Approximation Algorithms for Stochastic Inventory Control Models

Approximation Algorithms for Stochastic Inventory Control Model

Cost-based filtering for stochastic inventory control

Abstract. An interesting class of production/inventory control problems considers a single product and a single stocking location, given a stochastic demand with a known non-stationary probability distribution. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using a cost-based filtering method. Our algorithm can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all.

Strategic inventories under limited commitment

In a dynamic storable good market where demand changes over time, we investigate the producer's strategic incentives to hold inventories in response to the possibility of buyer stockpiling. The literature on storable goods has demonstrated that buyer stockpiling in anticipation of higher future prices harms the producer's profitability, particularly when the producer cannot commit to future prices. We show that the producer's inventories act as a strategic device to mitigate the loss from the lack of commitment. Our results provide a rationale for the producer's inventory behavior that sheds new light on the well-documented empirical evidence about inventories

Base stock inventory systems with compound poisson demand: case of partial lost sales

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