[1] S.D. Smith, G.S, Wood, M. Gould, A new earthworks estimating methodology, Construction Management and Economics 18 (2) (2000) 219-228.

[2] S.D. Smith, J.R. Obsborne, M.C. Forde, Analysis of Earth –moving Systems using discrete-event simulation, Journal of Civil Engineering and Management 121 (4) (1995) 388-396.

[3] D.G. Maritas, D.A. Xirocostas, The M/Ek/r machine interference model, European Journal of Operational Research 1 (2) (1977) 112-123.

[4] J. Mrozowicz, R. Szczepaniak, Optimization of selected cyclic-work technological systems in mass earthworks, in: Methods of designing civil structures and planning construction processes, Scientific Papers of the Institute of Building Engineering at Wroclaw University of Technology, Number 27, Wroclaw, 1978.

[5] O. Kapliński: Development and usefulness of planning techniques and decision making foundations on the example of construction enterprises in Poland. Technological and Economic Development of Economy XIV (4) (2008) 492-502.

Dear Nirmala S.V.S.G

thnk you for your helpful answers.

The Construction Industry Institute has several publications on this topic.

Check any publication of Salem et. al.

Maybe some more:

[1] E. Sakalauskas, E. K. Zavadskas, Optimization and intelligent decisions, technological and economic development of economy 2009 15(2) 189-196

[2] S.M. AbouRizk, D.W. Halpin, J.R. Wilson, Visual interactive fitting of beta distributions, Journal of Construction Enginnering and Management 117 (4) (1991) 589-605.

[3] S. Alkass, K. El-Moslmani, M. AlHussein, A computer model for selecting equipment for earthmoving operations using queuing theory, 20th International Conference on IT in Construction, (2003).

[4] H. Zhang, Multi-objective simulation-optimization for earthmoving operations, Automation in Construction 18 (1) (2008) 79-86.

[5] J.J. Shi, A neural network based system for predicting earthmoving production, Construction Management & Economics 17(4) (1999) 463-471.

[6] B. Hola, K.Schabowicz, Neural identification of earthmoving machinery’s productivity, Archives of Civil Engineering 53 (4) (2007) 697-711.

[7] K. Schabowicz, B. Hoła: Mathematical-neural model for assessing productivity of earthmoving machinery, Journal of Civil Engineering and Management 13 (1) (2007) 47-54.

[8] K. Schabowicz, B. Hoła: Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios, Archives of Civil and Mechanical Engineering 8 (4) (2008) 73-84.

[9] C.M. Tam, T.K.L. Tong, S.L. Tse, Artificial neural networks model for predicting excavator productivity, Journal of Engineering Construction and Architectural Management 9 (5-6) (2002) 446-452.

[10] J. Yang., D. Edwards., P.E.D. Love, A computational intelligent fuzzy model approach for excavator cycle time simulation, Automation in Construction 12 (6) (2003) 725-735.

[11] M. Marzouk, O. Moselhi, Simulation optimization for earthmoving operations using genetic algorithms, Construction Management & Economics 20 (6) (2002) 535.

[12] M. Marzouk, O. Moselhi, Multiobjective optimization of earthmoving operations, Journal of Construction Engineering and Management 130 (1) (2004) 105-113.

Construction Productivity Measurement and Improvement. James J. Adrian. Bradley University . Stipes Publishing L.L.C.. 2004.

https://www.amazon.com/Construction-Management-Estimators-Environmental-Engineering/dp/0849340373