Abstract

Manufacturing supply networks (MSNs) involve group decisions to achieve group goals and network decisions to achieve network goals. These decisions are made across multiple levels of a decision hierarchy. Resilience—the ability to maintain the satisfactory network functionality despite disruptions, is a vital network goal. When designing MSNs for resilience, the resilience and group goals often conflict, requiring simultaneous consideration of network and group decisions. Limited information in the early stages of MSN design necessitates focusing on design exploration. Hence, facilitating “co-design exploration”—a simultaneous exploration of network and group solution spaces, is crucial. Current approaches for designing MSNs for resilience do not support simultaneous consideration of network and group decisions. To bridge this gap, we present the co-design exploration of MSNs for resilience (CoDE-MR) framework to facilitate co-design exploration of the network and the groups. The CoDE-MR framework allows designers to model multilevel network and group decisions and their interactions, manage disruptions, and visualize and simultaneously explore the multilevel network and group solution spaces. In the framework, we integrate a combination of Preemptive and Archimedean formulations of the coupled-compromise decision support problem construct with resilience index metric and interpretable self-organizing map (iSOM)-based visualization to facilitate co-design exploration of MSNs for resilience. The framework's efficacy is demonstrated using a steel MSN problem, considering network and group decisions across two levels. The decision-centric framework is generic and well suited for the co-design exploration of multilevel systems to ensure resilience.

