LEADER 05480nam 2200673Ia 450 001 9910145768203321 005 20170809152543.0 010 $a1-282-30800-9 010 $a9786612308000 010 $a0-470-31703-5 010 $a0-470-31787-6 035 $a(CKB)1000000000013693 035 $a(EBL)469893 035 $a(OCoLC)658055610 035 $a(SSID)ssj0000343261 035 $a(PQKBManifestationID)11280696 035 $a(PQKBTitleCode)TC0000343261 035 $a(PQKBWorkID)10290927 035 $a(PQKB)11274139 035 $a(MiAaPQ)EBC469893 035 $a(PPN)159315867 035 $a(EXLCZ)991000000000013693 100 $a19980128d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStochastic dynamic programming and the control of queueing systems$b[electronic resource] /$fLinn I. Sennott 210 $aNew York $cJohn Wiley Sons$dc1999 215 $a1 online resource (354 p.) 225 1 $aWiley series in probability and statistics. Applied probability and statistics section 300 $a"A Wiley-Interscience publication." 311 $a0-471-16120-9 320 $aIncludes bibliographical references (p. 316-323) and index. 327 $aStochastic Dynamic Programming and the Control of Queueing Systems; Contents; Preface; 1. Introduction; 1.1. Examples; 1.2. Aspects of Control; 1.3. Goals and Summary of Chapters; Bibliographic Notes; Problems; 2. Optimization Criteria; 2.1. Basic Notation; 2.2. Policies; 2.3. Conditional Cost Distributions; 2.4. Optimization Criteria; 2.5. Approximating Sequence Method; Bibliographic Notes; Problems; 3. Fiite Horizon Optimization; 3.1. Finite Horizon Optimality Equation; 3.2. ASM for the Finite Horizon; 3.3. When Does FH(?, n) Hold?; 3.4. A Queueing Example; Bibliographic Notes; Problems 327 $a4. Lnfinite Horizon Discounted Cost Optimization4.1 Infinite Horizon Discounted Cost Optimality Equation; 4.2 Solutions to the Optimality Equation; 4.3 Convergence of Finite Horizon Value Functions; 4.4 Characterization of Optimal Policies; 4.5 Analytic Properties of the Value Function; 4.6 ASM for the Infinite Horizon Discounted Case; 4.7 When Does DC(?) HOLD?; Bibliographic Notes; Problems; 5. An inventory Model; 5.1. FomuIation of the MDC; 5.2. Optimality Equations; 5.3. An Approximating Sequence; 5.4. Numerical Results; Bibliographic Notes; Problems 327 $a6 Average Cost Optimization for Finite State Spaces6.1. A Fundamental Relationship for S Countable; 6.2. An Optimal Stationary Policy Exists; 6.3. An Average Cost Optimality Equation; 6.4. ACOE for Constant Minimum Average Cost; 6.5. Solutions to the ACOE; 6.6 Method of Calculation; 6.7. An Example; Bibliographic Notes; Problems; 7. Average Cost Optimization Theory for Countable State Spaces; 7.1. Counterexamples; 7.2. The (SEN) Assumptions; 7.3. An Example; 7.4. Average Cost Optimality Inequality; 7.5. Sufficient Conditions for the (SEN) Assumptions; 7.6. Examples 327 $a7.7. Weakening the (SEN) AssumptionsBibliographic Notes; Problems; 8. Computation of Average Cost Optimal Policies for Infinite State Spaces; 8.1. The (AC) Assumptions; 8.2. Verification of the Assumptions; 8.3. Examples; *8.4. Another Example; 8.5. Service Rate Control Queue; 8.6. Routing to ParalleI Queues; 8.7. Weakening the (AC) Assumptions; Bibliographic Notes; Problems; 9. Optimization Under Actions at Selected Epochs; 9.1. Single- and Multiple-Sample Models; 9.2. Properties of an MS Distribution; 9.3. Service Control of the Single-Server Queue 327 $a9.4. Arrival Control of the Single-Server Queue9.5. Average Cost Optimization of Example 9.3.1; 9.6. Average Cost Optimization of Example 9.3.2; 9.7. Computation Under Deterministic Service Times; 9.8. Computation Under Geometric Service Times; Bibliographic Notes; Problems; 10. Average Cost Optimization of Continuous Time Processes; 10.1. Exponential Distributions and the Poisson Process; 10.2. Continuous Time Markov Decision Chains; 10.3. Average Cost Optimization of a CTMDC; 10.4. Service Rate Control of the M/M/l Queue,; 10.5. MW/K Queue with Dynamic Service Pool 327 $a10.6. Control of a Polling System 330 $aA path-breaking account of Markov decision processes-theory and computationThis book's clear presentation of theory, numerous chapter-end problems, and development of a unified method for the computation of optimal policies in both discrete and continuous time make it an excellent course text for graduate students and advanced undergraduates. Its comprehensive coverage of important recent advances in stochastic dynamic programming makes it a valuable working resource for operations research professionals, management scientists, engineers, and others.Stochastic Dynamic Programmi 410 0$aWiley series in probability and statistics.$pApplied probability and statistics. 606 $aStochastic programming 606 $aDynamic programming 606 $aQueuing theory 615 0$aStochastic programming. 615 0$aDynamic programming. 615 0$aQueuing theory. 676 $a519.703 676 $a519.82 700 $aSennott$b Linn I.$f1943-$0985041 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145768203321 996 $aStochastic dynamic programming and the control of queueing systems$92250833 997 $aUNINA