Optimization of environmental impacts of construction projects: a time–cost–quality trade-off approach

Abstract

The construction industry and the implementation of civil projects are considered to be one of the causes of environmental pollution. Given the impacts and pollution created by the implementation of projects, it is necessary to identify the environmental impacts in order to reduce their effects. The purpose of this paper is to present a discrete time–cost–quality–environmental impacts trade-off problem for the construction industry with multiple execution modes for project activities in order to reduce the environmental impacts of the project implementation. A multi-objective planning model was developed in this problem to address the four objectives of minimizing time, cost, and environmental impacts and maximizing quality of the project implementation. Then, the problem was modeled as a single-objective programming model by converting the objective function to a constraint. The Leopold matrix method was used to evaluate environmental impacts. Finally, part of a rural water supply project was used as a practical example to demonstrate the applicability and efficiency of the model. The results of this study showed that the proposed method to optimize the aforementioned objectives is highly efficient, and planning and decision making in the early stages of a project to choose the method of doing the project activities will result in reducing costs, time, environmental impacts, and enhancing the quality of the project. By calculating the environmental impacts of project activities in each execution mode, project managers and stakeholders can take into account the environment around them, along with pursuing the economic, time, and quality objectives of the project, and minimize the project overall negative environmental impacts as a measurable amount. This research can help project managers choose the most appropriate method to execute their activities so that the project will ultimately be delivered with the least amount of time, cost, and environmental impact, and with the highest quality.