0.55	0.52
28	0.31	0.89	0.99	0.62	0.67	0.6	0.79	0.25	0.38	0.72	0.51	0.8	0.54	0.39	0.11	0.55
29	0.6	0.02	0.76	0.73	0.64	0.7	0.5	0.67	0.53	0.64	0.42	0.79	0.62	0.45	0.99	0.36
30	0.42	0.23	0.76	0.9	0.81	0.66	0.79	0.96	0.69	0.78	0.56	0.51	0.76	0.2	0.51	0.36
31	0.8	0.48	0.8	0.51	0.83	0.43	0.66	0.84	0.3	0.78	0.48	0.58	0.69	0.48	0.12	0.31
32	0.58	0.05	0.66	0.6	0.73	0.61	0.73	0.23	0.87	0.74	0.49	0.67	0.77	0.32	0.03	0.68
33	0.57	0.21	0.86	0.71	0.64	0.54	0.78	0.45	0.55	0.74	0.68	0.77	0.56	0.31	0.99	0.41
34	0.76	0.63	0.65	0.93	0.66	0.44	0.68	0.99	0.75	0.72	0.52	0.8	0.5	0.2	0.5	0.38
35	0.28	0.04	0.71	0.67	0.72	0.43	0.57	0.44	0.72	0.71	0.45	0.5	0.75	0.41	0.29	0.47
36	0.45	0.75	0.87	0.7	0.62	0.41	0.57	0.92	0.63	0.73	0.62	0.78	0.67	0.34	0.36	0.35
37	0.73	0.1	0.75	0.98	0.73	0.55	0.56	0.34	0.75	0.69	0.62	0.52	0.73	0.23	0.61	0.57
38	0.75	0.2	0.75	0.72	0.95	0.51	0.67	0.72	0.46	0.72	0.66	0.66	0.75	0.49	0.91	0.58
39	0.8	0.98	0.52	0.86	0.86	0.68	0.5	0.31	0.69	0.65	0.63	0.64	0.76	0.17	0.27	0.43
41	0.33	0.44	0.84	0.84	0.68	0.7	0.58	0.98	0.37	0.6	0.5	0.78	0.54	0.52	0.27	0.7
42	0.66	0.97	0.59	0.7	0.78	0.59	0.77	0.72	0.44	0.68	0.67	0.71	0.66	0.16	0.34	0.54
43	0.32	0.72	0.75	0.58	0.73	0.58	0.56	0.26	0.21	0.62	0.51	0.66	0.78	0.28	0.62	0.62
44	0.52	0.17	0.56	0.99	0.76	0.65	0.77	0.59	0.46	0.73	0.67	0.67	0.5	0.41	0.87	0.56
45	0.59	0.53	0.8	0.78	0.93	0.46	0.71	0.07	0.78	0.66	0.52	0.59	0.68	0.16	0.71	0.36
46	0.46	0.01	0.77	0.77	0.99	0.5	0.71	0.76	0.9	0.78	0.59	0.6	0.79	0.48	0.46	0.54
47	0.79	0.07	0.82	0.68	0.81	0.59	0.74	0.3	0.64	0.75	0.65	0.62	0.72	0.12	0.41	0.6
48	0.41	0.18	0.64	0.59	0.79	0.46	0.73	0.89	0.76	0.64	0.62	0.54	0.58	0.53	0.22	0.41
49	0.45	0.09	0.53	0.86	0.65	0.64	0.74	0.27	0.28	0.77	0.62	0.58	0.71	0.46	0.84	0.4
50	0.4	0.49	0.91	0.62	0.88	0.58	0.75	0.39	0.35	0.71	0.65	0.78	0.64	0.13	0.39	0.44
51	0.66	0.47	0.71	0.61	0.67	0.57	0.66	0.02	0.31	0.6	0.61	0.75	0.76	0.51	0.69	0.35
52	0.62	0.42	0.81	0.54	0.93	0.54	0.52	0.11	0.22	0.69	0.68	0.79	0.62	0.35	0.55	0.69
53	0.7	0.93	0.66	0.52	0.9	0.45	0.57	0.65	0.31	0.64	0.56	0.58	0.8	0.23	0.25	0.43
54	0.25	0.91	0.82	0.99	0.63	0.65	0.58	0.88	0.36	0.76	0.46	0.78	0.64	0.52	0.17	0.62
55	0.28	0.37	0.52	0.55	0.69	0.6	0.65	0.63	0.28	0.63	0.7	0.72	0.62	0.15	0.88	0.67
56	0.64	0.42	1.00	1.00	0.91	0.69	0.71	0.11	0.89	0.77	0.51	0.62	0.56	0.42	0.21	0.63
57	0.49	0.63	0.91	0.82	0.84	0.54	0.8	0.01	0.53	0.79	0.62	0.56	0.52	0.48	0.19	0.49
58	0.49	0.97	0.56	0.7	0.6	0.66	0.58	0.26	0.82	0.8	0.42	0.61	0.77	0.46	0.59	0.56
59	0.71	0.09	0.62	0.5	0.79	0.52	0.67	0.59	0.21	0.66	0.44	0.51	0.73	0.29	0.6	0.4
60	0.27	0.06	0.97	0.68	0.98	0.59	0.58	0.25	0.2	0.69	0.4	0.75	0.68	0.54	0.72	0.49

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[1]	H. Abdi and L. J. Williams, Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2010), 433-459. doi: 10.1002/wics.101.
