Multiobjective optimization of earthmoving operations

This paper presents a framework for optimizing earthmoving operations using computer simulation and genetic algorithms. It provides a multiobjective optimization tool geared towards selection of near-optimum fleet configurations. The optimization aims at minimizing time and cost of earthmoving operations. The proposed framework considers factors that influence earthmoving operations including equipment availability and project indirect cost, The simulation process, in the proposed methodology, utilizes discrete event simulation and object oriented modeling. The optimization process uses a recently developed genetic algorithm to search for a near-optimum fleet configuration employing Pareto optimality to account for multiobjective optimization. The algorithm considers a set of qualitative and quantitative variables that influence the production of earthmoving operations. The developed framework supports time-cost tradeoff analysis and can assist users in considering what if scenarios with respect to fleet configurations. A numerical example is presented to illustrate a number of practical features of the proposed framework and to demonstrate its capabilities in selecting near-optimum fleet configurations.