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101 0 $aeng
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181   $ctxt
182   $cc
183   $acr
200 10$aProbabilistic models in operations research /$fSilviu Guiasu
205   $a1st ed.
210   $aNew York $cNova Science Publishers$dc2009
215   $a1 online resource (300 p.)
300   $aDescription based upon print version of record.
311 08$a1-60692-233-5 
320   $aIncludes bibliographical references (p. 286-288) and index.
327   $aIntro -- PROBABILISTIC MODELS INOPERATIONS RESEARCH -- PROBABILISTIC MODELS INOPERATIONS RESEARCH -- Contents -- INTRODUCTION -- Chapter 1. GAME THEORY -- ABSTRACT -- 1. Generalities -- 2. Pure strategies -- 3. Mixed strategies -- 4. Graphical method -- 5. Brown's algorithm -- 6. The linear programs induced by a two-person zero-sum game -- 7. Computer tips -- 1. LINDO. -- 2. SAS-OR. -- 3. EXCEL. -- 4. MATHEMATICA. -- 8. Prisoner's dilemma -- 9. n-person games -- Chapter 2. DECISION THEORY -- ABSTRACT -- 1. Generalities -- 2. Oil-drilling example -- 3. Decision without experimentation -- 4. Decision with experimentation -- 5. Risk function -- 6. Computer tips -- 1 FORTRAN. -- 2. MATHEMATICA. -- 7. Multiple-stage decision -- A variant of the decision tree problem. -- Chapter 3. SIMULATION -- ABSTRACT -- 1. Generalities -- 2. Minitab -- 3. Random numbers -- 4. Sampling from discrete probability distributions -- 5. Sampling from continuous probability distributions -- 6. Monte Carlo method -- Chapter 4. RELIABILITY -- ABSTRACT -- 1. Structure function -- 2. Exact reliability -- 3. Bounds for reliability -- 4. Lifetime -- Chapter 5. QUEUEING SYSTEMS -- ABSTRACT -- 1. Generalities -- 2. Steady state condition -- 3. M=M=1 with constant mean arrival rate and constant meanservice rate -- 4. M=M=s with constant mean arrival rate and constant meanservice rate per busy server -- 5. Waiting time -- 6. Finite queue -- 7. Finite input source -- 8. Other queueing systems -- 9. Number of arrivals during the time-interval [0 -- t] -- 10. Computer tips -- 11. Simulation -- APPENDIX -- The dual theorem -- References -- Index.
330   $aOperations Research, often abbreviated as OR, is a branch of Applied Mathematics. Its aim is to optimise mathematical models of reality involving probabilistic uncertainty and its results are applied to real life problems. This book intends to cover the main chapter of the OR probablisitc models based on optimisation.
606   $aOperations research
606   $aProbabilities
615  0$aOperations research.
615  0$aProbabilities.
700   $aGuiasu$b Silviu$026318
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910959671203321
996   $aProbabilistic models in operations research$94481243
997   $aUNINA