Currently, construction projects have a significant share in environmental pollution. Usually, the employers and managers of construction projects pay attention to the project implementation with the shortest duration and the lowest cost, whereas less attention is paid to the environmental effects of the implementation of projects. Sustainable development requires the planning and implementation of construction projects, taking environmental impacts, along with other factors, into account. Few studies have investigated the balancing time, cost, and environmental effects. Although the selection of an execution method for the project activity requires the use of decision-making methods, these methods have not been used in the project scheduling problems. This study seeks to simultaneously minimize the project time, cost, and environmental impacts. The purpose of this study is to evaluate the environmental impact of project activities in three physical, biological, and social aspects throughout the construction projects, and to attempt to minimize them as measurable values. In this paper, the environmental effects of an urban water supply construction project as a real case study are assessed in different activity execution modes by the Leopold matrix and the best execution mode of each project activity is selected using the CoCoSo (combined compromise solution) multi-criteria decision-making method, considering the time–cost-environmental impact trade-off. The CoCoSo method is employed because of its high flexibility compared to other multi-criteria decision-making methods. The results of this study will direct managers and stakeholders of construction projects to pay more attention to the environmental effects of construction project activities, together with the other conventional project goals and objectives, such as the time and cost.

The construction supply chain network has been facing challenges in relation to reducing cost and delivery time, increasing the quality of the built assets, and reducing environmental pollution. These issues have caused contractors and project managers in this industry to note the concept of green construction supply chain management (GCSCM). This study examined the most important challenges and barriers to the implementation of GSCM in the construction industry. In this paper, the components and sub-components of GCSCM were identified using the literature review and opinions of the experts according to the supply chain management. The opinions of construction experts and project managers were collected through focus group meetings. The components were categorized into five main and supporting groups, with “Green Design”, “Green Procurement”, and “Green Production” as the main components and “Green Management” and “Green Information” as the supporting components. Subsequently, the sub-components, in regard to each component, were distinguished. Finally, the fuzzy best–worst method (BWM) was utilized to determine the importance weights of the identified components and sub-components through the opinions of five experts with practical relevant experience. The findings of the fuzzy BWM method show that “Green Design” is the most important component, followed by “Green Management” and “Green Implementation”. Additionally, “Lack of designers, contractors and planners” was ranked the first among the identified sub-components. This paper can assist construction managers, contractors, and policymakers with finding and overcoming the barriers and obstacles of implementing GCSCM.

The increasing number of construction projects together with the limited resources of organizations led to tough competition for achieving project goals. Time, cost, and quality have been known as the project iron triangle. Project managers attempt to allocate the appropriate resources and make the best decisions for accomplishing projects with the shortest durations, lowest costs, and the highest quality. No study has examined the time–cost–quality trade-off problem with decision-making approaches. In this study, the fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity. For this purpose, the SWARA method is applied to determine the importance weights of time, cost, and quality. In addition, the TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) technique is used to rank and select the best activity execution modes. The proposed model is implemented on two medium- and large-size construction projects to evaluate its efficiency. Several execution modes with fuzzy duration, cost, and quality are considered for each project activity. Finally, sensitivity analysis is conducted taking three different conditions into account: the shortest duration of the execution modes, the lowest cost of the execution modes, and the highest quality of execution modes for each activity. The solution of each trade-off is compared with the solution obtained from the fuzzy SWARA–TOPSIS method. The schedule is developed according to the best execution mode for each project activity. The obtained results in two different construction projects show significant improvements in the overall project objectives so that the projects can be completed in fewer durations and costs along with higher quality. Because of the higher importance of cost, the cost of each activity is closer to the lowest cost. The activity duration is also closer to the most likely duration, and quality is closer to the high-quality level. The application of this approach can create new opportunities for research and knowledge development in the field of construction project scheduling.

Due to limited financial resources and high costs of infrastructure projects, project stakeholders seek to gain maximum profits with optimal resource utilization as well as cost and time minimization. Therefore, this study presents a multi-mode resource-constrained project scheduling model considering the uncertain parameters of cost and time together with the goals of maximizing the net present value and minimizing resource usage fluctuation. Also, the assumptions related to the real-world projects regarding multi-mode activities, limitation of renewable resources, and the deadline of project are incorporated into the proposed model. Moreover, a robust scheduling method is presented to better deal with the inherent uncertainties of projects regarding cost and time. The model is solved with the exact method named lexicographic goal programming (LGP). Due to the NP-hardness of the problem, two metaheuristic algorithms named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to solve various medium and large size problems. The obtained results indicate the high efficiency of the two metaheuristic algorithms in solving the problem and the better performance of the MOPSO algorithm compared with NSGA-II in terms of five indices. Furthermore, the model is implemented in an offshore equipment installation phase of a wellhead platform project. Finally, the sensitivity analysis of the proposed robust model is performed considering different conservation levels, and the results are evaluated by Monte Carlo simulation with three normal, uniform and triangular distributions. The findings demonstrate that the robustness of the model against the variations of uncertain parameters.