Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models

2019 ◽  
Author(s):  
Hanzhang Qin ◽  
David Simchi-Levi ◽  
Li Wang
Keyword(s):  
Inventory Control ◽  
Data Driven ◽  
Approximation Schemes ◽  
Control Models ◽  
Joint Pricing

scholarly journals Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models

2019 ◽  
Vol 44 (2) ◽  
pp. 668-692 ◽  
Author(s):  
Wang Chi Cheung ◽  
David Simchi-Levi
Keyword(s):  
Inventory Control ◽  
Sample Average Approximation ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Data Driven ◽  
Approximation Schemes ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Sample Average ◽  
Full Knowledge

We study the classical multiperiod capacitated stochastic inventory control problems in a data-driven setting. Instead of assuming full knowledge of the demand distributions, we assume that the demand distributions can only be accessed through drawing random samples. Such data-driven models are ubiquitous in practice, where the cumulative distribution functions of the underlying random demand are either unavailable or too complex to work with. We consider the sample average approximation (SAA) method for the problem and establish an upper bound on the number of samples needed for the SAA method to achieve a near-optimal expected cost, under any level of required accuracy and prespecified confidence probability. The sample bound is polynomial in the number of time periods as well as the confidence and accuracy parameters. Moreover, the bound is independent of the underlying demand distributions. However, the SAA requires solving the SAA problem, which is #P-hard. Thus, motivated by the SAA analysis, we propose a polynomial time approximation scheme that also uses polynomially many samples. Finally, we establish a lower bound on the number of samples required to solve this data-driven newsvendor problem to near-optimality.

Asymptotic Optimality of Constant-Order Policies in Joint Pricing and Inventory Control Models

2019 ◽  
Author(s):  
Xin Chen ◽  
Alexander Stolyar ◽  
Linwei Xin
Keyword(s):  
Inventory Control ◽  
Asymptotic Optimality ◽  
Control Models ◽  
Joint Pricing

Nonparametric Algorithms for Joint Pricing and Inventory Control with Lost-Sales and Censored Demand

2015 ◽  
Author(s):  
Boxiao Chen ◽  
Xiuli Chao ◽  
Cong Shi
Keyword(s):  
Inventory Control ◽  
Lost Sales ◽  
Joint Pricing

Data-Driven Joint Pricing and Inventory Management Newsvendor Problem

2021 ◽  
pp. 397-403
Author(s):  
Pengxiang Zhou ◽  
Wai Kin Chan
Keyword(s):  
Inventory Management ◽  
Newsvendor Problem ◽  
Data Driven ◽  
Joint Pricing

Typology and literature review on multiple supplier inventory control models

2020 ◽  
Author(s):  
Josef Svoboda ◽  
Stefan Minner ◽  
Man Yao
Keyword(s):  
Literature Review ◽  
Inventory Control ◽  
Control Models

scholarly journals Joint pricing and inventory control with a Markovian demand model

2007 ◽  
Vol 182 (1) ◽  
pp. 113-126 ◽  
Author(s):  
Rui Yin ◽  
Kumar Rajaram
Keyword(s):  
Inventory Control ◽  
Demand Model ◽  
Joint Pricing

Approximation Algorithms for Stochastic Inventory Control Models

2005 ◽  
pp. 306-320 ◽  
Author(s):  
Retsef Levi ◽  
Martin Pál ◽  
Robin Roundy ◽  
David B. Shmoys
Keyword(s):  
Approximation Algorithms ◽  
Inventory Control ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Control Models
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