References

1.
Mistree
,
F.
,
Smith
,
W. F.
,
Bras
,
B. A.
,
Allen
,
J. K.
, and
Muster
,
D.
,
1990
, “
Decision-Based Design—A Contemporary Paradigm for Ship Design
,”
Trans. Soc. Nav. Archit. Mar. Eng.
,
98
, pp.
565
597
.
2.
Simon
,
H. A.
,
1947
,
Administrative Behavior
,
McMillan
,
New York
.
3.
Muster
,
D.
, and
Mistree
,
F.
,
1988
, “
The Decision Support Problem Technique in Engineering Design
,”
Int. J. Appl. Eng. Educ.
,
4
(
1
), pp.
23
33
.
4.
Mistree
,
F.
,
Hughes
,
O. F.
, and
Bras
,
B.
,
1993
, “Compromise Decision Support Problem and the Adaptive Linear Programming Algorithm,”
Structural Optimization: Status and Promise
,
M. P.
Kamat
, ed., AIAA, Washington, DC, pp.
247
286
.
5.
Simon
,
H. A.
,
1956
, “
Rational Choice and the Structure of the Environment
,”
Psychol. Rev.
,
63
(
2
), pp.
129
138
.
6.
Sharma
,
G.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2023
, “
Exploring Robust Decisions in the Design of Coupled Engineered Systems
,”
ASME J. Mech. Des.
,
145
(
12
), pp.
121402
35
.
7.
Pourmehdi
,
M.
,
Paydar
,
M. M.
, and
Asadi-Gangraj
,
E.
,
2020
, “
Scenario-Based Design of a Steel Sustainable Closed-Loop Supply Chain Network Considering Production Technology
,”
J. Cleaner Prod.
,
277
, p.
123298
.
8.
Jabbarzadeh
,
A.
,
Fahimnia
,
B.
, and
Sabouhi
,
F.
,
2018
, “
Resilient and Sustainable Supply Chain Design: Sustainability Analysis Under Disruption Risks
,”
Int. J. Prod. Res.
,
56
(
17
), pp.
5945
5968
.
9.
Rezapour
,
S.
,
Farahani
,
R. Z.
, and
Pourakbar
,
M.
,
2017
, “
Resilient Supply Chain Network Design Under Competition: A Case Study
,”
Eur. J. Oper. Res.
,
259
(
3
), pp.
1017
1035
.
10.
Martins
,
J. R. R. A.
, and
Lambe
,
A. B.
,
2013
, “
Multidisciplinary Design Optimization: A Survey of Architectures
,”
AIAA J.
,
51
(
9
), pp.
2049
2075
.
11.
Kim
,
H. M.
,
Rideout
,
D. G.
,
Papalambros
,
P. Y.
, and
Stein
,
J. L.
,
2003
, “
Analytical Target Cascading in Automotive Vehicle Design
,”
ASME J. Mech. Des.
,
125
(
3
), pp.
481
489
.
12.
Huang
,
Y.
,
Gao
,
K.
,
Wang
,
K.
,
Lv
,
H.
, and
Gao
,
F.
,
2022
, “
Analytical Target Cascading for Multilevel Supply Chain Decisions in Cloud Perspective
,”
Ind. Manag. Data Syst.
,
122
(
6
), pp.
1480
1498
.
13.
Huang
,
G. Q.
, and
Qu
,
T.
,
2008
, “
Extending Analytical Target Cascading for Optimal Configuration of Supply Chains With Alternative Autonomous Suppliers
,”
Int. J. Prod. Econ.
,
115
(
1
), pp.
39
54
.
14.
Shahan
,
D. W.
, and
Seepersad
,
C. C.
,
2012
, “
Bayesian Network Classifiers for Set-Based Collaborative Design
,”
ASME J. Mech. Des.
,
134
(
7
), p.
071001
.
15.
Khosrojerdi
,
A.
,
2015
, “
Resilient and Structurally Controllable Design of Multilevel Infrastructure Networks Under Disruptions
,”
Ph.D. dissertation
,
University of Oklahoma
,
Norman, OK
.
16.
Nellippallil
,
A. B.
,
Song
,
K. N.
,
Goh
,
C.-H.
,
Zagade
,
P.
,
Gautham
,
B. P.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2017
, “
A Goal-Oriented, Sequential, Inverse Design Method for the Horizontal Integration of a Multistage Hot Rod Rolling System
,”
ASME J. Mech. Des.
,
139
(
3
), p.
031403
.
17.
Sharma
,
G.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2021
, “
A Method for Robust Design in a Coupled Decision Environment
,”
Des. Sci.
,
7
, p.
e23
.
18.
Sharma
,
G.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2022
, “
Designing Concurrently and Hierarchically Coupled Engineered Systems
,”
Eng. Optim.
,
55
(
9
), pp.
1556
1576
.
19.
Sun
,
W.
,
Bocchini
,
P.
, and
Davison
,
B. D.
,
2020
, “
Resilience Metrics and Measurement Methods for Transportation Infrastructure: The State of the Art
,”
Sustainable Resilient Infrastruct.
,
5
(
3
), pp.
168
199
.
20.
Thole
,
S. P.
, and
Ramu
,
P.
,
2020
, “
Design Space Exploration and Optimization Using Self-Organizing Maps
,”
Struct. Multidiscipl. Optim.
,
62
(
3
), pp.
1071
1088
.
21.
Reddy
,
R.
,
Smith
,
W.F.
,
Mistree
,
F.
,
Bras
,
B. A.
,
Chen
,
W.
,
Malhotra
,
A.
,
Badhrinath
,
K.
,
Lautenschlager
,
U.
,
Pakala
,
R.
,
Vadde
,
S.
, and
Patel
,
P.
,
1996
, “
DSIDES User Manual
,” Realization Laboratory, Woodruff School of Mechanical Engineering, Georgia Institue of Technology, Atlanta, GA.
22.
Kohonen
,
T.
, and
Somervuo
,
P.
,
1998
, “
Self-Organizing Maps of Symbol Strings
,”
Neurocomputing
,
21
(
1
), pp.
19
30
.
23.
Sushil
,
R. R.
,
Baby
,
M.
,
Sharma
,
G.
,
Balu Nellippallil
,
A.
, and
Ramu
,
P.
,
2022
, “
Data Driven Integrated Design Space Exploration Using iSOM
,”
ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, Paper. No. DETC2022-89895.
24.
Baby
,
M.
,
Rama Sushil
,
R.
,
Ramu
,
P.
,
Allen
,
J. K.
,
Mistree
,
F.
, and
Nellippallil
,
A. B.
,
2023
, “
Robust, Co-Design Exploration of Multilevel Product, Material, and Manufacturing Process Systems
,”
Integr. Mater. Manuf. Innov.
,
13
(
1
), pp.
14
35
.
25.
Madhavan
,
N.
,
Brooks
,
G.
,
Rhamdhani
,
M. A.
, and
Bordignon
,
A.
,
2022
, “
Contribution of CO2 Emissions From Basic Oxygen Steelmaking Process
,”
Metals
,
12
(
5
), p.
797
.
26.
Ruth
,
M.
,
2004
, “Steel Production and Energy,”
Encyclopedia of Energy
,
C. J.
Cleveland
, ed.,
Elsevier
,
New York
, pp.
695
706
.
27.
Medina-González
,
S.
,
Papageorgiou
,
L. G.
, and
Dua
,
V.
,
2021
, “
A Reformulation Strategy for Mixed-Integer Linear Bi-Level Programming Problems
,”
Comput. Chem. Eng.
,
153
, p.
107409
.
You do not currently have access to this content.