Approximate dynamic programming for capacity allocation in the service industry
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Publication:439484
DOI10.1016/j.ejor.2011.09.007zbMath1244.90238OpenAlexW3020899234MaRDI QIDQ439484
Hans-Jörg Schütz, Rainer Kolisch
Publication date: 16 August 2012
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2011.09.007
reinforcement learningsemi-Markov decision processservicesapproximate dynamic programmingcapacity allocationhealth care operations
Queues and service in operations research (90B22) Dynamic programming (90C39) Markov and semi-Markov decision processes (90C40)
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Cites Work
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- Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities
- A policy gradient method for semi-Markov decision processes with application to call admission control
- Optimization models for radiotherapy patient scheduling
- Optimal grouping for a nuclear magnetic resonance scanner by means of an open queueing model.
- Reinforcement learning for long-run average cost.
- Simulation-based optimization: Parametric optimization techniques and reinforcement learning
- The theory and practice of revenue management
- Providing radiology health care services to stochastic demand of different customer classes
- Airline Yield Management with Overbooking, Cancellations, and No-Shows
- Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning
- Managing Patient Service in a Diagnostic Medical Facility
- Dynamic Bid Prices in Revenue Management
- Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice
- Dynamic Multipriority Patient Scheduling for a Diagnostic Resource
- The Linear Programming Approach to Approximate Dynamic Programming
- Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery
- Approximate Dynamic Programming
- A Price-Directed Approach to Stochastic Inventory/Routing
- A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees
- On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
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