121,127 research outputs found
Design of engineering systems in Polish mines in the third quarter of the 20th century
Participation of mathematicians in the implementation of economic projects in
Poland, in which mathematics-based methods played an important role, happened
sporadically in the past. Usually methods known from publications and verified
were adapted to solving related problems. The subject of this paper is the
cooperation between mathematicians and engineers in Wroc{\l}aw in the second
half of the twentieth century established in the form of an analysis of the
effectiveness of engineering systems used in mining. The results of this
cooperation showed that at the design stage of technical systems it is
necessary to take into account factors that could not have been rationally
controlled before. The need to explain various aspects of future exploitation
was a strong motivation for the development of mathematical modeling methods.
These methods also opened research topics in the theory of stochastic processes
and graph theory. The social aspects of this cooperation are also interesting.Comment: 45 pages, 11 figures, 116 reference
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe
Long-Range Correlation Underlying Childhood Language and Generative Models
Long-range correlation, a property of time series exhibiting long-term
memory, is mainly studied in the statistical physics domain and has been
reported to exist in natural language. Using a state-of-the-art method for such
analysis, long-range correlation is first shown to occur in long CHILDES data
sets. To understand why, Bayesian generative models of language, originally
proposed in the cognitive scientific domain, are investigated. Among
representative models, the Simon model was found to exhibit surprisingly good
long-range correlation, but not the Pitman-Yor model. Since the Simon model is
known not to correctly reflect the vocabulary growth of natural language, a
simple new model is devised as a conjunct of the Simon and Pitman-Yor models,
such that long-range correlation holds with a correct vocabulary growth rate.
The investigation overall suggests that uniform sampling is one cause of
long-range correlation and could thus have a relation with actual linguistic
processes
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
Gaussianisation for fast and accurate inference from cosmological data
We present a method to transform multivariate unimodal non-Gaussian posterior
probability densities into approximately Gaussian ones via non-linear mappings,
such as Box--Cox transformations and generalisations thereof. This permits an
analytical reconstruction of the posterior from a point sample, like a Markov
chain, and simplifies the subsequent joint analysis with other experiments.
This way, a multivariate posterior density can be reported efficiently, by
compressing the information contained in MCMC samples. Further, the model
evidence integral (i.e. the marginal likelihood) can be computed analytically.
This method is analogous to the search for normal parameters in the cosmic
microwave background, but is more general. The search for the optimally
Gaussianising transformation is performed computationally through a
maximum-likelihood formalism; its quality can be judged by how well the
credible regions of the posterior are reproduced. We demonstrate that our
method outperforms kernel density estimates in this objective. Further, we
select marginal posterior samples from Planck data with several distinct
strongly non-Gaussian features, and verify the reproduction of the marginal
contours. To demonstrate evidence computation, we Gaussianise the joint
distribution of data from weak lensing and baryon acoustic oscillations (BAO),
for different cosmological models, and find a preference for flat CDM.
Comparing to values computed with the Savage-Dickey density ratio, and
Population Monte Carlo, we find good agreement of our method within the spread
of the other two.Comment: 14 pages, 9 figure
Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results
We apply methods for estimating the algorithmic complexity of sequences to
behavioural sequences of three landmark studies of animal behavior each of
increasing sophistication, including foraging communication by ants, flight
patterns of fruit flies, and tactical deception and competition strategies in
rodents. In each case, we demonstrate that approximations of Logical Depth and
Kolmogorv-Chaitin complexity capture and validate previously reported results,
in contrast to other measures such as Shannon Entropy, compression or ad hoc.
Our method is practically useful when dealing with short sequences, such as
those often encountered in cognitive-behavioural research. Our analysis
supports and reveals non-random behavior (LD and K complexity) in flies even in
the absence of external stimuli, and confirms the "stochastic" behaviour of
transgenic rats when faced that they cannot defeat by counter prediction. The
method constitutes a formal approach for testing hypotheses about the
mechanisms underlying animal behaviour.Comment: 28 pages, 7 figures and 2 table
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