An efficient learning framework for multiproduct inventory systems with customer choices.
Gao, Xiangyu; Zhang, Huanan. An efficient learning framework for multiproduct inventory systems with customer choices. Production & Operations Management. Jun2022, Vol. 31 Issue 6, p2492-2516.泭泭泭泭
We consider a periodicreview multiproduct inventory system where customers' purchasing decisions are affected by the product availabilities. Demands need to be learned on the fly, through the partial and censored feedback of customers. For this learning problem, if one ignores the inventory dynamic and treats it as a multiarmed bandit problem and directly applies some existing algorithms, for example, the upper confidence bound (UCB) algorithm, the convergence can be extremely slow due to the highdimensionality of the policy space. We propose a UCBbased learning framework that utilizes the sales information based on two improvement ideas. We illustrate how these two ideas can be incorporated by considering two specific systems: (1) multiproduct inventory system with stockout substitutions, (2) multiproduct inventory assortment problem for urban warehouses. We develop improved UCB algorithms for both systems, using the two improvements. For both systems, the algorithm can achieve a tight worstcase convergence rate (up to a logarithmic term) on the planning horizon T$T$. Extensive numerical experiments are conducted to demonstrate the efficiency of the improved UCB algorithms for the two systems. In the experiments, when there are more than 1000 candidate policies to choose from, the algorithms can achieve around 15%$15\%$ average expected regret within 50 periods and continue to steadily improve as time increases.泭泭泭泭
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