An Economical Operation Analysis Optimization Model for Heavy Equipment Selection
Optimizing equipment selection in heavy earthwork
operations is a critical key in the success of any construction project.
The objective of this research incentive was geared towards
developing a computer model to assist contractors and construction
managers in estimating the cost of heavy earthwork operations.
Economical operation analysis was conducted for an equipment fleet
taking into consideration the owning and operating costs involved in
earthwork operations. The model is being developed in a Microsoft
environment and is capable of being integrated with other estimating
and optimization models. In this study, Caterpillar® Performance
Handbook [5] was the main resource used to obtain specifications of
selected equipment. The implementation of the model shall give
optimum selection of equipment fleet not only based on cost
effectiveness but also in terms of versatility. To validate the model, a
case study of an actual dam construction project was selected to
quantify its degree of accuracy.
[1] Alkass, S., & Harris, F. (1988). Expert system for earthmoving
equipment selection in road construction. Journal of Construction
Engineering and Management , 114 (3), 426-440.
[2] Amirkhanian, S., & Baker, N. (1992). Expert system for equipment
selection for earthmoving operations. Journal of Construction
Engineering and Management , 118 (2), 318-331.
[3] Anderson, D., Sweeney, D., Williams, T., & Martin, K. (2008). An
introduction to management science quantitative approaches to decision
making. Mason,OH: Thomson Higher Education.
[4] Belegundu, A., & Chandrupatla, T. (2002). Optimization concepts and
applications in engineering. Delhi: Pearson Education.
[5] Caterpillar® (2011). Caterpillar performance handbook. Peoria:
Caterpillar.
[6] Day, D., & Benjamin, N. (1991). Construction equipment guide. New
York: John Wiley & Sons.
[7] Gransberg, D., Popescu, C., & Ryan, R. (2006). Construction equipment
management for engineers, estimators, and owners. Boca Raton, FL:
Taylor & Francis Group.
[8] Haidar, A., Naoum, S., Howes, R., & Tah, J. (1999). Genetic algorithms
application and testing for equipment selection. Journal of Construction
Engineering and Management , 125 (1), 32-38.
[9] Marzouk, M., & Moselhi, O. (2003). Object-oriented simulation model
for earthmoving operations. Journal of Construction Engineering and
Management , 129 (2), 173-181.
[10] Marzouk, M., & Moselhi, O. (2004). Multiobjective optimization of
earthmoving operations. Journal of Construction Engineering and
Management , 130 (1), 105-113.
[11] Moselhi, O., & Marzouk, M. (2000). Automated system for cost estimating of earthmoving operations. Proceedings of the 17th International Symposium on Automation and Robotics in Construction
(ISARC), Taipei, Taiwan,, 1053-1058.
[12] Nunnally, S. (1977). Managing construction equipment. Englewood
Cliffs, NJ: Prentice-Hall.
[13] RS Means (2011). Heavy construction cost data. Kingston, MA: RS Means.
[14] Schaufelberger, J. (1999). Construction equipment management. Upper Saddle River, NJ: Prentice-Hall.
[15] Shapira, A., & Goldenberg, M. (2005). AHP-based equipment selection
model for construction projects. Journal of Construction Engineering
and Management , 131 (12), 1263-1273.
[16] Tavakoli, A. (1985). Productivity analysis of construction operations.
Journal of Construction Engineering and Management , 111 (1), 31-39.
[17] Tavakoli, A., & Taye, E. (1989). Equipment policy of top 400 conractors: a survey. Journal of Construction Engineering and Management , 115 (2), 317-329.
[1] Alkass, S., & Harris, F. (1988). Expert system for earthmoving
equipment selection in road construction. Journal of Construction
Engineering and Management , 114 (3), 426-440.
[2] Amirkhanian, S., & Baker, N. (1992). Expert system for equipment
selection for earthmoving operations. Journal of Construction
Engineering and Management , 118 (2), 318-331.
[3] Anderson, D., Sweeney, D., Williams, T., & Martin, K. (2008). An
introduction to management science quantitative approaches to decision
making. Mason,OH: Thomson Higher Education.
[4] Belegundu, A., & Chandrupatla, T. (2002). Optimization concepts and
applications in engineering. Delhi: Pearson Education.
[5] Caterpillar® (2011). Caterpillar performance handbook. Peoria:
Caterpillar.
[6] Day, D., & Benjamin, N. (1991). Construction equipment guide. New
York: John Wiley & Sons.
[7] Gransberg, D., Popescu, C., & Ryan, R. (2006). Construction equipment
management for engineers, estimators, and owners. Boca Raton, FL:
Taylor & Francis Group.
[8] Haidar, A., Naoum, S., Howes, R., & Tah, J. (1999). Genetic algorithms
application and testing for equipment selection. Journal of Construction
Engineering and Management , 125 (1), 32-38.
[9] Marzouk, M., & Moselhi, O. (2003). Object-oriented simulation model
for earthmoving operations. Journal of Construction Engineering and
Management , 129 (2), 173-181.
[10] Marzouk, M., & Moselhi, O. (2004). Multiobjective optimization of
earthmoving operations. Journal of Construction Engineering and
Management , 130 (1), 105-113.
[11] Moselhi, O., & Marzouk, M. (2000). Automated system for cost estimating of earthmoving operations. Proceedings of the 17th International Symposium on Automation and Robotics in Construction
(ISARC), Taipei, Taiwan,, 1053-1058.
[12] Nunnally, S. (1977). Managing construction equipment. Englewood
Cliffs, NJ: Prentice-Hall.
[13] RS Means (2011). Heavy construction cost data. Kingston, MA: RS Means.
[14] Schaufelberger, J. (1999). Construction equipment management. Upper Saddle River, NJ: Prentice-Hall.
[15] Shapira, A., & Goldenberg, M. (2005). AHP-based equipment selection
model for construction projects. Journal of Construction Engineering
and Management , 131 (12), 1263-1273.
[16] Tavakoli, A. (1985). Productivity analysis of construction operations.
Journal of Construction Engineering and Management , 111 (1), 31-39.
[17] Tavakoli, A., & Taye, E. (1989). Equipment policy of top 400 conractors: a survey. Journal of Construction Engineering and Management , 115 (2), 317-329.
@article{"International Journal of Business, Human and Social Sciences:55462", author = "A. Jrade and N. Markiz and N. Albelwi", title = "An Economical Operation Analysis Optimization Model for Heavy Equipment Selection", abstract = "Optimizing equipment selection in heavy earthwork
operations is a critical key in the success of any construction project.
The objective of this research incentive was geared towards
developing a computer model to assist contractors and construction
managers in estimating the cost of heavy earthwork operations.
Economical operation analysis was conducted for an equipment fleet
taking into consideration the owning and operating costs involved in
earthwork operations. The model is being developed in a Microsoft
environment and is capable of being integrated with other estimating
and optimization models. In this study, Caterpillar® Performance
Handbook [5] was the main resource used to obtain specifications of
selected equipment. The implementation of the model shall give
optimum selection of equipment fleet not only based on cost
effectiveness but also in terms of versatility. To validate the model, a
case study of an actual dam construction project was selected to
quantify its degree of accuracy.", keywords = "Operation analysis, optimization model, equipment economics, equipment selection.", volume = "6", number = "1", pages = "77-6", }