Probability in the Engineering and Informational Sciences
Pull systems with advance demand information
WSC '05 Proceedings of the 37th conference on Winter simulation
Approximate Solutions of a Dynamic Forecast-Inventory Model
Manufacturing & Service Operations Management
Dynamic modeling and control of supply chain systems: A review
Computers and Operations Research
Manufacturing & Service Operations Management
Optimal production policies with multistage stochastic demand lead times
Probability in the Engineering and Informational Sciences
Transformation of a production/assembly washing machine lines into a lean manufacturing system
WSEAS Transactions on Systems and Control
Strategic Safety Stocks in Supply Chains with Evolving Forecasts
Manufacturing & Service Operations Management
Capacity Rationing in Stochastic Rental Systems with Advance Demand Information
Operations Research
Competition and Cooperation in a Two-Stage Supply Chain with Demand Forecasts
Operations Research
Manufacturing & Service Operations Management
Journal of Intelligent Manufacturing
Approximation algorithms for stochastic inventory control models
IPCO'05 Proceedings of the 11th international conference on Integer Programming and Combinatorial Optimization
Multiresource Allocation Scheduling in Dynamic Environments
Manufacturing & Service Operations Management
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There is a growing consensus that a portfolio of customers with different demand lead times can lead to higher, more regular revenues and better capacity utilization. Customers with positive demand lead times place orders in advance of their needs, resulting inadvance demand information. This gives rise to the problem of finding effective inventory control policies under advance demand information. We show that state-dependent ( s, S) and base-stock policies are optimal for stochastic inventory systems with and without fixed costs. The state of the system reflects our knowledge of advance demand information. We also determine conditions under which advance demand information has no operational value. A numerical study allows us to obtain additional insights and to evaluate strategies to induce advance demand information.