Selection of heavy machinery for earthwork activities: A multi-objective optimization approach using a genetic algorithm

Abstract

Earthmoving activity is considered one of the most critical elements in construction projects. The overall cost of earthmoving activity during construction projects can account for more than 30% of the total cost. Moreover, earthmoving equipment emits enormous carbon, which has adverse environmental effects. A mathematical model is needed to optimize the selection of the equipment types (i.e., trucks and excavators) and the numbers of each type to be employed on a particular project, based on the work capacity of each unit, the number of units, and the speed at which each unit travels. The model proposed here is based on a genetic algorithm (GA) based optimization technique for planning earthmoving projects. The model has three main steps: (1) identify all the relevant decision variables for choosing the earthmoving equipment, (2) derive a mathematical optimization model, and (3) apply multi-objective genetic algorithms. For a particular selection of earthmoving units, the model can show how the total project costs will vary concerning the time allowed to complete the project while also providing data showing the total amount of carbon emissions and fuel consumption for the entire project. Data derived from a real-world earthmoving project was employed to test and validate the model. The model was able to show the potential for saving substantial cost and time. On average, the optimization model showed how to obtain savings of 14.35% and 9.5% for the time and the cost objectives, respectively, along with significant reductions in fuel consumption and CO2 emissions. These results suggest that the proposed optimization model would be a valuable tool to support contractors and construction management engineers to minimize earthmoving projects' time and cost.