Skip to main content
SpringerLink
Log in

A hybrid method for evaluating the effectiveness of giant systems with indicator correlations: an application for naval formation decision making in multiple scenarios

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This article discusses the giant system effectiveness evaluation (GSEE) problem with inevitable correlation in indicator systems due to their high specificity and complexity and proposes a hybrid method that is then applied to the naval formation decision-making process in multiple scenarios. The indicator correlation in a large-scale system will generate bias in its evaluation of effectiveness; the proposal that the lower the correlation is, the better the performance of the evidential reasoning approach (ERA) has been proven mathematically. In light of this proposition, a corollary was put forward: Fewer indicators would improve the precision of the result of the ERA application when considering the correlation. Considering that the giant system can be split into respective subsystems, which can then be analyzed by experts in their own fields, a hybrid method was developed for the GSEE problem based on the ERA and prospect theory. The core of the method is the construction of a nonlinear optimization model (NOM) aimed at minimizing the correlation and maximizing the evaluation ability of the prospect value of the indicator system. By constraint, the NOM also includes the optimized weight value of each indicator. For demonstration purposes, a naval formation operation effectiveness evaluation (NFOEE) was performed to assess the feasibility of the proposed method and the NOM. The results show that the proposed method can solve the NFOEE effectively and allow the decision maker to obtain useful information for naval formation-type decisions in multiple scenarios. Furthermore, the evaluation method is a general tool that can be applied to other GSEE problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alomair Y, Ahmad I, Alghamdi A (2015) A review of evaluation methods and techniques for simulation packages. Procedia Comput Sci 62:249–256

    Google Scholar 

  • Bao T, Xie X, Long P, Wei Z (2017) MADM method based on prospect theory and evidential reasoning approach with unknown attribute weights under intuitionistic fuzzy environment. Expert Syst Appl 88:305–317

    Google Scholar 

  • Cao J, Chen G, Khoveyni M, Eslami R, Yang G (2016) Specification of a performance indicator using the evidential-reasoning approach. Knowl Based Syst 92(C):138–150

    Google Scholar 

  • Chen X, Jing Y, Li C, Jiang N (2010) Effectiveness evaluation of warfare command systems with dissymmetrical warfare information. In: IEEE American control conference, pp 5556–5560

  • Chin KS, Fu C (2014) Integrated evidential reasoning approach in the presence of cardinal and ordinal preferences and its applications in software selection. Pergamon Press Inc, Oxford

    Google Scholar 

  • Chin KS, Fu C, Wang Y (2015) A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes. Pergamon Press Inc, Oxford

    Google Scholar 

  • Cho V (2007) A study of the impact of organizational learning on information system effectiveness. Int J Bus Inf 2(1):127–158

    Google Scholar 

  • Dubois D, Pap E, Prade H (2000) Hybrid probabilistic-possibilistic mixtures and utility functions. In: Fodor J, de Baets B, Perny P (eds) Preferences and Decisions under Incomplete Knowledge. Studies in Fuzziness and Soft Computing, vol 51. Springer, Heidelberg, pp 51–73

    MATH  Google Scholar 

  • Dubrova E (2013) Information redundancy. In: Dubrova E (ed) Fault-tolerant design. Springer, New York

    MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali A (2017) Precursor selection for sol–gel synthesis of titanium carbide nano powders by a new cubic fuzzy multi-attribute group decision-making model. J Intell Fuzzy Syst. https://doi.org/10.1515/jisys-2017-0083

    Article  Google Scholar 

  • Fahmi A, Fazli A, Saleem A, Asad A (2018a) Cubic fuzzy Einstein aggregation operators and its application to decision-making. Int J Syst Sci 49:1–13

    MathSciNet  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Sajjad Ali Khan M (2018b) Trapezoidal cubic fuzzy number einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 23:5753

    MATH  Google Scholar 

  • Fazli A, Fahmi A, Abdullah S, Ahmed A, Rehan A, Ghani F (2018) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34:2401–2416

    Google Scholar 

  • Grabisch M, Marichal JL, Mesiar R, Pap E (2009) Aggregation functions. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Grabisch M, Marichal JL, Mesiar R, Pap E (2011a) Aggregation functions: means. Inf Sci 181(1):1–22

    MathSciNet  MATH  Google Scholar 

  • Grabisch M, Marichal JL, Mesiar R, Pap E (2011b) Aggregation functions: construction methods, conjunctive, disjunctive and mixed classes. Inf Sci 181(1):23–43

