Bottlenecks with respect to due-time performance in pull serial production lines.
Li, Jingshan, Meerkov, Semyon M. (2000)
Mathematical Problems in Engineering
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Li, Jingshan, Meerkov, Semyon M. (2000)
Mathematical Problems in Engineering
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Daoui, Cherki, Abbad, Mohamed, Tkiouat, Mohamed (2010)
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Roberta Jungblut-Hessel, Brigitte Plateau, William J. Stewart, Bernard Ycart (2001)
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In this paper we present a method to perform fast simulation of large markovian systems. This method is based on the use of three concepts: Markov chain uniformization, event-driven dynamics, and modularity. An application of urban traffic simulation is presented to illustrate the performance of our approach.
Xu, Qing, Batabyal, Amitrajeet A. (2002)
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Ulrich Herkenrath, Radu Theodorescu (1981)
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The aim of this paper is to show that the theory of (generalized) random systems with complete connection may serve as a mathematical framework for learning and adaption. Chapter 1 is of an introductory nature and gives a general description of the problems with which one is faced. In Chapter 2 the mathematical model and some results about it are explained. Chapter 3 deals with special learning and adaption models.
Antoni Donigiewicz (2004)
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Chiang, S.-Y., Kuo, C.-T., Lim, J.-T., Meerkov, S.M. (2000)
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Haifeng Huo, Xian Wen (2021)
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This paper presents a study the risk probability optimality for finite horizon continuous-time Markov decision process with loss rate and unbounded transition rates. Under drift condition, which is slightly weaker than the regular condition, as detailed in existing literature on the risk probability optimality Semi-Markov decision processes, we prove that the value function is the unique solution of the corresponding optimality equation, and demonstrate the existence of a risk probability...
Ball, Frank G., Milne, Robin K., Yeo, Geoffrey F. (2007)
Journal of Applied Mathematics and Decision Sciences
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