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The paper summarizes the results of research on the modeling and implementation of advanced planning and scheduling (APS) systems done in recent twenty years. It discusses the concept of APS system – how it is thought of today – and highlights the modeling and implementation challenges with which the developers of such systems should cope. Some from these challenges were identified as a result of the study of scientific literature, others – through an in-depth analysis of the experience gained during the development of real-world APS system – a Production Efficiency Navigator (PEN system). The paper contributes to APS systems theory by proposing the concept of an ensemble of collaborating algorithms.
The paper proposes a novel predictive–reactive planning and scheduling framework in which both approaches are combined in order to complement each other in a reasonably balanced way. It proposes neither any original scheduling algorithms nor techniques. It also not aims to invent some new mechanisms or to propose some cardinally new ideas. The aim is to chose, adapt and test ideas, mechanisms and algorithms already proposed by other researchers. The focus of research is set to make-to-order production environments. The proposed approach aims not only to absorb disruptions in shop floor level schedules but also to mitigate the impacts of potential exceptions disrupting mid-term level production plans. It is based on application of risk mitigation techniques and combines various simulation techniques expanded by optimization procedures. The proposed approach is indented to be applied by an Advanced Planning and Scheduling system implemented as add-on for an Enterprise Resources Planning system. To make it easier to understand the focus of the paper, we clarify at the beginning the position from which we start.
We have established virtual exhibitions on the web to preserve and present displays. Now, we would like to present objects and exhibits in a way that it does not only reflect museologists’ points of view of visiting the showcases but, with the help of cameras, we are following the regular visitors’ movements in the physical space and can make the classification of them. With this system, we will be able to produce visits of a virtual exhibition that will be very close to real-life personal visits of an exhibition as there can be various stopover and direction selections reproduced that provide the same experience and feeling as in the course of a real visit.
Although object tracking solutions based on signal detection or sensor network have already been long known, these are only able to describe the direction and speed of the movement, although sometimes even with cm exactitude. Meanwhile they are not capable of providing information related to the direction of the sight of a person whose behaviour/movement is tracked, i.e. to what extent is the course he/she is following determined by an actual endpoint i.e. the destination and by any other circumstances. There have been several such researches conducted by human observers, but excessive number of such observations would be very costly. There is another problem inherent in former methods: track segments describing continuous movement in spaces that due to obstacles and overlays could be watched by several cameras should afterwards be manually consolidated. As to our knowledge, currently accessible solutions are not capable to identify completely the space sections covered by a person's sight. In case video recordings could be processed by automated systems, the observation of a sufficiently large number of cases would enable the investigation of the interaction between the actual course of movement and the view of the environment; research results could directly be obtained on several aspects of spatial orientation. Our objective is to design and develop an experimental, multimedia-aided IT system that is capable of processing video camera recordings and thereby, in a highly automated way, distinguish observed objects and persons, watch and describe their spatial movements and calculate statistical distributions from such descriptions. With the help of the descriptions of spatial and time coordinates of tracks, we could detect regularities and irregularities intrinsic in tracks followed, in separation/grouping of the observed objects or persons. In the present article, the architecture of the planned system is described. The recording tool of the experimental system is a stereo camera system producing two time-synchronized video streams. With the appropriate optics, lighting and arrangement, this system is capable to continuously distinguish objects moving simultaneously and to provide instantaneous information appropriate for measuring the actual spatial location of the objects. The image analysing module identifies objects to be observed, identifies their actual position and records their places relative to each other in a track curve database. The track analyser module contains the procedures for the classification and analysis of movement curves. Finally, we are presenting an initial application carried out by the experimental system where children could be studied from the aspect of communal behaviour, track-following/target orientation and efficiency.
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