Utilizing simulation derived quantitative formulas for accurate excavator hauler fleet selection

Discrete event simulation (DES) produces models of greater granularity and higher accuracy in analysis of heavy construction operations than classic quantitative techniques; specifically utilizing average production rates for determining the fleet required for and duration of earthmoving operations. Nonetheless, the application of DES is not readily applied beyond academic work for high level analysis in the heavy construction industry. Field level planners default to the use of average production rates, which can be easily applied with simple spreadsheet tools and allows quick recalculations to be performed when existing input data is changed or more data becomes available. To aid in fleet selection and determination of the duration of site grading earthworks operations where one fleet is applied, this research presents a new approach by developing quantitative formulas from DES analysis. The approach simplifies DES application and reduces the barrier to access simulation-generalized and field-applicable knowledge, while providing greater accuracy than simply relying on average production rates.

[1]  Osama Moselhi,et al.  Optimization of Earthmoving Operations in Heavy Civil Engineering Projects , 2009 .

[2]  AlshibaniAdel,et al.  Fleet selection for earthmoving projects using optimization-based simulation , 2012 .

[3]  Mohamed Marzouk,et al.  Simulation optimization for earthmoving operations using genetic algorithms , 2002 .

[4]  Mohamed Marzouk,et al.  Multiobjective Optimization of Earthmoving Operations , 2004 .

[5]  Hong Zhang.  Multi-objective simulation-optimization for earthmoving operations , 2008 .

[6]  Ali Touran,et al.  Optimum fleet size determination by queuing and simulation , 1988 .

[7]  Mohamed Marzouk,et al.  Bid preparation for earthmoving operations , 2002 .

[8]  Julio C. Martínez,et al.  EarthMover-simulation tool for earthwork planning , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[9]  Sangyoub Lee,et al.  Production prediction of conventional and global positioning system–based earthmoving systems using simulation and multiple regression analysis , 2008 .

[10]  Mohamed Marzouk,et al.  Constraint-based genetic algorithm for earthmoving fleet selection , 2003 .

[11]  Simon Smith,et al.  Earthmoving productivity estimation using linear regression techniques , 1999 .

[12]  Yong Bai,et al.  A Petri net simulation model for virtual construction of earthmoving operations , 2011 .

[13]  Jingsheng Shi,et al.  An Automated Modeling System for Simulating Earthmoving Operations , 1998 .

[14]  Douglas D. Gransberg.  Optimizing haul unit size and number based on loading facility characteristics , 1996 .

[15]  Mohamed Marzouk,et al.  Object-oriented Simulation Model for Earthmoving Operations , 2003 .

[16]  Jonathan Jingsheng Shi,et al.  A neural network based system for predicting earthmoving production , 1999 .

[17]  Krzysztof Schabowicz,et al.  MATHEMATICAL-NEURAL MODEL FOR ASSESSING PRODUCTIVITY OF EARTHMOVING MACHINERY , 2007 .

[18]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[19]  John Christian,et al.  Improving earthmoving estimating by more realistic knowledge , 1996 .

[20]  Simaan M. AbouRizk,et al.  AP2-Earth: a simulation based system for the estimating and planning of earth moving operations , 1997, WSC '97.

[21]  Simaan M. AbouRizk,et al.  A resource based simulation approach with application in earthmoving/strip mining , 1994, Proceedings of Winter Simulation Conference.