[2]	R. Abratt and N. Kleyn, Corporate identity, corporate branding and corporate reputations: Reconciliation and integration, European Journal of Marketing, 46 (2012), 1048-1063.
[3]	I. M. Ambe, Agile supply chain: Strategy for competitive advantage, Journal of Global Strategic Management, 4 (2010), 5-17. doi: 10.20460/JGSM.2010415835.
[4]	Y. Aït-Sahalia and D. Xiu, Principal component analysis of high frequency data, Journal of the American Statistical Association, 2017.
[5]	R. Babazadeh, J. Razmi and R. Ghodsi, Supply chain network design problem for a new market opportunity in an agile manufacturing system Journal of Industrial Engineering International, 8 (2012), 19pp. doi: 10.1186/2251-712X-8-19.
[6]	R. Babazadeh and J. Razmi, A robust stochastic programming approach for agile and responsive logistics under operational and disruption risks, International Journal of Logistics Systems and Management, 13 (2012), 458-482. doi: 10.1504/IJLSM.2012.050158.
[7]	M. Bachlaus, M. K. Pandey, C. Mahajan, R. Shankar and M. K. Tiwari, Designing an integrated multi-echelon agile supply chain network: A hybrid taguchi-particle swarm optimization approach Journal of Intelligent Manufacturing, 9 (2008), p747. doi: 10.1007/s10845-008-0125-1.
[8]	B. W. Bolch and C. Huang, Multivariate Statistical Methods for Business and Economics, Prentice-Hall, 1973.
[9]	M. J. Braunscheidel and N. C. Suresh, The organizational antecedents of a firm's supply chain agility for risk mitigation and response, Journal of Operations Management, 27 (2009), 119-140. doi: 10.1016/j.jom.2008.09.006.
[10]	T. A. Brown, Confirmatory Factor Analysis for Applied Research, Guilford Publications, 2014.
[11]	H. Carvalho, S. G. Azevedo and V. Cruz-Machado, Agile and resilient approaches to supply chain management: Influence on performance and competitiveness, Logistics Research, 4 (2012), 49-62. doi: 10.1007/s12159-012-0064-2.
[12]	J. Chai, J. N. Liu and E. W. Ngai, Application of decision-making techniques in supplier selection: A systematic review of literature, Expert Systems with Applications, 40 (2013), 3872-3885. doi: 10.1016/j.eswa.2012.12.040.
[13]	A. T. Chan, E. W. Ngai and K. K. Moon, The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry, European Journal of Operational Research, 259 (2017), 486-499. doi: 10.1016/j.ejor.2016.11.006.
[14]	M. Christopher, R. Lowson and H. Peck, Creating agile supply chains in the fashion industry, International Journal of Retail & Distribution Management, 32 (2004), 367-376.
[15]	M. Christopher, The agile supply chain: Competing in volatile markets, Industrial Marketing Management, 29 (2000), 37-44. doi: 10.1016/S0019-8501(99)00110-8.
