Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

Rui Wang, Xianghua Gan, Qing Li, Xiao Yan and Abd E.I.-Baset Hassanien

Complexity, 2021, vol. 2021, 1-17

Abstract: We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension†is circumvented.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6643131

DOI: 10.1155/2021/6643131

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