Sondes Chaabane

A simulation-based decision support system to prevent and predict strain situations in emergency department systems

Abstract The management of patient flow, especially the flow resulting from health crises in emergency departments (ED), is one of the most important problems managed by ED managers. To handle this influx of patients, emergency departments require significant human and material resources, but these are limited. Under these conditions, the medical and paramedical staff are often confronted with strain situations. To deal with these situations, emergency departments have no choice but to adapt. The main purpose of this article is to develop a simulation-based decision support system (DSS) to prevent and predict strain situations in an ED in order to improve their management by the hospital system. A discrete-event simulation model was constructed in order to visualize the strain situations, examine the relationship between the strain situations and propose corrective actions. A simulation experiment is presented with the results, identifying several important aspects of the strain situations and corrective actions in ED systems. The results have proven the importance of anticipation and management of strain situations in emergency departments.

Time Series Modelling and Forecasting of Emergency Department Overcrowding

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

A genetic algorithm for robust hybrid flow shop scheduling

Most of scheduling methods consider a deterministic environment for which the data of the problem are known. Nevertheless, in reality, several kinds of uncertainties should be considered, and robust scheduling allows uncertainty to be taken into account. In this article, we consider a scheduling problem under uncertainty. Our case study is a hybrid flow shop scheduling problem, and the processing time of each job for each machine at each stage is the source of uncertainty. To solve this problem, we developed a genetic algorithm. A robust bi-objective evaluation function was defined to obtain a robust, effective solution that is only slightly sensitive to data uncertainty. This bi-objective function minimises simultaneously the makespan of the initial scenario, and the deviation between the makespan of all the disrupted scenarios and the makespan of the initial scenario. We validated our approach with a simulation in order to evaluate the quality of the robustness faced with uncertainty. The computational results show that our algorithm can generate a trade off for effectiveness and robustness for various degrees of uncertainty.

Improved principal component analysis for anomaly detection

Developed PCA-based MCUSUM anomaly detection (AD) method.Extended the AD advantages of the MCUSUM to enhance the conventional PCA.The proposed algorithm is applied to monitor an emergency department.The detection results show effectiveness of the proposed method. Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotellings T 2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

Seasonal ARMA-based SPC charts for anomaly detection

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

Semi-heterarchical control of FMS: From theory to application

This paper presents both a model and a real implementation of a semi-heterarchical control system for flexible manufacturing systems (FMS). After presenting the concepts of heterarchical and semi-heterarchical control, a product-based control structure, composed of a dynamic allocation process (DAP) and a dynamic routing process (DRP), is proposed. Though the associated control processes (DAP and DRP) are hierarchically dependant, each is managed heterarchically, with no supervisor. The dynamic allocation algorithms are presented, and our highly distributed approach to routing control is then explained in detail. A real distributed application of the active entities and the control architecture was implemented in the AIP-PRIMECA pole at the University of Valenciennes, and this implementation is described in detail. A mixed-integer linear model of the FMS was used to compute lower bounds. The flexibility and robustness of our approach are highlighted through several real experiments.

Scheduling under uncertainty: Survey and research directions

In real-world scheduling problems, several kinds of hard-to-predict risk must be considered. Scheduling under uncertainty allows these kinds of risks to be taken into account. This paper provides an overview of the state of the art in scheduling under uncertainty, including a survey on modeling techniques of uncertainty and a survey of the existing positioning typologies and contributions. A new classification scheme for the different approaches to scheduling under uncertainty is proposed and discussed. Several areas for future research are suggested.

A rail-road PI-hub allocation problem

The rail-road π-hub allocation problem for the newly proposed physical internet concept is studied.Both static and dynamic scenarios are considered.For static case, heuristic and Simulating Annealing based approaches are proposed.Multi-agent based approach is proposed to deal with dynamic scenarios in case of perturbations.All approaches are evaluated on simulated scenarios. This research concerns an allocation problem in the context of the physical internet aimed at improving rail-road π-hub efficiency by optimizing the distance travelled by each container to the dock, as well as the number of trucks used. To achieve this, heuristic, metaheuristic and Multi-agent-based approaches are proposed. When given the sequence of all the containers in the train, the proposed heuristic approach can assign these containers to outbound doors. Then, the Simulating Annealing (SA) method improves this allocation by minimizing the distance travelled. In addition, a multi-agent system model is proposed to generate reactive solutions which take dynamic aspects into account.The experimental results show that the proposed SA yields an improvement of about 2.42-7.67% in relation to the solution generated by the heuristic; it provides good results within a reasonable time. Conversely, the multi-agent-based approach provides good solutions in case of perturbations or unexpected events.

A multi-agent system based on reactive decision rules for solving the caregiver routing problem in home health care

Home Health Care (HHC) services are growing worldwide. HHC providers that employ their caregivers have to manage operational decisions such as assigning patients to caregivers and planning the caregivers’ routes. Centralized “off-line” approaches are generally used to deal with both these problems. In this paper, we solved the caregiver routing problem in a dynamic and distributed way using a Multi-agent system (MAS) to simulate caregiver behavior. Four decision rules were developed for the caregivers: NPR (Nearest Patient Rule), NRR (No-wait Route Rule), SRR (Shortest Route Rule), and BRR (Balanced Route Rule). These decision rules were implemented and tested on a multi-agent platform to assess their performances. We designed an experimental plan based on case studies that represent different sizes of HHC provider inspired from real-world examples. The results obtained show the relevance of using local decision rules to plan the caregivers route.

Early detection of abnormal patient arrivals at hospital emergency department

Overcrowding is one of the most crucial issues confronting emergency departments (EDs) throughout the world. Efficient management of patient flows for ED services has become an urgent issue for most hospital administrations. Handling and detection of abnormal situations is a key challenge in EDs. Thus, the early detection of abnormal patient arrivals at EDs plays an important role from the point of view of improving management of the inspected EDs. It allows the EDs mangers to prepare for high levels of care activities, to optimize the internal resources and to predict enough hospitalization capacity in downstream care services. This study reports the development of statistical method for enhancing detection of abnormal daily patient arrivals at the ED, which able to provide early alert mechanisms in the event of abnormal situations. The autoregressive moving average (ARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the pediatric emergency department (PED) at Lille regional hospital center, France.