Dynamic pricing and inventory control with nonparametric demand learning

Chung, Byung Do; Li, Jiahan; Yao, Tao

doi:10.1504/ijsoi.2011.041421

Cited by 2 publications

(1 citation statement)

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“…The extension to the parametric case (the firm knows the class of distribution but not the parameters) has been studied by, for example, Subrahmanyan and Shoemaker (1996), Petruzzi and Dada (2002), and Zhang and Chen (2006). Chung et al (2011) also consider the problem of dynamic pricing and inventory planning with demand learning, and they develop learning algorithms using Bayesian method and Markov chain Monte Carlo (MCMC) algorithms, and numerically evaluate the importance of dynamic pricing. An alternative to the parametric approach is to model the firm's problem in a nonparametric setting.…”

Section: Related Literaturementioning

confidence: 99%

Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning

2015

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We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity, but it knows neither the form of demand dependency on selling price nor the distribution of demand uncertainty a priori, hence it has to make pricing and ordering decisions based on historical demand data. We propose a nonparametric data-driven policy that learns about the demand on the fly and, concurrently, applies learned information to determine replenishment and pricing decisions. The policy integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with the least possible profit loss. Besides convergence of optimal policies, we show that the regret, defined as the average profit loss compared with that of the optimal solution when the firm has complete information about the underlying demand, vanishes at the fastest possible rate as the planning horizon increases.

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