265,486 research outputs found
Reactive scheduling using a multi-agent model: the SCEP framework
Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, andit permits a natural co-habitation between infinite capacity scheduling processes, performedby the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop
A GPU-enabled solver for time-constrained linear sum assignment problems
This paper deals with solving large instances of the Linear Sum Assignment Problems (LSAPs) under realtime constraints, using Graphical Processing Units (GPUs). The motivating scenario is an industrial application for P2P live streaming that is moderated by a central tracker that is periodically solving LSAP instances to optimize the connectivity of thousands of peers. However, our findings are generic enough to be applied in other contexts. Our main contribution is a parallel version of a heuristic algorithm called Deep Greedy Switching (DGS) on GPUs using the CUDA programming language. DGS sacrifices absolute optimality in favor of a substantial speedup in comparison to classical LSAP solvers like the Hungarian and auctioning methods. We show the modifications needed to parallelize the DGS algorithm and the performance gains of our approach compared to a sequential CPU-based implementation of DGS and a mixed CPU/GPU-based implementation of it
Towards homeostatic architecture: simulation of the generative process of a termite mound construction
This report sets out to the theme of the generation of a ‘living’,
homeostatic and self-organizing architectural structure. The main research
question this project addresses is what innovative techniques of design,
construction and materials could prospectively be developed and eventually
applied to create and sustain human-made buildings which are mostly
adaptive, self-controlled and self-functioning, without option to a vast supply
of materials and peripheral services. The hypothesis is that through the
implementation of the biological building behaviour of termites, in terms of
collective construction mechanisms that are based on environmental stimuli,
we could achieve a simulation of the generative process of their adaptive
structures, capable to inform in many ways human construction. The essay
explicates the development of the 3-dimensional, agent-based simulation of
the termite collective construction and analyzes the results, which involve
besides physical modelling of the evolved structures. It finally elucidates the
potential of this emerging and adaptive architectural performance to be
translated to human practice and thus enlighten new ecological engineering
and design methodologies
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