Stochastic Modeling and Optimization: With Applications in Queues, Finance, and Supply Chains
David D. Yao Β· Hanqin Zhang Β· Xun Yu Zhou
Π΄Π΅ΠΊ. 2012β―Π³. Β· Springer Science & Business Media
ΠΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ ΠΊΠ½ΠΈΠ³Π°
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ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΡΡΠ°Π½ΠΈΡ
reportΠΡΠ΅Π½ΠΊΠΈ ΠΈ ΠΎΡΠ·ΡΠ²Ρ Π½Π΅ ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Ρ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅β¦
ΠΠ± ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΊΠ½ΠΈΠ³Π΅
The objective of this volume is to highlight through a collection of chap ters some of the recent research works in applied prob ability, specifically stochastic modeling and optimization. The volume is organized loosely into four parts. The first part is a col lection of several basic methodologies: singularly perturbed Markov chains (Chapter 1), and related applications in stochastic optimal control (Chapter 2); stochastic approximation, emphasizing convergence properties (Chapter 3); a performance-potential based approach to Markov decision program ming (Chapter 4); and interior-point techniques (homogeneous self-dual embedding and central path following) applied to stochastic programming (Chapter 5). The three chapters in the second part are concerned with queueing the ory. Chapters 6 and 7 both study processing networks - a general dass of queueing networks - focusing, respectively, on limit theorems in the form of strong approximation, and the issue of stability via connections to re lated fluid models. The subject of Chapter 8 is performance asymptotics via large deviations theory, when the input process to a queueing system exhibits long-range dependence, modeled as fractional Brownian motion.
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Π§ΡΠΎΠ±Ρ ΠΎΡΠΊΡΡΡΡ ΠΊΠ½ΠΈΠ³Ρ Π½Π° ΡΠ°ΠΊΠΎΠΌ ΡΡΡΡΠΎΠΉΡΡΠ²Π΅ Π΄Π»Ρ ΡΡΠ΅Π½ΠΈΡ, ΠΊΠ°ΠΊ Kobo, ΡΠΊΠ°ΡΠ°ΠΉΡΠ΅ ΡΠ°ΠΉΠ» ΠΈ Π΄ΠΎΠ±Π°Π²ΡΡΠ΅ Π΅Π³ΠΎ Π½Π° ΡΡΡΡΠΎΠΉΡΡΠ²ΠΎ. ΠΠΎΠ΄ΡΠΎΠ±Π½ΡΠ΅ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΠΈ ΠΌΠΎΠΆΠ½ΠΎ Π½Π°ΠΉΡΠΈ Π² Π‘ΠΏΡΠ°Π²ΠΎΡΠ½ΠΎΠΌ ΡΠ΅Π½ΡΡΠ΅.