[16]	M. Christopher, A. Harrison and R. van Hoek, Creating the agile supply chain: Issues and challenges, in Developments in Logistics and Supply Chain Management, Springer, (2016), 61–68.
[17]	N. Costantino, M. Dotoli, M. Falagario, M. P. Fanti and A. M. Mangini, A model for supply management of agile manufacturing supply chains, International Journal of Production Economics, 135 (2012), 451-457. doi: 10.1016/j.ijpe.2011.08.021.
[18]	L. De Boer, E. Labro and P. Morlacchi, A review of methods supporting supplier selection, European Journal of Purchasing & Supply Management, 7 (2001), 75-89.
[19]	G. W. Dickson, An analysis of vendor selection systems and decisions, European Journal of Marketing, 2 (1996), 5-17. doi: 10.1111/j.1745-493X.1966.tb00818.x.
[20]	D. Dubois and H. Prade, The mean value of a fuzzy number, Fuzzy Sets and Systems, 24 (1987), 279-300. doi: 10.1016/0165-0114(87)90028-5.
[21]	D. Dubois, E. Kerre, R. Mesiar and H. Prade, Fuzzy interval analysis, in Fundamentals of Fuzzy Sets, Springer, 2000, 483–558.
[22]	E. A. Elsayed, A. Shaik Dawood and R. Karthikeyan, Evaluating alternatives through the application of topsis method with entropy weight International Journal of Engineering Trends and Technology (IJETT), 46 (2017). doi: 10.14445/22315381/IJETT-V46P211.
[23]	H. Fargani, W. M. Cheung and R. Hasan, Ranking of factors that underlay the drivers of sustainable manufacturing based on their variation in a sample of UK manufacturing plants, International Journal of Manufacturing Technology and Management (IJMTM), (2017).
[24]	S. Fayezi, A. Zutshi and A. O'Loughlin, Understanding and development of supply chain agility and flexibility: A structured literature review, International Journal of Management Reviews, 19 (2017), 379-407. doi: 10.1111/ijmr.12096.
[25]	M. Fazli-Khalaf, A. Mirzazadeh and M. S. Pishvaee, A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network, Human and Ecological Risk Assessment: An International Journal, 23 (2017), 2119-2149. doi: 10.1080/10807039.2017.1367644.
[26]	P. Fortemps and M. Roubens, Ranking and defuzzification methods based on area compensation, Fuzzy Sets and Systems, 82 (1996), 319-330. doi: 10.1016/0165-0114(95)00273-1.
[27]	A. Ganguly, R. Nilchiani and J. V. Farr, Evaluating agility in corporate enterprises, International Journal of Production Economics, 118 (2009), 410-423. doi: 10.1016/j.ijpe.2008.12.009.
[28]	S. H. Ghodsypour and C. O'Brien, A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming, International Journal of Production Economics, 56/57 (1998), 199-212. doi: 10.1016/S0925-5273(97)00009-1.
[29]	N. Gholamian, I. Mahdavi, R. Tavakkoli-Moghaddam and N. Mahdavi-Amiri, Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty, Applied Soft Computing, 37 (2015), 585-607. doi: 10.1016/j.asoc.2015.08.041.
[30]	D. M. Gligor and M. C. Holcomb, Understanding the role of logistics capabilities in achieving supply chain agility: A systematic literature review, Supply Chain Management: An International Journal, 17 (2012), 438-453. doi: 10.1108/13598541211246594.
[31]	A. González, A study of the ranking function approach through mean values, Fuzzy Sets and Systems, 35 (1990), 29-41. doi: 10.1016/0165-0114(90)90016-Y.
[32]	D. Harrington, Confirmatory Factor Analysis, Oxford University Press, 2009. doi: 10.1093/acprof:oso/9780195339888.001.0001.
[33]	M. A. Hasan, J. Sarkis and R. Shankarr, Agility and production flow layouts: An analytical decision analysis, Computers & Industrial Engineering, 62 (2012), 898-907. doi: 10.1016/j.cie.2011.12.011.
[34]	A. Hasani, S. H. Zegordi and E. Nikbakhsh, Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty, International Journal of Production Research, 50 (2012), 4649-4669. doi: 10.1080/00207543.2011.625051.
