An application of game theory in distributed collaborative decision making

doi:10.1007/s11465-019-0523-4

Front. Mech. Eng.

2019, Vol. 14

Issue (1) : 85-101 https://doi.org/10.1007/s11465-019-0523-4

RESEARCH ARTICLE

An application of game theory in distributed collaborative decision making

Angran XIAO(

)

Department Mechanical Engineering and Industrial Design Technology, New York City College of Technology, City University of New York, Brooklyn, NY 11201, USA

Download: PDF(725 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In a distributed product realization environment, new paradigms and accompanying software systems are necessary to support the collaborative work of geographically dispersed engineering teams from different disciplines who have different knowledge, experience, tools and resources. To verify the concept of collaboration by separation, we propose a generic information communication medium to enable knowledge representation and exchange between engineering teams, a digital interface. Across digital interfaces, each engineering team maintains its own perspective towards the product realization problem, and each controls a subset of design variables and seeks to maximize its own payoff function subject to individual constraints. Hence, we postulate the use of principles from game theory to model the relationships between engineering teams and facilitate collaborative decision making without causing unnecessary information exchange or iteration across digital interfaces. A product design and manufacturing scenario is introduced to demonstrate the efficacy of using game theory to maintain a clean interface between design and manufacturing teams.

Keywords collaboration distributed product realization game theory digital interface

Corresponding Author(s): Angran XIAO

Just Accepted Date: 13 June 2018 Online First Date: 26 July 2018 Issue Date: 30 November 2018

Cite this article:

Angran XIAO. An application of game theory in distributed collaborative decision making[J]. Front. Mech. Eng., 2019, 14(1): 85-101.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0523-4
https://academic.hep.com.cn/fme/EN/Y2019/V14/I1/85

Fig.1 Mathematical formulation of the compromise DSP [²³]

Fig.2 Design-manufacturing interfaces

Fig.3 Solution of concurrent compromise DSPs

Fig.4 Solution of sequential compromise DSPs

Fig.5 Game solution of coupled compromise DSPs

Fig.6 Solving a leader/follower game

Fig.7 Light switch cover plate and the snap fit

Fig.8 The design player’s compromise DSP

Fig.9 Finite element analysis of cover plate

Fig.10 The manufacturing player’s Compromise DSP

Fig.11 Solution process for games (manuf.: Manufacturing)

