An Economical Operation Analysis Optimization Model for Heavy Equipment Selection
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
An Economical Operation Analysis Optimization Model for Heavy Equipment Selection

Authors: A. Jrade, N. Markiz, N. Albelwi

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, CaterpillarĀ® 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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062804

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4263

References:


[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] CaterpillarĀ® (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.