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

Author:

Wang Rui1ORCID,Gan Xianghua1ORCID,Li Qing2ORCID,Yan Xiao1ORCID

Affiliation:

1. School of Business Administration, The Southwestern University of Finance and Economics, Chengdu, Sichuan, China

2. China Construction Bank, Hengshui Branch, Hengshui, Hebei, China

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.

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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