Muhammad Umer Aslam’s scientific contributions

To depict the dynamics between the two decision-makers in the research, two models are developed: one based on game theory and the other on cooperative dynamics. Russian water delivery networks as they stand right now, with a focus on the replacement of a large chunk to prevent disastrous repercussions. Government subsidies are used to build models of cooperative and non-cooperative games using an innovative approach to financial accounting. An investigation of the Volga River Water Diversion Project in the southwest of the country is conducted to demonstrate the effectiveness of the concept. The Volga River has ongoing challenges in its ability to purify itself and provide potable water as a result of pollution and diminishing resources, similar to other significant rivers in Europe. Currently, national goal programs are being put in place to guarantee the fast development of certain regions within the Russian Federation. These programs seek to provide the best possible environment for investment to flow into different areas of regional development. In theory, the Shapley value is defined by characteristics with appealing real-world implications; hence, its application in practical settings is easily justified. The Shapley value is an important solution idea in voting games in general, and optimal water supply and income rose in tandem with the number of subsidies, while water work costs fell.

Citation: Ghaffar, M.A.; Peng, L.; Aslam, M.U.; Adeel, M.; Dassari, S. Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies. Electronics 2024, 13, 3650. Abstract: In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations.