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Hordijk, A and Kallenberg, LCM (1979) Linear programming and Markov decision chains Management Sci, 25, 352 362 Hordijk, A and Kallenberg, LCM (1984) Constrained undiscounted stochastic dynamic programming Math Operat Res, 9, 276 289 Howard, RA (1960) Dynamic Programming and Markov Processes John Wiley & Sons, Inc, New York Kawai, H (1983) An optimal ordering and replacement policy of a Markovian degradation system under complete observation, part I J Operat Res Soc Japan, 26, 279 290 Kolderman, J and Volgenant, A (1985) Optimal claiming in an automobile insurance system with bonus-malus structure J Operat Res Soc, 36, 239 247 MacQueen, J (1966) A modi ed dynamic programming method for Markovian decision problems J Math Appl Math, 14, 38 43 Manne, A (1960) Linear programming and sequential decisions Management Sci, 6, 259 267 Norman, JM and Shearn, DCS (1980) Optimal claiming on vehicle insurance revisited J Operat Res Soc, 31, 181 186 Odoni, A (1969) On nding the maximal gain for Markov decision processes Operat Res, 17, 857 860 Popyack, JL, Brown, RL and White, CC III (1979) Discrete versions of an algorithm due to Varaiya IEEE Trans Automat Contr, 24, 503 504 Puterman, ML (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming John Wiley & Sons, Inc, New York Schweitzer, PJ and Federgruen, A (1979) Geometric convergence of value iteration in multichain Markov decision problems Adv Appl Prob, 11, 188 217 Sennott, LI (1999) Stochastic Dynamic Programming and the Control of Queueing Systems John Wiley & Sons, Inc, New York Stengos, D and Thomas, LC (1980) The blast furnaces problem Eur J Operat Res, 4, 330 336 Su, Y and Deininger, R (1972) Generalization of White s method of successive approximations to periodic Markovian decision processes Operat Res, 20, 318 326 Tijms, HC and Van der Duyn Schouten, FA (1985) A Markov decision algorithm for optimal inspections and revisions in a maintenance system with partial information Eur J Operat Res, 21, 245 253 Van der Wal, J (1980) The method of value oriented successive approximations for the average reward Markov decision process OR Spektrum, 1, 233 242 Veinott, AF Jr (1966) On nding optimal policies in discrete dynamic programming with no discounting Ann Math Statist, 37, 1284 1294 Wagner, HM (1975) Principles of Operations Research, 2nd edn Prentice Hall, Englewood Cliffs NJ White, DJ (1963) Dynamic programming, Markov chains nd the method of successive approximations J Math Anal Appl, 6, 373 376 White, DJ (1985) Real applications of Markov decision processes Interfaces, 15, no 6, 73 78.

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showing that P {N1 (t) = k, N2 (t) = m} = P {N1 (t) = k}P {N2 (t) = m} The remarkable result ( 115) states that the next arrival is of type i with robability i /( 1 + 2 ) regardless of how long it takes until the next arrival This result is characteristic for competing Poisson processes which are independent of each other As an illustration, suppose that long-term parkers and short-term parkers arrive at a parking lot according to independent Poisson processes with respective rates 1 and 2 Then the merged arrival process of parkers is a Poisson process with rate 1 + 2 and the probability that a newly arriving parker is a long-term parker equals 1 /( 1 + 2 ) Example 112 A stock problem with substitutable products A store has a leftover stock of Q1 units of product 1 and Q2 units of product 2 Both products are taken out of production Customers asking for product 1 arrive according to a Poisson process with rate 1 Independently of this process, customers asking for product 2 arrive according to a Poisson process with rate 2 Each customer asks for one unit of the concerning product The two products serve as substitute for each other, that is, a customer asking for a product that is sold out is satis ed with the other product when still in stock What is the probability distribution of the time until both products are sold out What is the probability that product 1 is sold out before product 2 To answer the rst question, observe that both products are sold out as soon as Q1 + Q2 demands have occurred The aggregated demand process is a Poisson process with rate 1 + 2 Hence the time until both products are sold out has an Erlang (Q1 + Q2 , 1 + 2 ) distribution To answer the second question, observe that product 1 is sold out before product 2 only if the rst Q1 + Q2 1 aggregated demands have no more than Q2 1 demands for product 2 Hence, by (115), the desired probability is given by.

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PARALLEL SYSTEMS AND PROGRAMMING. Encoding Bar Code In . Making DataMatrix In Java Using Barcode encoder for Java . and to static networks Instead of the mean degree (Section 221), the maximum number of links per switch is given Bus One particular dynamic network, the us, deserves special attention, since it is found in many small-sized parallel systems and also as a building block in larger scale computers Conceptually, it is quite different from the dynamic networks analyzed so far The units of the parallel system are grouped around the bus, which is shared among them for their communications, as shown in Figure 213 A controller assigns the bus to a unit on request and during the assigned time the unit can use the bus exclusively In terms of performance scalability, it is situated on the opposite side of the spectrum, when compared with the crossbar Already one communication between two processors blocks the bus for any other connection As a consequence, the bus is usually time-shared among the connected units in practice While its simplicity makes the bus very attractive, it very quickly becomes the performance bottleneck of a parallel system Consequently, the number of connected units is small (usually 8).Related: .NET WinForms EAN-8 Generator , ISBN Generation VB.NET , UPC-E Generator .NET WinForms

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