Azar Sadeghnejad-Barkousaraie

Current beam orientation optimization algorithms for radiotherapy, such as column generation (CG), are typically heuristic or greedy in nature because of the size of the combinatorial problem, which leads to suboptimal solutions. We propose a reinforcement learning strategy using a Monte Carlo Tree Search (MCTS) that can find a better beam orientation set in less time than CG. We utilize a reinforcement learning structure involving a supervised learning network to guide the MCTS and to explore the decision space of beam orientation selection problems. We previously trained a deep neural network (DNN) that takes in the patient anatomy, organ weights, and current beams, then approximates beam fitness values to indicate the next best beam to add. Here, we use this DNN to probabilistically guide the traversal of the branches of the Monte Carlo decision tree to add a new beam to the plan. To assess the feasibility of the algorithm, we used a test set of 13 prostate cancer patients, distinct from the 57 patients originally used to train and validate the DNN, to solve five-beam plans. To show the strength of the guided MCTS (GTS) compared to other search methods, we also provided the performances of Guided Search, Uniform Tree Search and Random Search algorithms. On average, GTS outperformed all the other methods. It found a better solution than CG in 237 s on average, compared to 360 s for CG, and outperformed all other methods in finding a solution with a lower objective function value in less than 1000 s. Using our GTS method, we could maintain planning target volume (PTV) coverage within 1% error similar to CG, while reducing the organ-at-risk mean dose for body, rectum, left and right femoral heads; the mean dose to bladder was 1% higher with GTS than with CG.

There has been a significant increase in the number of sensors deployed to accomplish military missions. These sensors might be on manned or unmanned resources, and might collect quantitative and/or qualitative information important for mission success. Of critical importance for mission success is ensuring that the collected information is routed to the people/systems that need the information for the proper making of decisions. For military applications, routing of information across a communication network has typically been accomplished using fixed, a priori defined, routing paths. When bandwidth between resources is unlimited, this presents no problems. However, in bandwidth constrained environments, when not all information is able to be routed across the network, then fixed routing paths presents limitations in information reaching appropriate consumers. In this research, we consider the advantages to using dynamic shortest temporal path routes for the information, as opposed to fixed routing paths. Multiple metrics show empirically the benefit to dynamic shortest path routes.

Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE) and a greedy randomised adaptive search procedure (GRASP) to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP