Christian Tahon

Integration of the human operator into responsive discrete production management systems

Abstract The need for production systems that can react or respond to dynamic changes is continuously increasing because of the reduction of product life cycle time and the rise of competition. To improve responsiveness, we show that integrating the intelligence of the human operator into the system helps to face complexity. However, little work has been done about the optimization of such integration considering production system constraints (real time decision making, observability, etc.) and human operator constraints (mental workload, trust in management system, self-confidence, etc.). This paper aims at discussing ways to take account of the cognitive abilities of the human operator and offers some advice on how to take accurately into account the integration of the human operator by the proposal of a set of global specifications. To illustrate how it is possible to contribute to the optimized design of a system based upon such specifications, we propose the concept of “distributed production management system”. We first address the specific interface issue. A classical example of “advanced display” designed for continuous systems, that is, the Ecological Interface Design (EID) approach, is applied to discrete production systems. We show that such an approach is coherent with parts of the introduced specifications but can be adapted to large complex and discrete systems with difficulty. To solve this issue, we propose the development of a distributed DSS where each local DSS integrates an advanced display and manages a set of production resources in cooperation with an operator, which reduces the global complexity.

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.

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.

A multicriteria decision support system for dynamic task allocation in a distributed production activity control structure

A decision support system DSS for dynamic task allocation in a distributed structure for flexible manufacturing systems FMS has been developed in the LAMIH laboratory. An entity of the manufacturing system is considered as an autonomous agent, called Integrated Management Station IMS , able to co-operate with other agents to achieve a global production program. Co-operation is performed by exchanging messages among the different agents. The characteristics of a DSS that supports multi-criteria MC algorithms and sensitivity tests is presented. This DSS is integrated to each decision system of every IMS. It aims at allocating tasks in a dynamic way by proposing to the human operator a selection of possible resources.

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.

Hybrid approach to decision-making for job-shop scheduling

The job-shop scheduling problem is one of the hardest problems (NP-complete problem) {Garey and Johnson 1979, Computer and Intractability: A Guide to the Theory of NP-completeness (San Francisco: Freeman)}. In lots of cases, the combination of goals and resources exponentially increases the search space, and the generation of consistent and satisfying schedules is more and more difficult. This article shows the coupling of three approaches in order to contribute to the solving of the job-shop scheduling problem: genetic algorithms (GAs); constraint logic programming (CLP); and multi-criteria decision-making (MCDM). The GAs are searching algorithms based on the mechanics of natural selection. They employ a probabilistic search for locating the globally optimal solution. They start with an initial population often randomly generated. The difficulty holds in the creation of this initial population which is considered an important parameter of GAs. CLP is a concept based on operation research (OR) and artific...

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.

Hybrid production control approach for JIT scheduling

A distributed production activity control system has been developed in the LAMIH laboratory to solve several problems inherent in the inflexibility of classic centralized production activity control systems. The distributed approach implies local decision making and real time control for task allocations. This forbids correct forecast on critical information such as time cycle for each manufacturing order, meaning a lack of viability for industrial application. The aim of our work is to provide a structure which takes advantage of both extrema and which is able to support just-in-time concepts: the hybrid production activity control structure. The hybrid approach uses the notion of bottleneck and non-bottleneck resources and is based on the distributed control structure developed in the laboratory. The bottleneck is the only resource scheduled. It forces the synchronicity of the whole production structure because of its characteristics. This paper evaluates the relative performances of the three control structures (centralized, distributed and hybrid) through theoretical discussions and practical examples from a simplified industrial case study.

Personal rapid transit in open control framework

Since last decade, the use of safe, comfortable and ecologic vehicles to operate in urban context as an alternative to private car has been required by authorities. Several projects have emerged on on-demand transport with new automated vehicles known as Cybercars or PRTs. In the context of intelligent transportation, where vehicles can be seen as autonomous and decisional entities composing a self-organized system to be controlled, this paper presents the open-control paradigm and gives an example of its instantiation with stigmergic scheme. The open-control concept, developed in our Lab (LAMIH), exhibits the classic explicit control, as well as an innovative type of control called implicit control. The implicit control allows system entities to be influenced via an Optimization Mechanism (OM). After introducing the open-control paradigm, we illustrate one possible implementation based upon a stigmergic approach and applied to a PRTs network. Implementation architecture and first experimental results are reported.

Reactive scheduling of complex system maintenance in a cooperative environment with communication times

This paper focuses on the problem of complex system maintenance in a cooperative environment composed of several decision centers, taking into account communication times. Maintenance activities generate a specific issue for the scheduling of tasks since they identify, dynamically, new tasks, or modify the duration of planned tasks. In addition, when an unexpected event is detected by a decision center, some modifications of the schedule required by this decision center may have consequences on tasks under the control of other decision centers. In fact, some constraints exist between the jobs under the control of different decision centers. Each modification of the execution of a job should be submitted to the approval of each concerned decision center, implying a communication time between all these decision centers. Nevertheless, these communication times may not be compatible with desired modifications. This is why we present an algorithm to ensure rescheduling feasibility, including communication times between decision centers for the maintenance of complex systems in a coordinated environment. Finally, we implement our algorithm on the industrial context of the TGV maintenance in the French railway company.