| Abstrakt: |
Purpose: The cost-time tradeoff in project scheduling is a significant challenge that has garnered substantial attention. This research aims to establish a balance between time compression and delays in activity execution to optimize resource utilization and facilitate activity scheduling based on existing constraints. To achieve this, a bi-objective mathematical model is developed to support decision-makers in selecting the optimal schedule for project execution. Methodology: In this study, a bi-objective mathematical model is proposed to balance cost and time, incorporating a nonlinear cost function and the time value of money. The model is then solved using the multi-objective particle swarm optimization (MOPSO) algorithm to analyze the effects of time compression and activity delays on the outcomes. Findings: The proposed model's results demonstrate its ability to optimize project resource usage by considering current constraints and capacities. This enables decision-makers to adjust activity scheduling to achieve the best balance between cost and time. Furthermore, the model assumes costs are nonlinear and calculated based on the time value of money. This approach allows for scheduling decisions that account for the time value, reducing delay-related costs and enhancing overall project efficiency. Originality/Value: This research presents a novel approach by introducing a bi-objective decision-making model that integrates the time value of money in project scheduling, marking a new step in optimizing the cost-time tradeoff. Unlike previous studies that focus solely on reducing either cost or time, the proposed model provides a comprehensive solution by accounting for nonlinear complexities and the time value of money. This model assists project managers in gaining better insight into cost-time impacts, optimizing resource allocation, and ultimately improving project performance by reducing delays. [ABSTRACT FROM AUTHOR] |
