A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

doi:10.23919/JSEE.2022.000130

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (4): 966-980.doi: 10.23919/JSEE.2022.000130

• • 上一篇

收稿日期:2021-09-14 接受日期:2022-07-25 出版日期:2023-08-18 发布日期:2023-08-28

A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

Jian’gang PENG¹(), Guang XIA²^,*(), Baoqun SUN²(), Shaojie WANG²()

¹ Intelligent Manufacturing Institute, Hefei University of Technology, Hefei 230009, China
² Institute of Automotive Engineering, Hefei University of Technology, Hefei 230009, China

Received:2021-09-14 Accepted:2022-07-25 Online:2023-08-18 Published:2023-08-28
Contact: Guang XIA E-mail:peng_jiangang@sina.com;xiaguang008@hfut.edu.cn;sbq1956@sina.com;wsj73@aliyun.con
About author:
PENG Jian’gang was born in 1970. He received his Ph.D. degree in industrial engineering at the School of Mechanical and Automotive Engineering in 2016, Hefei University of Technology, China. He is currently an associate research fellow. His main research interests include production planning and scheduling, uncertain linguistic terms, and fuzzy decision-making. E-mail: peng_jiangang@sina.com

XIA Guang was born in 1983. He received his Ph.D. degree in vehicle engineering from Hefei University of Technology. He is an associate research fellow in Hefei University of Technology. His research interest is vehicle dynamics control. E-mail: xiaguang008@hfut.edu.cn

SUN Baoqun was born in 1956. He received his B.S. degree from Hefei University of Technology. He is a researcher in Hefei University of Technology. His research interest is vehicle automatic transmission control. E-mail: sbq1956@sina.com

WANG Shaojie was born in 1973. He received his B.S. degree from Hefei University of Technology. He is a researcher in Hefei University of Technology. His research interest is vehicle automatic transmission control. E-mail: wsj73@aliyun.con
Supported by:
This work was supported by the National Key Research and Development Program of China (2016YFD0700605), the National Natural Science Foundation of China (51875151), and Hefei Municipal Natural Science Foundation (2021029).

摘要/Abstract

Abstract:

This paper presents an operational framework of unstructured decision-making approach involving quality function deployment (QFD) in an uncertain linguistic context. Firstly, QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment. Secondly, hesitant fuzzy linguistic term sets (HFLTSs), which facilitate the management and handling of information equivocality, are designed to construct a house of quality (HoQ) in the product planning process. The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets. Thirdly, a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization. The inter-relationships of cooperative partners are directly matched with a back propagation neural network (BPNN) to construct the multi-enterprise manufacturing network. The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives. Finally, a real-world example, namely, the prototype manufacturing of an automatic transmission for a vehicle, is provided to illustrate the effectiveness of the proposed decision-making approach.

Key words: decision-making, quality function deployment (QFD), uncertainty, fuzzy set

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 966-980.

Jian’gang PENG, Guang XIA, Baoqun SUN, Shaojie WANG. A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 966-980.

