There is no doubt Medical Devices have a crucial role in hospital processes such as surgeries and therapeutic procedures. Medical devices available in hospitals are of two types; reusable and non-resalable medical devices. Reusable medical devices are washed and sterilized after each use. The process of sterilizing medical devices is performed in the sterilization department. Each medical device travels through a cycle each time it is utilized. It is explicit that any part of the sterilization cycle that delays the process can cause serious problems for hospitals’ performance. The washing step of the sterilization process has been a bottleneck in the system. Thus, optimization approaches can be highly advantageous to improve this bottleneck. The data of the medical devices are usually unknown prior to the scheduling process since the finishing time of the surgeries are not known in advance. Thus, there is no information available on the ready time of medical devices to be sterilized. Due to this factor, to develop applicable solutions, it is critical to consider this problem as an online problem and develop online scheduling methods. In this thesis, we take advantage of mathematical programming and heuristic algorithms to solve both the offline and online settings of the problem. We model the washing step of the sterilization cycle as a scheduling problem. Batch scheduling and bin packing, two well-known optimization approaches, are used for this purpose. Medical devices are batched together first and then scheduled on machines to reduce the total washing time of all medical devices. First, a mathematical model for the offline problem is provided and tested to solve the problem. Then a series of heuristic algorithms are developed using the batch scheduling approach for solving both offline and online problems. Moreover, a special case with divisible job sizes and equal release dates is studied. It was proved that for the strongly divisible sequence the First Fit Increasing algorithm finds the optimal solution, also for the weakly divisible sequence a Dynamic Programming algorithm is developed. Finally, we couple optimization with simulation to test the impact of the optimization of the washing step on the entire sterilization system. Moreover, since the next step of the sterilization cycle, the sterilization step, is very similar to the washing step, we also implement the developed heuristics in this step to evaluate its performance and improve it further. The results show that as long as the washing step is optimized it does not differ which algorithm is used in the sterilization step, thus, the optimization of this step is not necessary.