This research is focused on the proactive-reactive rescheduling process of the Train Unit Shunting Problem (TUSP) on train maintenance shunting yards. An important difference between a scheduling process and the rescheduling process is that a reschedule must be both feasible and desirable (similar to pre-schedule), while a schedule does not require desirability. Firstly, an abstract proactive-reactive rescheduling framework is proposed. This framework is proposed as a base for the general problem of proactive-reactive rescheduling. A total of four reactive rescheduling methods for the TUSP are proposed and implemented as extensions to the Simulated Annealing local search algorithm. The extensions are created in order to achieve reschedule desirability by either guiding the local search process during its iterations or influencing its starting point. Using experiments based on realistic data, it is concluded that the Simulated Annealing can be used to create reschedules that are both feasible as well as desirable. It is shown that both proposed extensions are required for the best reschedule quality. Finally, further analysis of the results shows that the schedule resilience to late arrival of trains can be improved upon by increasing the amount of time between a train arrival and the time at which its services are scheduled.

The Train Unit Shunting Problem (TUSP) is a well studied problem within the NS (Dutch Railways). TUSP is a problem where trains have to be routed on a shunting yard such that maintenance tasks can be performed and no collisions occur. Furthermore, incoming trains have to be matched to outgoing trains, since trains of the same type can be used interchangeably. Currently, the NS uses a local-search approach to solve TUSP; however, this approach is sometimes unable to find a simple detour route when the initial route of the train is blocked by another train. This research models the TUSP as a Multi-Agent Pathfinding (MAPF) problem to solve the routing issue. We solve this model of the TUSP using Conflict-Bases Search (CBS). To achieve this, CBS is altered to work with multiple trains on the same node, to work with directed edges, and to only allow wait-moves on certain nodes. In this work, we propose two ways of routing multiple trains on the same node; CBSL, where trains are given an exact location on the track; and CBSQ, where the order in which trains enter a track is recorded with a queue. Of these two methods, the queue model was deemed best. Finally, we propose a heuristic to prefer solutions where trains are more spread out on the shunting yard. This heuristic greatly improves the runtime at a loss of optimality.

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone microscope system compared to traditional high-end light microscopes, conventional algorithms fail to process these images. We propose a framework using a convolutional neural network as a pixel classifier to localize the erythrocytes. Afterwards we classify them accordingly, using a convolutional neural network as an object classifier. Such a system can offer in the-field malaria diagnosis without human intervention or can act as an aid for human experts to lower workload and increase diagnosis accuracy. The algorithm successfully localizes the erythrocytes with an average sensitivity of 97.31% and precision of 92.21%. Classification performed inadequate, in terms of low agreement with two human experts. This can be due to the low image quality or the small amount of training data available at the time.