Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
How to dynamically merge Markov decision processes
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Scheduling to minimize average completion time: off-line and on-line algorithms
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
A Meta-Heuristic Factory for Vehicle Routing Problems
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Revenue Management: Research Overview and Prospects
Transportation Science
Approximation Schemes for Minimizing Average Weighted Completion Time with Release Dates
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Dynamic Programming
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Introduction to Stochastic Programming
Introduction to Stochastic Programming
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Certainty closure: Reliable constraint reasoning with incomplete or erroneous data
ACM Transactions on Computational Logic (TOCL)
Waiting and relocation strategies in online stochastic vehicle routing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Discrete Applied Mathematics
Self-adaptive middleware: Supporting business process priorities and service level agreements
Advanced Engineering Informatics
Synthesizing filtering algorithms for global chance-constraints
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Evolving parameterised policies for stochastic constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Online stochastic reservation systems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Filtering algorithms for global chance constraints
Artificial Intelligence
Approximability of the two-stage stochastic knapsack problem with discretely distributed weights
Discrete Applied Mathematics
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Constraint Programming (CP) is a very general programming paradigm that proved its efficiency on solving complex industrial problems. Most real-life problems are stochastic in nature, which is usually taken into account through different compromises, such as applying a deterministic algorithm to the average values of the input, or performing multiple runs of simulation. Our goal in this paper is to analyze different techniques taken either from practical CP applications or from stochastic optimization approaches. We propose a benchmark issued from our industrial experience, which may be described as an Online Multi-choice Knapsack with Deadlines. This benchmark is used to test a framework with four different dynamic strategies that utilize a different combination of the stochastic and combinatorial aspects of the problem. To evaluate the expected future state of the reservations at the time horizon, we either use simulation, average values, systematic study of the most probable scenarios, or yield management techniques.