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

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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.

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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).

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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
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  2. Xinlian Xie
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  3. Bofei Zhang
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  4. Wei Pan
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Correspondence to Xinlian Xie.

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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

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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

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