Production-inventory systems with imperfect advance demand information and updating
Article first published online: 3 FEB 2011
DOI: 10.1002/nav.20443
Copyright © 2011 Wiley Periodicals, Inc.
Additional Information
How to Cite
Benjaafar, S., Cooper, W. L. and Mardan, S. (2011), Production-inventory systems with imperfect advance demand information and updating. Naval Research Logistics, 58: 88–106. doi: 10.1002/nav.20443
Publication History
- Issue published online: 18 FEB 2011
- Article first published online: 3 FEB 2011
- Manuscript Accepted: 16 NOV 2010
- Manuscript Revised: 27 OCT 2010
- Manuscript Received: 19 SEP 2008
Keywords:
- advance demand information;
- production-inventory systems;
- make-to-stock queues;
- continuous-time Markov decision processes
Abstract
We consider a supplier with finite production capacity and stochastic production times. Customers provide advance demand information (ADI) to the supplier by announcing orders ahead of their due dates. However, this information is not perfect, and customers may request an order be fulfilled prior to or later than the expected due date. Customers update the status of their orders, but the time between consecutive updates is random. We formulate the production-control problem as a continuous-time Markov decision process and prove there is an optimal state-dependent base-stock policy, where the base-stock levels depend upon the numbers of orders at various stages of update. In addition, we derive results on the sensitivity of the state-dependent base-stock levels to the number of orders in each stage of update. In a numerical study, we examine the benefit of ADI, and find that it is most valuable to the supplier when the time between updates is moderate. We also consider the impact of holding and backorder costs, numbers of updates, and the fraction of customers that provide ADI. In addition, we find that while ADI is always beneficial to the supplier, this may not be the case for the customers who provide the ADI. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

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