Vehicle Routing Problem (VRP) is important in the transportation and logistics industries. Vehicle Routing Problem with Time Window (VRPTW) is a kind of VRP with the additional time windows constraint in the model and is classified as an NP-hard problem. In this study, we proposed Stas crossover in Genetic Algorithm (GA) to solve VRPTW by developing the problem with K-mean clustering. The experiments use the standard Solomon’s benchmark problem instances for VRPTW. The results with K-mean clustering are shown to perform better for minimum distance and average distance than without K-mean clustering. In the case of location and dispersion characteristics of the customer, the paths with K-mean clustering are arranged into groups and are orderly, but the paths without K-mean clustering are disordered. After that, this paper shows the comparison of the crossover operator performance on instances of Solomon benchmark, and appropriate crossover operators are recommended for each type of problem. The results of the proposed algorithm are better than the best-known solutions from the previous studies for some instances. Moreover, our proposed research will serve as a guideline for a real-world case study.

COVID -19 pandemic has a huge global impact on education over the world. Many countries decided to close universities, colleges, and schools to limit the spread of this disease. Almost 91% of students worldwide have shifted to online education. Educational institutions have struggled to provide their students with suitable online learning and assessment tools. As a new experience for both teachers and students, Imam Abdulrahman Bin Faisal University has set new online academic services to make it possible and easy for students to get the help they need and to overcome the new obstacles they are facing. The purpose of this study is to gain a deeper understanding of student satisfaction with their academic advising in light of the new emerging situation. Additionally, direc-tions were presented for the academic advising section members to allow them to manage the unit appropriately. To achieve that, students were clustered regarding their level of satisfaction with the provided services. Students’ answers were collected through an online questionnaire and the data were analyzed and segmented using the k-mean clustering technique. Regarding results, recommendations for improvements were suggested and action plans were prepared.