Naval Research Logistics (NRL) · 2011 · 53 citations · 38 references
ManagementOperations ManagementSupply Chain ManagementInventory ControlInventory ManagementMarkov Decision ProcessesInventory Control PoliciesInventory OptimizationInventory Replenishment SystemsInventory TheoryQueueing TheoryStochastic Inventory ModelsContinuous-time Markov ChainsContinuous-time Markov ProcessesDue DatesInventory Control Under Demand UncertaintyInventory Control Under UncertaintyInventory Management Under UncertaintyProduction-inventory SystemsState-dependent Inventory Policies
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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Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues
John Odentrantz · Technometrics · 2000 · 1K citations