[35]	S. Heilpern, The expected value of a fuzzy number, Fuzzy Sets and Systems, 47 (1992), 81-86. doi: 10.1016/0165-0114(92)90062-9.
[36]	F. R. Jacobs, R. B. Chase and R. R. Lummus, Operations and Supply Chain Management, McGraw-Hill/Irwin New York, 2014.
[37]	M. Jiménez, M. Arenas, A. Bilbao and M. V. Rodríguez, Linear programming with fuzzy parameters: An interactive method resolution, European Journal of Operational Research, 177 (2007), 1599-1609. doi: 10.1016/j.ejor.2005.10.002.
[38]	M. Jiménez, Ranking fuzzy numbers through the comparison of its expected intervals, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 4 (1996), 379-388. doi: 10.1142/S0218488596000226.
[39]	T. Jitpaiboon, The Roles of Information Systems Integration in the Supply Chain Integration Context-Firm Perspective, Ph. D thesis, University of Toledo, 2005.
[40]	I. T. Jolliffe and J. Cadima, Principal component analysis: A review and recent developments, Phil. Trans. R. Soc. A, 374 (2016), 20150202.
[41]	D. Kannan, Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process, International Journal of Production Economics, 195 (2018), 391-418. doi: 10.1016/j.ijpe.2017.02.020.
[42]	K. C. Lam, R. Tao and M. C. K. Lam, A material supplier selection model for property developers using fuzzy principal component analysis, Automation in Construction, 19 (2010), 608-618. doi: 10.1016/j.autcon.2010.02.007.
[43]	T. Mak and F. Nebebe, Factor analysis and methods of supplier selection, International Journal of Supply Chain Management, 5 (2016), 1-9.
[44]	A. W. Min and K. G. Shin, Exploiting multi-channel diversity in spectrum-agile networks, The 27th Conference on Computer Communications, IEEE, (2008), 1921–1929. doi: 10.1109/INFOCOM.2008.256.
[45]	K. Mukherjee, Modeling and optimization of traditional supplier selection, in Supplier Selection, Springer, 2017, 31–58. doi: 10.1007/978-81-322-3700-6_2.
[46]	D. J. Olive, Principal component analysis, in Robust Multivariate Analysis, Springer, 2017, 189–217. doi: 10.1007/978-3-319-68253-2_6.
[47]	F. Pan and R. Nagi, Robust supply chain design under uncertain demand in agile manufacturing, Computers & Industrial Engineering, 37 (2010), 668-683. doi: 10.1016/j.cor.2009.06.017.
[48]	F. Pan and R. Nagi, Multi-echelon supply chain network design in agile manufacturing, Omega, 41 (2013), 969-983. doi: 10.1016/j.omega.2012.12.004.
[49]	M. A. Parra, A. B. Terol, B. P. Gladish and M. V. R. Uría, Solving a multiobjective possibilistic problem through compromise programming, European Journal of Operational Research, 164 (2005), 748-759. doi: 10.1016/j.ejor.2003.11.028.
[50]	D. Peidro, J. Mula, M. Jiménez and M. del Mar Botella, A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment, European Journal of Operational Research, 205 (2010), 65-80. doi: 10.1016/j.ejor.2009.11.031.
[51]	A. Petroni and M. Braglia, Vendor selection using principal component analysis, Journal of Supply Chain Management, 36 (2000), 63-69. doi: 10.1111/j.1745-493X.2000.tb00078.x.
[52]	M. S. Pishvaee and J. Razmi, Environmental supply chain network design using multi-objective fuzzy mathematical programming, Applied Mathematical Modelling, 36 (2012), 3433-3446. doi: 10.1016/j.apm.2011.10.007.
[53]	M. S. Pishvaee and S. A. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty, Fuzzy Sets and Systems, 161 (2010), 2668-2683. doi: 10.1016/j.fss.2010.04.010.
[54]	L. L. Portes and L. A. Aguirre, Matrix formulation and singular-value decomposition algorithm for structured varimax rotation in multivariate singular spectrum analysis, Physical Review E, 93 (2016), 052216. doi: 10.1103/PhysRevE.93.052216.
[55]	J. Razmi, M. Seifoory and M. S. Pishvaee, A fuzzy multi-attribute decision making model for selecting the best supply chain strategy: Lean, agile or leagile, Journal of Industrial Engineering, 45 (2011), 127-142.
