Approximate dynamic programming based on high dimensional model representation

Miroslav Pištěk

Displaying similar documents to “Approximate dynamic programming based on high dimensional model representation”

A fast Lagrangian heuristic for large-scale capacitated lot-size problems with restricted cost structures

Kjetil K. Haugen, Guillaume Lanquepin-Chesnais, Asmund Olstad (2012)

Kybernetika

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In this paper, we demonstrate the computational consequences of making a simple assumption on production cost structures in capacitated lot-size problems. Our results indicate that our cost assumption of increased productivity over time has dramatic effects on the problem sizes which are solvable. Our experiments indicate that problems with more than 1000 products in more than 1000 time periods may be solved within reasonable time. The Lagrangian decomposition algorithm we use does of...

Dynamic approach to optimum synthesis of a four-bar mechanism using a swarm intelligence algorithm

Edgar A. Portilla-Flores, Maria B. Calva-Yáñez, Miguel G. Villarreal-Cervantes, Paola A. Niño Suárez, Gabriel Sepúlveda-Cervantes (2014)

Kybernetika

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This paper presents a dynamic approach to the synthesis of a crank-rocker four-bar mechanism, that is obtained by an optimization problem and its solution using the swarm intelligence algorithm called Modified-Artificial Bee Colony (M-ABC). The proposed dynamic approach states a mono-objective dynamic optimization problem (MODOP), in order to obtain a set of optimal parameters of the system. In this MODOP, the kinematic and dynamic models of the whole system are consider as well as a...

Holt-Winters method with general seasonality

Tomáš Hanzák (2012)

Kybernetika

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The paper suggests a generalization of widely used Holt-Winters smoothing and forecasting method for seasonal time series. The general concept of seasonality modeling is introduced both for the additive and multiplicative case. Several special cases are discussed, including a linear interpolation of seasonal indices and a usage of trigonometric functions. Both methods are fully applicable for time series with irregularly observed data (just the special case of missing observations was...