RK
R. Katz
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Effectively solving the Nurse Rostering Problem enhances nurse moral and leads to improved patient care. While the use of ejection chains has shown promise in previous studies, studying their impact on real-life instances from two Dutch hospitals further deepens our understanding of their practical utility. This thesis begins by discussing the Nurse Rostering Problem (NRP) as presented in literature, highlighting the lack of research conducted in real-life settings. We then describe the context of Dutch hospitals by defining the relevant hard and soft constraints. Afterwards, a review of previous NRP solutions is provided, covering various solvers and techniques, including ejection chains. Ejection chains are an approach that combines multiple local search moves into a larger one, where each move forces (ejects) the next move. These moves are executed consecutively, forming a chain. Two recent ejection chain approaches were identified for further investigation, and a novel approach is also proposed.
Afterwards, the concept of the three ejection chains is explained. The first ejection chain, InfeasibleEC, is inspired by Kingston, and explores regions of the search space that contain infeasible rosters. It starts with a swap that introduces a single hard constraint violation, but results in lower roster penalty. Then the ejection chain proceeds with a series of repairs, where each repair fixes the previous violation, but may introduce a single new violation. Four different approaches to explore the search space were implemented. The second ejection chain described is EmulateEC by Curtois et al. This chain emulates human schedule planners by performing a series of vertical swaps. Each swap improves the penalty for one nurse but simultaneously increases the penalty for a different nurse. Thus, the next swap aims to improve the nurse whose penalty was worsened in the previous step. This series of swaps is repeated until a better roster is found. The third ejection chain, RuinRecreateEC, repeatedly performs ruin and recreate operations in sequence. The idea is that each ruin and recreate operation makes a relatively small change to the roster, and by repeating the process multiple times, the final roster undergoes substantial diversification while still retaining much of the structure of the original roster.
All three ejection chains underwent individual parameter tuning using a sequential greedy tuning strategy to identify suitable parameter configurations based on two instances from the same hospital. Once tuned, the chains were further tested by rerunning the chains on the initial instances as well as on additional instances from a second hospital to assess their robustness. We analysed the breakdown of the penalty based on the different soft constraints, and investigated the impact of the chains on finding better rosters throughout the running time. We could not find improvements on specific soft constraints penalties, nor on finding better rosters faster. A significance test was conducted to determine whether the chains significantly reduce the penalty of the final rosters. There was insufficient evidence to conclude a statistically significant improvement. ...
Afterwards, the concept of the three ejection chains is explained. The first ejection chain, InfeasibleEC, is inspired by Kingston, and explores regions of the search space that contain infeasible rosters. It starts with a swap that introduces a single hard constraint violation, but results in lower roster penalty. Then the ejection chain proceeds with a series of repairs, where each repair fixes the previous violation, but may introduce a single new violation. Four different approaches to explore the search space were implemented. The second ejection chain described is EmulateEC by Curtois et al. This chain emulates human schedule planners by performing a series of vertical swaps. Each swap improves the penalty for one nurse but simultaneously increases the penalty for a different nurse. Thus, the next swap aims to improve the nurse whose penalty was worsened in the previous step. This series of swaps is repeated until a better roster is found. The third ejection chain, RuinRecreateEC, repeatedly performs ruin and recreate operations in sequence. The idea is that each ruin and recreate operation makes a relatively small change to the roster, and by repeating the process multiple times, the final roster undergoes substantial diversification while still retaining much of the structure of the original roster.
All three ejection chains underwent individual parameter tuning using a sequential greedy tuning strategy to identify suitable parameter configurations based on two instances from the same hospital. Once tuned, the chains were further tested by rerunning the chains on the initial instances as well as on additional instances from a second hospital to assess their robustness. We analysed the breakdown of the penalty based on the different soft constraints, and investigated the impact of the chains on finding better rosters throughout the running time. We could not find improvements on specific soft constraints penalties, nor on finding better rosters faster. A significance test was conducted to determine whether the chains significantly reduce the penalty of the final rosters. There was insufficient evidence to conclude a statistically significant improvement. ...
Effectively solving the Nurse Rostering Problem enhances nurse moral and leads to improved patient care. While the use of ejection chains has shown promise in previous studies, studying their impact on real-life instances from two Dutch hospitals further deepens our understanding of their practical utility. This thesis begins by discussing the Nurse Rostering Problem (NRP) as presented in literature, highlighting the lack of research conducted in real-life settings. We then describe the context of Dutch hospitals by defining the relevant hard and soft constraints. Afterwards, a review of previous NRP solutions is provided, covering various solvers and techniques, including ejection chains. Ejection chains are an approach that combines multiple local search moves into a larger one, where each move forces (ejects) the next move. These moves are executed consecutively, forming a chain. Two recent ejection chain approaches were identified for further investigation, and a novel approach is also proposed.
