Dernière sortie
Dynamic Pricing and Revenue Management - A Control-Theoretic Approach
Bridging advanced control theory, operations research, and high-performance distributed architecture. In modern digital commerce, algorithmic brilliance musters little success unless paired with execution speed. Traditional static pricing models and nightly batch spreadsheets are no longer enough to survive volatile, high-frequency markets. Dynamic Pricing and Revenue Management: A Control-Theoretic Approach provides the comprehensive mathematical machinery and architectural blueprint needed to design autonomous, self-correcting commercial systems.
Written for engineers, data scientists, operations researchers, and advanced students, this book bridges the gap between classical optimal control-such as Hamilton-Jacobi-Bellman equations and Markov Decision Processes-and modern machine learning, multi-armed bandits, and low-latency distributed microservices. What You Will Learn: Continuous-Time & Stochastic Control: Master calculus of variations, HJB equations, and stochastic dynamic programming for perishable inventory and expiration dynamics.
Online Learning & Bandits: Explore multi-armed bandits, contextual bandits, and reinforcement learning strategies for pricing in unmapped, non-stationary markets. Network Yield & Fulfillment Integration: Connect pricing algorithms directly to physical supply chains, multi-echelon routing, lead-time uncertainty, and linear programming shadow prices. Real-Time Engineering at Scale: Architect high-frequency pricing pipelines, distributed dynamic programming clusters, stream-processing demand sensing, and worst-case robust optimization defenses.
Real-World Case Studies & Frontiers: Analyze implementation blueprints from e-commerce, airline yield management, ride-hailing surge pricing, synthetic market sandboxes, and autonomous multi-agent commerce. Whether you are building automated pricing engines from scratch or scaling enterprise supply chain infrastructure, this book equips you with the mental models and technical execution frameworks required to thrive in the era of algorithmic commerce.
Written for engineers, data scientists, operations researchers, and advanced students, this book bridges the gap between classical optimal control-such as Hamilton-Jacobi-Bellman equations and Markov Decision Processes-and modern machine learning, multi-armed bandits, and low-latency distributed microservices. What You Will Learn: Continuous-Time & Stochastic Control: Master calculus of variations, HJB equations, and stochastic dynamic programming for perishable inventory and expiration dynamics.
Online Learning & Bandits: Explore multi-armed bandits, contextual bandits, and reinforcement learning strategies for pricing in unmapped, non-stationary markets. Network Yield & Fulfillment Integration: Connect pricing algorithms directly to physical supply chains, multi-echelon routing, lead-time uncertainty, and linear programming shadow prices. Real-Time Engineering at Scale: Architect high-frequency pricing pipelines, distributed dynamic programming clusters, stream-processing demand sensing, and worst-case robust optimization defenses.
Real-World Case Studies & Frontiers: Analyze implementation blueprints from e-commerce, airline yield management, ride-hailing surge pricing, synthetic market sandboxes, and autonomous multi-agent commerce. Whether you are building automated pricing engines from scratch or scaling enterprise supply chain infrastructure, this book equips you with the mental models and technical execution frameworks required to thrive in the era of algorithmic commerce.
