In a dynamic environment, the assignment problem can be adapted by incorporating real-time data and decision-making flexibility into the model. Traditional assignment models assume static resources and tasks, but in a dynamic setting, the availability of resources or tasks may fluctuate, requiring continuous updates to the assignment decisions. One way to address this is by using a dynamic or time-dependent assignment approach, where assignments are reevaluated at regular intervals based on new information. Algorithms such as those based on rolling horizons, where decisions are made for short periods and then reassessed, can help accommodate these changes. Additionally, techniques like the use of predictive analytics or machine learning can help forecast changes in task or resource availability, allowing for more proactive adjustments. This adaptability ensures that the assignment remains optimized despite the evolving environment.