Utility theory model for equipment selection

This paper presents a model for equipment selection in earthmoving operations, utilizing multi‐attribute utility theory, analytical hierarchy process and computer simulation. Fleet configurations in the developed model are generated randomly from predefined fleet scenarios within a specified range. Simulation experiments are conducted for these generated configurations. The performance of these configurations is obtained from simulation experiments in the form of four measures which represent loader utilization, hauler utilization, project duration and project total cost. The utility values which represent the degree of satisfaction with those measures are estimated. These utility values are multiplied by their corresponding measures’ weights, calculated utilizing the analytical hierarchy process, in order to estimate the expected utility for each configured fleet. The fleet configuration that has the largest utility value is selected as the optimum fleet for the case at hand. A numerical example is presented to illustrate the different features of the developed model.

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

[2]  Mohamed Marzouk,et al.  A decision support tool for construction bidding , 2003 .

[3]  Ian Flood,et al.  Modeling construction processes using artificial neural networks , 1996 .

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

[5]  Jingsheng Shi,et al.  Automated Construction‐Simulation Optimization , 1994 .

[6]  Brenda McCabe,et al.  Belief networks in construction simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

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

[8]  Ali Touran,et al.  Integration of simulation with expert systems , 1990 .

[9]  Joseph H. M. Tah,et al.  Genetic algorithms application and testing for equipment selection , 1999 .

[10]  Mohamed Marzouk Optimizing earthmoving operations using computer simulation , 2002 .

[11]  Said M. Easa,et al.  EARTHWORK ALLOCATIONS WITH NONCONSTANT UNIT COSTS , 1987 .

[12]  Thomas L. Saaty,et al.  Decision making for leaders , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Daniel W. Halpin,et al.  Design of construction and process operations , 1976 .

[14]  Sabah Alkass,et al.  EXPERT SYSTEM FOR EARTHMOVING EQUIPMENT SELECTION IN ROAD CONSTRUCTION , 1988 .

[15]  Michael Pidd,et al.  Object-orientation, Discrete Simulation and the Three-Phase Approach , 1995 .

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

[17]  David G. Carmichael,et al.  Eriang loading models in earthmoving , 1986 .

[18]  Miroslaw J. Skibniewski,et al.  Estimating construction productivity: neural-network-based approach , 1994 .