Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

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Abstract-Human resources assignment is the process of creating an employee's assignment in order to meet the demand of a set of tasks over time horizon. Many research works have been developed for similar problems in many field areas like health-caring, manufacturing, transportation… However in our study, we approach a general case where tasks do not have a specific daily or weekly pattern. Employees can be assigned to more than one task per day and taking in consideration multiple sites. The objective of our study is to find a feasible solution that respect different constraints relative to labor regulations and a constraint relative to multiple sites, balance the workload over employees and minimize overload hours. We propose a mixed integer programming model and a key performance indicator based heuristic to solve this problem. The results of the heuristic are very promising.

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