Afterwards, the concept of the three ejection chains is explained. The first ejection chain, InfeasibleEC, is inspired by Kingston, and explores regions of the search space that contain infeasible rosters. It starts with a swap that introduces a single hard constraint violation, but results in lower roster penalty. Then the ejection chain proceeds with a series of repairs, where each repair fixes the previous violation, but may introduce a single new violation. Four different approaches to explore the search space were implemented. The second ejection chain described is EmulateEC by Curtois et al. This chain emulates human schedule planners by performing a series of vertical swaps. Each swap improves the penalty for one nurse but simultaneously increases the penalty for a different nurse. Thus, the next swap aims to improve the nurse whose penalty was worsened in the previous step. This series of swaps is repeated until a better roster is found. The third ejection chain, RuinRecreateEC, repeatedly performs ruin and recreate operations in sequence. The idea is that each ruin and recreate operation makes a relatively small change to the roster, and by repeating the process multiple times, the final roster undergoes substantial diversification while still retaining much of the structure of the original roster.
All three ejection chains underwent individual parameter tuning using a sequential greedy tuning strategy to identify suitable parameter configurations based on two instances from the same hospital. Once tuned, the chains were further tested by rerunning the chains on the initial instances as well as on additional instances from a second hospital to assess their robustness. We analysed the breakdown of the penalty based on the different soft constraints, and investigated the impact of the chains on finding better rosters throughout the running time. We could not find improvements on specific soft constraints penalties, nor on finding better rosters faster. A significance test was conducted to determine whether the chains significantly reduce the penalty of the final rosters. There was insufficient evidence to conclude a statistically significant improvement.
Afterwards, the concept of the three ejection chains is explained. The first ejection chain, InfeasibleEC, is inspired by Kingston, and explores regions of the search space that contain infeasible rosters. It starts with a swap that introduces a single hard constraint violation, but results in lower roster penalty. Then the ejection chain proceeds with a series of repairs, where each repair fixes the previous violation, but may introduce a single new violation. Four different approaches to explore the search space were implemented. The second ejection chain described is EmulateEC by Curtois et al. This chain emulates human schedule planners by performing a series of vertical swaps. Each swap improves the penalty for one nurse but simultaneously increases the penalty for a different nurse. Thus, the next swap aims to improve the nurse whose penalty was worsened in the previous step. This series of swaps is repeated until a better roster is found. The third ejection chain, RuinRecreateEC, repeatedly performs ruin and recreate operations in sequence. The idea is that each ruin and recreate operation makes a relatively small change to the roster, and by repeating the process multiple times, the final roster undergoes substantial diversification while still retaining much of the structure of the original roster.
All three ejection chains underwent individual parameter tuning using a sequential greedy tuning strategy to identify suitable parameter configurations based on two instances from the same hospital. Once tuned, the chains were further tested by rerunning the chains on the initial instances as well as on additional instances from a second hospital to assess their robustness. We analysed the breakdown of the penalty based on the different soft constraints, and investigated the impact of the chains on finding better rosters throughout the running time. We could not find improvements on specific soft constraints penalties, nor on finding better rosters faster. A significance test was conducted to determine whether the chains significantly reduce the penalty of the final rosters. There was insufficient evidence to conclude a statistically significant improvement.
Performance comparison of different federated learning aggregation algorithms
How does the performance of different federated learning aggregation algorithms compare to each other?
Federated learning enables the construction of machine learning models, while adhering to privacy constraints and without sharing data between different devices. It is achieved by creating a machine learning model on each device that contains data, and then combining these models through an aggregation algorithm without sharing the data. Federated learning is currently a hot topic, and a lot of research has gone into implementing accurate aggregation algorithms. The original algorithm is FedAvg, and since then many different algorithms have been introduced. In this paper, I will compare the performance of five different aggregation algorithms: FedAvg, FedProx, FedYogi, FedMedian and q-FedAvg. The algorithms are compared on different data sets, namely MNIST and a kinase inhibition data set, as well as on different data distributions and number of clients. The experiments indicate that among these five algorithms, FedYogi achieves the best performance, both in terms of highest final accuracy as well as in terms of convergence rate.
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Federated learning enables the construction of machine learning models, while adhering to privacy constraints and without sharing data between different devices. It is achieved by creating a machine learning model on each device that contains data, and then combining these models through an aggregation algorithm without sharing the data. Federated learning is currently a hot topic, and a lot of research has gone into implementing accurate aggregation algorithms. The original algorithm is FedAvg, and since then many different algorithms have been introduced. In this paper, I will compare the performance of five different aggregation algorithms: FedAvg, FedProx, FedYogi, FedMedian and q-FedAvg. The algorithms are compared on different data sets, namely MNIST and a kinase inhibition data set, as well as on different data distributions and number of clients. The experiments indicate that among these five algorithms, FedYogi achieves the best performance, both in terms of highest final accuracy as well as in terms of convergence rate.