Optimization of Dump Truck and Excavator Usage for Earthwork Activities using Genetic Algorithm Method

Abstract

Earthmoving activities constitute a fundamental component of infrastructure development, particularly in large-scale projects such as the construction of the Presidential Palace at Indonesia’s new capital city (Ibu Kota Nusantara/IKN). The effectiveness and efficiency of these operations are highly dependent on the optimal selection and combination of heavy equipment, notably excavators and dump trucks. The challenge lies in the complexity of multiple variables, including equipment capacity, operational speed, fuel consumption, and both fixed and variable costs. Conventional selection methods—often relying on manual calculation, managerial intuition, and trial-and-error—are limited in addressing these multidimensional considerations. This study proposes a computational model utilizing a Genetic Algorithm (GA) to optimize equipment configuration by minimizing project duration and operational costs. The model integrates various technical and economic parameters into two main objective functions: Activity Time (AT) and Activity Cost (AC). The GA's capability to efficiently explore large solution spaces and handle multi-objective problems provides a significant advantage over traditional approaches. A case study was conducted on the earthmoving operations of the Presidential Palace construction project, involving 55,052 m³ of soil and a 2.5 km hauling distance. The optimal configuration—comprising 3 excavators and 3 dump trucks operating at a speed of 25 km/h—achieved a project duration of 414.36 hours, a 30% reduction compared to the actual duration (592 hours), and a total cost of IDR 2,790,976,831, which is approximately 1.2% lower than the actual project cost. These results highlight the GA model’s effectiveness in producing more efficient and cost-effective equipment configurations. The proposed method offers valuable decision-making support for project managers and contractors seeking to improve the planning and execution of earthwork activities through systematic optimization.