    MathSciNet  MATH  Google Scholar 

  • Gul M, Celik E, Akyuz E (2017) A hybrid risk-based approach for maritime applications: the case of ballast tank maintenance. Hum Ecol Risk Assess 23(6):1389–1403

    Google Scholar 

  • Huang YY, Wang JY, Wang J, Dai YW (2009) An evaluation method of operational effectiveness for the emergency plans. In: IEEE informs international conference on service operations, logistics and informatics, pp 169–172

  • Huo JH, Zhang GH, Cai HL, Yuan L (2011) Research on an quantitative method for C^(4)ISR system effectiveness evaluation. Int Conf Intell Hum Mach Syst Cybern IEEE Comput Soc 2:23–26

    Google Scholar 

  • Hwang CL, Yoon KP (1981) Multiple attribute decision making: methods and applications. A state-of- the-art survey. Eur J Oper Res 4(4):287–288

    MATH  Google Scholar 

  • Jeong E, Oh C (2017) Evaluating the effectiveness of active vehicle safety systems. Accid Anal Prev 100:85–96

    Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–291

    MathSciNet  MATH  Google Scholar 

  • Kanada N, Morioka K, Sumiya Y, Yonemoto N, Shioji M, Kohmura A et al (2014) Evaluation by scale model experiments for aeronautical MIMO systems effectiveness of channel capacity evaluation in 5.1GHz band. In: IEEE antenna technology, pp 246–249

  • Kang J, Zhang J, Bai Y (2016) Modeling and evaluation of the oil-spill emergency response capability based on linguistic variables. Mar Pollut Bull 113(1–2):293–301

    Google Scholar 

  • Kong G, Xu DL, Yang JB, Ma X (2015) Combined medical quality assessment using the evidential reasoning approach. Expert Syst Appl 42(13):5522–5530

    Google Scholar 

  • Li J, Fu C, Chen Y, Yang K, Zhang X (2014) An operational efficiency evaluation method for weapon system-of-systems combat networks based on operation loop. In: IEEE international conference on system of systems engineering, pp 219–223

  • Liang H, Xiong W, Dong Y (2018) A prospect theory-based method for fusing the individual preference-approval structures in group decision making. Comput Ind Eng 117:237–248

    Google Scholar 

  • Liberatore MJ (1982) Book review of the analytic hierarchy process: planning, priority setting, resource allocation by Thomas L. Saaty. Am J Math Manag Sci 2(2):165–172

    Google Scholar 

  • Liu SL, Li H (2017) Modified ADC method and its application for weapon system effectiveness evaluation. J Natl Univ Def Technol 39(3):130–135

    Google Scholar 

  • Liu P, Jin F, Zhang X, Su Y, Wang M (2011) Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables. Knowl Based Syst 24(4):554–561

    Google Scholar 

  • Liu Y, Meng M, Zhang H, Yao J (2014) Effectiveness evaluation method of naval ship oriented to multi-mission base on MAS and FS. J Harbin Inst Technol 46(2):121–128

    Google Scholar 

  • Meng F, Wang N, Li B (2015) Selection method of evaluation indicators with three-parameter interval grey number. Open J Appl Sci 5(12):833–840

    Google Scholar 

  • Muñoz-Villamizar A, Santos J, Montoya-Torres JR, Jaca C (2018) Using oee to evaluate the effectiveness of urban freight transportation systems: a case study. Int J Prod Econ 197(C):232–242

    Google Scholar 

  • Ng CY (2016) An evidential reasoning-based AHP approach for the selection of environmentally-friendly designs. Environ Impact Assess Rev 61:1–7

    Google Scholar 

  • Pap E (1997) Pseudo-analysis as a mathematical base for soft computing. Soft Comput 1(2):61–68

    MathSciNet  Google Scholar 

  • Pap E (2002) Aggregation operators in the engineering design. In: Calvo T, Mayor G, Mesiar R (eds) Aggregation Operators. Studies in Fuzziness and Soft Computing, vol 97. Springer, Heidelberg, pp 195–223

    MATH  Google Scholar 

  • Qi ZF, Wang GS (2014) Grey synthetic relational analysis method-based effectiveness evaluation of ewcc system with incomplete information. Appl Mech Mater 638–640:2409–2412

    Google Scholar 

  • Rudas IJ, Pap E, Fodor J (2013) Information aggregation in intelligent systems: an application oriented approach. Knowl Based Syst 38:3–13

    Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York

    MATH  Google Scholar 

  • Stolzer AJ, Friend MA, Truong D, Tuccio WA, Aguiar M (2018) Measuring and evaluating safety management system effectiveness using data envelopment analysis. Saf Sci 104:55–69

    Google Scholar 

  • Wu QB, Ye FC, Wan XJ (2010) Study on measures of effectiveness of weapon system. Electron Prod Reliab Environ Test 28(4):27–31

    Google Scholar 

  • Wu W, Yan S, Feng R, Song D, Chen X (2017) Development of an environmental performance indicator framework to evaluate management effectiveness for jiaozhou bay coastal wetland special marine protected area, qingdao, China. Ocean Coast Manag 142:71–89

    Google Scholar 

  • Wu Y, Xu C, Zhang T (2018) Evaluation of renewable power sources using a fuzzy mcdm based on cumulative prospect theory: a case in china. Energy 147:1227–1239

    Google Scholar 

  • Xu Y, Wen X, Zhang W (2018) A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Comput Ind Eng 116:113–129

    Google Scholar 

  • Yanyan H (2009) A methodology of simulation and evaluation on the operational effectiveness of weapon equipment. IEEE, Guilin, China, pp 131–136

    Google Scholar 

  • Yang JB, Singh MG (1994) An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Trans Syst Man Cybern 24(1):1–18

    Google Scholar 

  • Yu J, Dai R (1993) A new discipline of science: the study of open complex giant system and its methodology. J Syst Eng Electron 4(2):2–12

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  • Zhao X, Chen G, Yue Q (2011) Equipment support quality management effectiveness evaluation based on fce method. Procedia Eng 15(1):4377–4381

    Google Scholar 

Download references

Acknowledgements

The authors are very grateful to the anonymous reviewers and editor for their very valuable comments and suggestions, which were greatly helpful in revising the manuscript. This work is supported by National Key R&D Program of China (Grant No. 2017YFC0805309) and the Fundamental Research Funds for the Central Universities (Logistics Research Institute, Dalian Maritime University Grant No. 3132019303).

Author information

Authors and Affiliations

  1. Integrated Transport Institute, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Xiaowei Xu, Xinlian Xie, Bofei Zhang & Wei Pan

Authors
  1. Xiaowei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinlian Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bofei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinlian Xie.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Nomenclature in tables

Appendix: Nomenclature in tables

EB

Evaluation objective

SSC

Saturation strike capability

OE

Operation effectiveness

SMC

Strike means completeness

FGEI

First-grade evaluation indicator

IntR

Interference ratio

SGEI

Second-grade evaluation indicator

IntD

Interference distance

TGEI

Third-grade evaluation indicator

IntT

Interference time

AC

Attack capability

EDR

Equipment damage rate

DetC

Detection capability

NumE

Number of equipment

DefC

Defense capability

Adv

Advancement

InfC

Information capability

DetCo

Detection coverage

SupC

Support capability

Rel

Reliability

ManC

Maneuvering capability

TraC

Trace capability

FAC

Firepower attack capability

ResP

Resolving power

ECM

Electronic countermeasure

DPT

Defense preparation time

DE

Detection equipment

SucR

Success rate

ADC

Active detection capability

DOD

Distance of defense

PDC

Passive detection capability

AAIC

Antiactive interference capability

AAC

Antifire power attack capability

APIC

Antipassive interference capability

AIC

Antielectronic interference capability

Tim

Timeliness

ISC

Information sharing capability

Val

Validity

IMC

Information management capability

Sat

Safety

DSC

Decision support capability

MPC

Maximum processing capacity

MaiC

Maintenance capability

SGT

Scheme generation time

RepC

Replenishment capability

SAR

Scheme adoption rate

HeaS

Health support

MainL

Maintenance level

VesT

Vessel tonnage

MainR

Maintenance rate

ManS

Maneuvering speed

RepCY

Replenishment cycle

TTSA

Take off time of shipboard aircraft

Con

Convenience

ACC

Accuracy

BFR

The battle field rescue

HeaC

Health care

Scen

Scenario

Eff

Effectiveness

Unc

Uncertainty

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, X., Xie, X., Zhang, B. et al. A hybrid method for evaluating the effectiveness of giant systems with indicator correlations: an application for naval formation decision making in multiple scenarios. Soft Comput 24, 4295–4306 (2020). https://doi.org/10.1007/s00500-019-04194-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04194-x

Keywords

Search

Navigation