图/表 19

$\lambda $	DR₁	DR₂	DR₃	DR₄	DR₅	DR₆	DR₇	DR₈
$ \lambda $ =0.5	0.2348	0.1693	0.0428	0.1490	0.2348	0.0428	0.0428	0.0817
	0.0522	0.0358	0.1778	0.1606	0.1417	0.0358	0.0358	0.1606
	0.1100	0.0648	0.1759	0.1994	0.2439	0.2439	0.1994	0.2439
	0.0397	0.0584	0.0690	0.0890	0.0397	0.0198	0.0198	0.0785
$\lambda$ =1	0.2353	0.1711	0.0428	0.1497	0.2353	0.0428	0.0428	0.0856
	0.0537	0.0358	0.1790	0.1611	0.1432	0.0358	0.0358	0.1611
	0.1111	0.0667	0.1778	0.2000	0.2444	0.2444	0.2000	0.2444
	0.0397	0.0595	0.0694	0.0892	0.0397	0.0198	0.0198	0.0793
$ \lambda $ =2	0.2363	0.1746	0.0428	0.1512	0.2363	0.0428	0.0428	0.0924
	0.0566	0.0358	0.1814	0.1621	0.1462	0.0358	0.0358	0.1621
	0.1133	0.0703	0.1814	0.2012	0.2454	0.2454	0.2012	0.2454
	0.0397	0.0617	0.0701	0.0898	0.0397	0.0198	0.0198	0.0810
$ \lambda $ =3	0.2372	0.1780	0.0428	0.1527	0.2372	0.0428	0.0428	0.0979
	0.0591	0.0358	0.1837	0.1631	0.1490	0.0358	0.0358	0.1631
	0.1154	0.0734	0.1849	0.2024	0.2464	0.2464	0.2024	0.2464
	0.0397	0.0636	0.0708	0.0903	0.0397	0.0198	0.0198	0.0825
$ \lambda $ =4	0.2382	0.1810	0.0428	0.1541	0.2382	0.0428	0.0428	0.1023
	0.0611	0.0358	0.1858	0.1640	0.1515	0.0358	0.0358	0.1640
	0.1173	0.0759	0.1881	0.2036	0.2474	0.2474	0.2036	0.2474
	0.0397	0.0653	0.0714	0.0908	0.0397	0.0198	0.0198	0.0839
$ \lambda $ =5	0.2391	0.1838	0.0428	0.1554	0.2391	0.0428	0.0428	0.1057
	0.0627	0.0358	0.1879	0.1649	0.1538	0.0358	0.0358	0.1649
	0.1190	0.0779	0.1909	0.2047	0.2484	0.2484	0.2047	0.2484
	0.0397	0.0668	0.0721	0.0913	0.0397	0.0198	0.0198	0.0852
$ \lambda $ =6	0.2400	0.1863	0.0428	0.1567	0.2400	0.0428	0.0428	0.1084
	0.0640	0.0358	0.1897	0.1658	0.1559	0.0358	0.0358	0.1658
	0.1205	0.0794	0.1935	0.2058	0.2493	0.2493	0.2058	0.2493
	0.0397	0.0680	0.0726	0.0918	0.0397	0.0198	0.0198	0.0863
$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $
$ \lambda $ =10	0.2431	0.1937	0.0428	0.1605	0.2431	0.0428	0.0428	0.1152
	0.0668	0.0358	0.1957	0.1688	0.1621	0.0358	0.0358	0.1688
	0.1246	0.0829	0.2012	0.2095	0.2526	0.2526	0.2095	0.2526
	0.0397	0.0715	0.0744	0.0935	0.0397	0.0198	0.0198	0.0898

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Assessment	CR₁	CR₂	CR₃	CR₄
e₁	Bet(sh, ah)	Dom(sh)	Ind(me)	Ind(sh)
e₂	Dom(rh)	Ind(sh)	Non(rh)	Ind(rl)

Assessment	CR₁	CR₂	CR₃	CR₄
e₁	{s₆}	{s₅, s₆}	{s₃}	{s₄}
e₂	{s₆,s₇}	{s₆}	{s₄,s₅}	{s₃}

CR	v	$ \gamma $
CR₁	0.3248	0.4172
CR₂	0.2906	0.3265
CR₃	0.2051	0.1482
CR₄	0.1795	0.1081

CR	DR₁	DR₂	DR₃	DR₄	DR₅	DR₆	DR₇	DR₈
CR₁	Bet(me,ai)	Bet(ru,ri)	Non(ru)	Bet(ru,si)	Bet(me,ai)	Non(su)	Non(su)	Non(me)
CR₂	Non(su)	Non(su)	Bet(su,ai)	Bet(su,ri)	Bet(ru,ri)	Non(su)	Non(su)	Bet(su,ri)
CR₃	Bet(au,me)	Non(su)	Bet(ru,ri)	Bet(su,ri)	Bet(me,ai)	Bet(me,ai)	Bet(su,ri)	Bet(me,ai)
CR₄	Ind(su)	Bet(au,si)	Bet(ru,si)	Bet(su,ri)	Ind(au)	Non(ru)	Non(ru)	Bet(ru,ri)

