Multiobjective optimization for improving sustainable equipment options in road construction projects

The increasing awareness of sustainability has resulted in the progressive regulation of contractors for public projects to lower the pollution associated with their equipment fleet. Nevertheless, such implementation has not been fully developed yet due to the challenges this presents to contractor profitability and the practical usefulness of decision-support tools. This paper developed a sustainable equipment fleet (SEF) decision-support optimization model with the capability to reduce the CO2 emissions of construction equipment and to minimize the equipment fleet modification cost, subject to the contractor's budget and time constraints. The model utilizes a multiobjective genetic algorithm (GA) to search for the combination of equipment utilization, equipment model, and sustainable options that provide the optimal equipment fleet options to contractors. The results from the case study of a road construction project demonstrated the tradeoff between emissions and costs, with a wide range of optimal sustainable equipment fleet options. In addition, the fuel equivalent ratio (FER) and the equipment cost-effective ratio (CER) were newly established to normalize pollutant generation and equipment investment and to enable equipment comparisons for facilitating equipment selection by the contractor. The findings suggested that equipment selection involving a lower FER is preferable for sustainability and likewise, a lower CER for the cost aspect. However, effectiveness was measured as the proportion of FER to CER, with higher effectiveness when the proportion was lower. This paper facilitates the assessment and prioritization by construction companies of their equipment investment options to satisfy sustainability requirements.