125,303 research outputs found
A linear programming-based method for job shop scheduling
We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation
completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach
Independent Resampling Sequential Monte Carlo Algorithms
Sequential Monte Carlo algorithms, or Particle Filters, are Bayesian
filtering algorithms which propagate in time a discrete and random
approximation of the a posteriori distribution of interest. Such algorithms are
based on Importance Sampling with a bootstrap resampling step which aims at
struggling against weights degeneracy. However, in some situations (informative
measurements, high dimensional model), the resampling step can prove
inefficient. In this paper, we revisit the fundamental resampling mechanism
which leads us back to Rubin's static resampling mechanism. We propose an
alternative rejuvenation scheme in which the resampled particles share the same
marginal distribution as in the classical setup, but are now independent. This
set of independent particles provides a new alternative to compute a moment of
the target distribution and the resulting estimate is analyzed through a CLT.
We next adapt our results to the dynamic case and propose a particle filtering
algorithm based on independent resampling. This algorithm can be seen as a
particular auxiliary particle filter algorithm with a relevant choice of the
first-stage weights and instrumental distributions. Finally we validate our
results via simulations which carefully take into account the computational
budget
Optimum non linear binary image restoration through linear grey-scale operations
Non-linear image processing operators give excellent results in a number of image processing tasks such as restoration and object recognition. However they are frequently excluded from use in solutions because the system designer does not wish to introduce additional hardware or algorithms and because their design can appear to be ad hoc. In practice the median filter is often used though it is rarely optimal. This paper explains how various non-linear image processing operators may be implemented on a basic linear image processing system using only convolution and thresholding operations. The paper is aimed at image processing system developers wishing to include some non-linear processing operators without introducing additional system capabilities such as extra hardware components or software toolboxes. It may also be of benefit to the interested reader wishing to learn more about non-linear operators and alternative methods of design and implementation. The non-linear tools include various components of mathematical morphology, median and weighted median operators and various order statistic filters. As well as describing novel algorithms for implementation within a linear system the paper also explains how the optimum filter parameters may be estimated for a given image processing task. This novel approach is based on the weight monotonic property and is a direct rather than iterated method
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