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Full-Text Articles in Business
Predicting The Performance Of Queues: A Data Analytic Approach, Kum Khiong Yang, Cayirli Tugba, Mei Wan Low
Predicting The Performance Of Queues: A Data Analytic Approach, Kum Khiong Yang, Cayirli Tugba, Mei Wan Low
Research Collection Lee Kong Chian School Of Business
Existing models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, combining computer simulation to generate the data and an advanced non-linear regression technique called the Alternating Conditional Expectation (ACE) to construct a set of easy-to-use equations to predict the performance of queues with a scheduled start and end time. Our results show that the equations can accurately predict the queue performance as a function of the number of servers, mean arrival load, session length and service time variability. …
Managing Emergency Department Crowding Through Improved Triaging And Resource Allocation, Kum Khiong Yang, Sean Shao Wei Lam, Joyce M. W. Low, Marcus Eng Hock Ong
Managing Emergency Department Crowding Through Improved Triaging And Resource Allocation, Kum Khiong Yang, Sean Shao Wei Lam, Joyce M. W. Low, Marcus Eng Hock Ong
Research Collection Lee Kong Chian School Of Business
Long waiting times in emergency departments (EDs) not only reduce patients’ perceived quality of care, but also increase crowding which can adversely affect patients’ outcomes. Waiting time has been found to affect patients’ outcomes and is closely associated with delays in the provision of ancillary services to ED patients by the diagnostic/treatment laboratories. The focus of this study is to improve the flow of ED patients by testing alternative triage processes and capacity of physicians, triage nurses and laboratories. Three alternative triage processes are examined for managing the flow of ED patients through shared and dedicated laboratories across different utilization …
Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H.
Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
On-line portfolio selection is a practical financial engineering problem, which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. In recent years, a variety of machine learning algorithms have been proposed to address this challenging problem, but no comprehensive open-source toolbox has been released for various reasons. This article presents the first open-source toolbox for "On-Line Portfolio Selection" (OLPS), which implements a collection of classical and state-of-the-art strategies powered by machine learning algorithms. We hope that OLPS can facilitate the development of new learning methods and enable the performance benchmarking and comparisons of …