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Dynamic Inventory and Price Controls Involving Unknown Demand on Discrete Nonperishable Items

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Abstract: We study adaptive policies that handle dynamic inventory and price controls when the random demand for discrete nonperishable items is unknown. Pure inventory control is achieved by targeting newsvendor ordering quantities that correspond to empirical demand distributions learned over time. On this basis we conduct the more complex joint inventory-price control, where demand-affecting prices await to be evaluated as well. We identify policies that strive to balance between exploration and exploitation, and measure their performances via regrets, i.e., the prices to pay for not knowing demand distributions a priori over a given horizon. Multiple bounds are derived on regrets growth rates; they vary with how thoroughly unknown the demand distributions are and whether nonperishability has indeed been accounted for. Our simulation study illustrates order-of-magnitude differences between pure inventory and joint inventory-price controls in terms of regret growth rates.

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