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Dynamic pricing and inventory control with learning. (English) Zbl 0991.90006

The authors determine optimal pricing and stocking policies over time for a monopolist when a given market parameter is initially unknown, and finally revealed through learning. They find that the first-period optimal price increases with the length of the planning horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Moreover, they provide the characterization of the optimal stocking quantity decision, and a computational algorithm to obtain the optimal solution.
As stated in the concluding comments, in reality, the “randomness is incorporated in \(\varepsilon\) such that the probabilistic nature of the problem does not end at the first occurrence of leftovers.” In addition, they present a dynamic model that simultaneously links price, learning from sales, and inventory. However, there is no inventory cost involved. Consequently, I conclude that it is an interesting and relevant basic research paper. However, it needs more further study to make it applicable in the real world.

MSC:

90B05 Inventory, storage, reservoirs
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