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Intelligent Techniques in Engineering Management pp 581–619Cite as

Intelligent Systems in Project Performance Measurement and Evaluation

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 87))

Abstract

Over the life cycle of a project, project costs and time estimations play important roles in baseline scheduling, schedule risk analysis and project control. Performance measurement is the ongoing, regular collection of information that can provide this controlling system. In this study, firstly, a new simulation approach is proposed to develop project progress time-series data, based on the complexity and specifications of the project as well as on the environment in which the project is executed. This simulator is capable of simulating fictitious projects, as well as real projects based on empirical data and helps project managers to monitor the project’s execution, despite the lack of historical data. Besides, this chapter compares the effects of different inputs on generated time series, as estimated results obtained on a fictitious dataset. Secondly, the validated outputs can provide researchers with an opportunity to generate general and customized formulae such as project completion time estimation. This study also implies four soft computing methods, Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interface System (ANFIS), Emotional Learning based Fuzzy Interface System (ELFIS) and Conventional Regression to forecast the completion time of project. Core variables in proposed model are known parameters in Earned Value Management (EVM). Finally, the result of using intelligent models and their performances in modeling the expert emotions are compared.

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Notes

  1. 1.

    Fuzzy Inference System.

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Author information

Authors and Affiliations

  1. Industrial Engineering Department, College of Engineering, University of Tehran, Tehran, Iran

    Seyed Hossein Iranmanesh

  2. Socio-Economic Systems Engineering, University of Tehran, Tehran, Iran

    Zahra Tavassoli Hojati

Authors
  1. Seyed Hossein Iranmanesh
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  2. Zahra Tavassoli Hojati
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Corresponding author

Correspondence to Seyed Hossein Iranmanesh .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University Management Faculty, Istanbul, Turkey

    Sezi Çevik Onar

Appendices

Appendix 23.1—A Sample Data for Proposed Models

Period

CPI

SPI

BCWS

BCWP

ACWP

AD

PD

ED

EAC (t)

1

0.955

0.970

24

23

24

76

64

74

66

2

0.969

0.920

93

85

88

76

64

70

69

3

0.993

0.860

134

115

116

76

64

65

74

4

1.080

0.821

196

161

149

76

64

62

78

5

1.111

0.790

261

206

186

76

64

60

81

6

1.064

0.738

348

257

241

76

64

56

86

7

1.020

0.749

428

320

314

76

64

57

85

8

1.056

0.712

578

412

390

76

64

54

89

9

1.057

0.784

699

548

518

76

64

60

81

10

1.049

0.718

913

655

625

76

64

55

89

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

\( \vdots \)

74

0.700

0.993

11669

11592

16560

76

64

75

59

75

0.696

0.999

11669

11658

16739

76

64

76

59

76

0.697

1.000

11669

11669

16739

76

64

76

59

Appendix 23.2—The Membership Functions Shapes Are Illustrated and Explained in the Following

2 types of sigmoidal shape are shown bellow:

The gaussian shape is illustrated bellow:

Π-shaped, generalized bell-shaped are as follows:

Trapezoidal-shaped and Triangular-shaped are illustrated bellow:

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Iranmanesh, S.H., Hojati, Z.T. (2015). Intelligent Systems in Project Performance Measurement and Evaluation. In: Kahraman, C., Çevik Onar, S. (eds) Intelligent Techniques in Engineering Management. Intelligent Systems Reference Library, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-319-17906-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-17906-3_23

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