Tab.1 Results from the Traditional Trial-and-Error Approach

Tab.2 Results from fully cooperative protocol

Tab.3 Experimental Results of Noncooperative Games

Tab.4 Results from a non-cooperative protocol

Fig.12 Response surface model of HOC, a and t

Fig.13 Deviation values of goals

Fig.14 Deviation values of design and manufacturing

1	Li W D, Lu W F, Fuh J Y H, et al. Collaborative computer-aided design—Research and development status. Computer Aided Design, 2005, 37(9): 931–940 https://doi.org/10.1016/j.cad.2004.09.020
2	Wang L, Shen W, Xie H, et al. Collaborative conceptual design—State of the art and future trends. Computer Aided Design, 2002, 34(13): 981–996 https://doi.org/10.1016/S0010-4485(01)00157-9
3	Rosen D, Chen Y, Gerhard J, et al. Design decision templates and their implementation for distributed design and solid freeform fabrication. In: Proceedings of ASME Computers and Information in Engineering Conference. Baltimore: ASME, 2000
4	Chao P, Wang Y. A data exchange framework for networked CAD/CAM. Computers in Industry, 2001, 44(2): 131–140 https://doi.org/10.1016/S0166-3615(00)00082-8
5	Wu D, Rosen D W, Wang L, et al. Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer Aided Design, 2015, 59: 1–14 https://doi.org/10.1016/j.cad.2014.07.006
6	Whitfield R I, Duffy A H B, Gatchell S, et al. A collaborative platform for integrating and optimizing computational fluid dynamics analysis requests. Computer Aided Design, 2012, 44(3): 224–240 https://doi.org/10.1016/j.cad.2011.04.004
7	Eynard B, Liénard S, Charles S, et al. Web-based collaborative engineering support system: Applications in mechanical design and structural analysis. Concurrent Engineering, Research and Applications, 2005, 13(2): 145–153 https://doi.org/10.1177/1063293X05053799
8	Kleiner S, Anderl R, Gräb R. A collaborative design system for product data integration. Journal of Engineering Design, 2003, 14(4): 421–428 https://doi.org/10.1080/09544820310001606902
9	Schlenoff C, Gruninger M, Creek T, et al. The essence of the process specification language. Transactions of the Society for Computer Simulation, 1999, 16(4): 204–216
10	Whitfield R, Duffy A H, Coates G, et al. Distributed design coordination. Research in Engineering Design, 2002, 13(4): 243–252 https://doi.org/10.1007/s00163-002-0021-1
11	Whitfield R I, Duffy A H B, Boyle I, et al. An integrated environment for organisational decision support. Computers in Industry, 2011, 62(8–9): 842–853 https://doi.org/10.1016/j.compind.2011.09.002
12	Whitfield R I, Duffy A H B, York P, et al. Managing the exchange of engineering product data to support through life ship design. Computer Aided Design, 2011, 43(5): 516–532 https://doi.org/10.1016/j.cad.2010.12.002
13	Hoffmann C M, Joan-Arinyo R. Distributed maintenance of multiple product views. Computer Aided Design, 2000, 32(7): 421–431 https://doi.org/10.1016/S0010-4485(00)00023-3
14	Hoffman C M, Joan-Arinyo R. CAD and the product master model. Computer Aided Design, 1998, 30(11): 905–918 https://doi.org/10.1016/S0010-4485(98)00047-5
15	Badin J, Monticolo D, Chamoret D, et al. Using the knowledge configuration model to manage knowledge in configuration for upstream phases of the design process. International Journal on Interactive Design and Manufacturing, 2011, 5: 171 https://doi.org/10.1007/s12008-011-0128-6
16	Belkadi F, Dremont N, Notin A, et al. A meta-modelling framework for knowledge consistency in collaborative design. Annual Reviews in Control, 2012, 36(2): 346–358 https://doi.org/10.1016/j.arcontrol.2012.09.016
17	Bracewell R, Wallace K, Moss M, et al. Capturing design rationale. Computer Aided Design, 2009, 41(3): 173–186 https://doi.org/10.1016/j.cad.2008.10.005
18	Peng G, Wang H, Zhang H, et al. A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model. Advanced Engineering Informatics, 2017, 33: 314–329 https://doi.org/10.1016/j.aei.2016.12.007
19	Chandrasegaran S K, Ramani K, Sriram R D, et al. The evolution, challenges, and future of knowledge representation in product design systems. Computer Aided Design, 2013, 45(2): 204–228 https://doi.org/10.1016/j.cad.2012.08.006
20	Mistree F, Smith W F, Bras B, et al. Decision-based design: A contemporary paradigm for ship design. Society of Naval Architects and Marine Engineers, 1990, 98: 565–597
21	McMahon C, Liu Y, McAdams D, et al. Ontology-based executable design decision template representation and reuse. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2016, 30(4): 390–405 https://doi.org/10.1017/S0890060416000378
22	Nellippallil A B, Song K N, Goh C H, et al. A goal-oriented, sequential, inverse design method for the horizontal integration of a multistage hot rod rolling system. Journal of Mechanical Design, 2017, 139(3): 031403 https://doi.org/10.1115/1.4035555
23	Vadde S, Allen J K, Mistree F. Compromise decision support problems for hierarchical design involving uncertainty. Computers & Structures, 1994, 52(4): 645–658 https://doi.org/10.1016/0045-7949(94)90346-8
24	Hu Y, Rao S S. Game-theory approach for multi-objective optimal design of stationary flat-plate solar collectors. Engineering Optimization, 2009, 41(11): 1017–1035 https://doi.org/10.1080/03052150902890064
25	Dhingra A K, Rao S S. A cooperative fuzzy game theoretic approach to multiple objective design optimization. European Journal of Operational Research, 1995, 83(3): 547–567 https://doi.org/10.1016/0377-2217(93)E0324-Q
26	Sunar M, Rao S S. Simultaneous passive and active control design of structures using multiobjective optimization strategies. Computers & Structures, 1993, 48(5): 913–924 https://doi.org/10.1016/0045-7949(93)90513-D
27	Rao S S. Game theory approach for multiobjective structural optimization. Computers & Structures, 1987, 25(1): 119–127 https://doi.org/10.1016/0045-7949(87)90223-9
28	Badhrinath K, Jagannatha Rao J R. Modeling for concurrent design using game theory formulations. Concurrent Engineering, 1996, 4(4): 389–399
29	Takai S. A game-theoretic model of collaboration in engineering design. Journal of Mechanical Design, 2010, 132(5): 051005 https://doi.org/10.1115/1.4001205
30	Xiao M, Shao X, Gao L, et al. A new methodology for multi-objective multidisciplinary design optimization problems based on game theory. Expert Systems with Applications, 2015, 42(3): 1602–1612 https://doi.org/10.1016/j.eswa.2014.09.047
31	Chen L, Li S. A computerized team approach for concurrent product and process design optimization. Computer Aided Design, 2002, 34(1): 57–69 https://doi.org/10.1016/S0010-4485(01)00047-1
32	Ge Z, Hu Q. Collaboration in R&D activities: Firm-specific decisions. European Journal of Operational Research, 2008, 185(2): 864–883 https://doi.org/10.1016/j.ejor.2007.01.020
33	Lewis K, Mistree F. Collaborative, sequential and isolated decisions in design. ASME journal of Mechanical Engineering, 1998, 120(4): 643–652
34	Lewis K, Mistree F. Modeling interactions in multidisciplinary design: A game theoretic approach. AIAA Journal, 1997, 35(8): 1387–1392 https://doi.org/10.2514/2.248
35	Montgomery D C. Design and Analysis of Experiments. 8th ed. Wiley, 2012
36	Sambu S. A design for manufacture method for rapid prototyping and rapid tooling. Thesis for the Master’s Degree. Atlanta: Georgia Institute of Technology, 2001
37	Gao W, Zhang Y, Ramanujan D, et al. The status, challenges, and future of additive manufacturing in engineering. Computer Aided Design, 2015, 69: 65–89 https://doi.org/10.1016/j.cad.2015.04.001

[1]	Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN. Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis[J]. Front. Mech. Eng., 2019, 14(4): 412-421.
[2]	ZHANG Xiaodong, LI Yingzi, ZHANG Zhiqiang. Study on affecting factors of collaborative product development based on collaboration hierarchy model[J]. Front. Mech. Eng., 2007, 2(2): 210-213.

Viewed

Full text

Abstract

Cited

Shared

Discussed