CR	DR₁	DR₂	DR₃	DR₄	DR₅	DR₆	DR₇	DR₈
CR₁	{s₅,s₆}	{s₃,s₄,s₅}	{s₁}	{s₃,s₄}	{s₅,s₆}	{s₁,s₂}	{s₁,s₂}	{s₁,s₂,s₃}
CR₂	{s₁,s₂}	{s₁,s₂}	{s₄,s₅,s₆}	{s₄,s₅}	{s₃,s₄,s₅}	{s₁,s₂}	{s₁,s₂}	{s₄,s₅}
CR₃	{s₂,s₃}	{s₁,s₂}	{s₃,s₄,s₅}	{s₄,s₅}	{s₅,s₆}	{s₅,s₆}	{s₄,s₅}	{s₅,s₆}
CR₄	{s₂}	{s₂,s₃,s₄}	{s₃,s₄}	{s₄,s₅}	{s₂}	{s₁}	{s₁}	{s₃,s₄,s₅}

A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

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$ \lambda $	$ {D}_{1gd} $	$ {D}_{2gd} $	$ {D}_{3gd} $	$ {D}_{4gd} $	$ {D}_{5gd} $	$ {D}_{6gd} $	$ {D}_{7gd} $	$ {D}_{8gd} $
$ \lambda $ =0.5	0.7817	0.8359	0.7672	0.7010	0.6700	0.8288	0.8511	0.7176
$ \lambda $ =1	0.7801	0.8335	0.7655	0.7000	0.6687	0.8286	0.8508	0.7148
$ \lambda $ =2	0.7771	0.8288	0.7621	0.6978	0.6662	0.8281	0.8502	0.7095
$ \lambda $ =3	0.7743	0.8246	0.7589	0.6957	0.6639	0.8276	0.8496	0.7050
$ \lambda $ =4	0.7719	0.8210	0.7559	0.6937	0.6616	0.8271	0.8490	0.7012
$ \lambda $ =5	0.7698	0.8179	0.7532	0.6918	0.6595	0.8266	0.8484	0.6979
$ \lambda $ =6	0.7680	0.8152	0.7507	0.6899	0.6576	0.8262	0.8479	0.6951
$\vdots$	$\vdots$	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $
$ \lambda $ =10	0.7629	0.8080	0.7429	0.6839	0.6513	0.8245	0.8461	0.6869

λ	${{\rm{En}}}$ ₁	${{\rm{En}}}$ ₂	${{\rm{En}}}$ ₃	${{\rm{En}}}$ ₄	${{\rm{En}}}$ ₅	${{\rm{En}}}$ ₆	${{\rm{En}}}$ ₇	${{\rm{En}}}$ ₈
λ = 0.5	0.1182	0.0889	0.1260	0.1619	0.1787	0.0927	0.0806	0.1529
λ = 1	0.1183	0.0896	0.1262	0.1615	0.1783	0.0923	0.0803	0.1535
λ = 2	0.1186	0.0911	0.1265	0.1607	0.1775	0.0914	0.0797	0.1545
λ = 3	0.1188	0.0923	0.1269	0.1601	0.1769	0.0907	0.0792	0.1552
λ = 4	0.1189	0.0933	0.1272	0.1596	0.1764	0.0901	0.0787	0.1557
λ = 5	0.1190	0.0941	0.1276	0.1593	0.1760	0.0896	0.0783	0.1561
λ = 6	0.1190	0.0948	0.1279	0.1590	0.1756	0.0892	0.0780	0.1564
$\vdots$	$\vdots$	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $
λ = 10	0.1189	0.0963	0.1289	0.1586	0.1749	0.0880	0.0772	0.1571

Enterprise	Scale	Delivery date	Quality	Research/design capability	Advanced equipment	η
a₁	3	5	4	3	4	0.9728
a₂	4	3	4	4	5	1.0000
a₃	3	3	4	3	4	0.9559

Attribute	a₁	a₂	a₃
V	Dom(me)	Bet(su,si)	Ind(ai)
F	Bet(me,ri)	Ind(su)	Bet(me,ri)
D	Ind(si)	Non(ai)	Dom(me)
S	Bet(si,ai)	Dom(me)	Non(su)

Attribute	a₁	a₂	a₃	${E_{gd}}({H_\vartheta }) $
V	0.0833	0.0833	0.2500	0.7917
F	0.3333	0.1667	0.1111	0.6945
D	0.2500	0.2500	0.1667	0.6667
S	0.3333	0.0833	0.1667	0.7084