[56]	J. Razmi and A. Sabbaghnia, Tracing the impact of non-uniform forecasting methods on the severity of the bullwhip effect in two-and three-level supply chains, International Journal of Management Science and Engineering Management, 10 (2015), 297-304. doi: 10.1080/17509653.2015.1016132.
[57]	P. Rola, D. Kuchta and D. Kopczyk, Conceptual model of working space for agile (scrum) project team, Journal of Systems and Software, 118 (2016), 49-63. doi: 10.1016/j.jss.2016.04.071.
[58]	C. Rolland-Debord, S. Fry, J. Giovannelli, C. Langlois, N. Bricout, B. Aguilaniu, A. Bellocq, O. Le Rouzic, S. Dominique, A. Delobbe and G. François, Physiologic determinants of exercise capacity in pulmonary langerhans cell histiocytosis: A multidimensional analysis, PloS one, 12 (2017), e0170035.
[59]	M. R. G. Samani, S. A. Torabi and S. M. Hosseini-Motlagh, Integrated blood supply chain planning for disaster relief, International Journal of Disaster Risk Reduction, (2017).
[60]	J. Sarkis and S. Talluri, A model for strategic supplier selection, Journal of Supply Chain Management, 38 (2002), 18-28. doi: 10.1111/j.1745-493X.2002.tb00117.x.
[61]	M. R. Shaharudin, K. Govindan, S. Zailani, K. C. Tan and M. Iranmanesh, Product return management: Linking product returns, closed-loop supply chain activities and the effectiveness of the reverse supply chains, Journal of Cleaner Production, 149 (2017), 1144-1156. doi: 10.1016/j.jclepro.2017.02.133.
[62]	T. P. Shri and N. Sriraam, Comparison of t-test ranking with PCA and SEPCOR feature selection for wake and stage 1 sleep pattern recognition in multichannel electroencephalograms, Biomedical Signal Processing and Control, 31 (2017), 499-512.
[63]	Z. A. Sohi and S. A. Torabi, Integrated home video content procurement and distribution planning under uncertainty, Computers & Industrial Engineering, 106 (2017), 329-337.
[64]	H. Stadtler, Supply chain management: An overview in Supply Chain Management and Advanced Planning, Springer, Berlin, Heidelberg, 2015, 3–28.
[65]	S. Subhash, Applied Multivariate Techniques, John Wily & Sons Inc., Canada, 1996.
[66]	R. Suri, QRM and POLCA: A winning combination for manufacturing enterprises in the 21st century, Citeseer, 32 (2003).
[67]	D. R. Towill, Engineering the agile supply chain, Logistics Systems Dynamics Group, (2001), 377-396. doi: 10.1016/B978-008043567-1/50020-6.
[68]	C. A. Weber, J. R. Current and W. Bentonn, Vendor selection criteria and methods, European Journal of Operational Research, The 27th Conference on Computer Communications, 50 (1991), 2–18. doi: 10.1016/0377-2217(91)90033-R.
[69]	C. Wu and D. Barnes, Formulating partner selection criteria for agile supply chains: A Dempster-Shafer belief acceptability optimisation approach, International Journal of Production Economics, 125 (2010), 284-293. doi: 10.1016/j.ijpe.2010.02.010.
[70]	R. R. Yager, A procedure for ordering fuzzy subsets of the unit interval, Information Sciences, 24 (1981), 143-161. doi: 10.1016/0020-0255(81)90017-7.
[71]	C. A. Yauch, Measuring agility as a performance outcome, Journal of Manufacturing Technology Management, 22 (2011), 384-404. doi: 10.1108/17410381111112738.
[72]	F. You and I. E. Grossmann, Design of responsive supply chains under demand uncertainty, Computers & Industrial Engineering, 32 (2008), 3090-3111. doi: 10.1016/j.compchemeng.2008.05.004.
[73]	Y. Y. Yusuf, A. Gunasekaran, E. Adeleye and K. Sivayoganathan, Agile supply chain capabilities: Determinants of competitive objectives, European Journal of Operational Research, 159 (2004), 379-392. doi: 10.1016/j.ejor.2003.08.022.