Dynamic Inventory Control with Fixed Setup Costs and Unknown Discrete Demand Distribution

2019 ◽  
Author(s):  
Mehdi Davoodi ◽  
Michael N. Katehakis ◽  
Jian Yang
Keyword(s):  
Inventory Control ◽  
Setup Costs ◽  
Demand Distribution

Simulation of Inventory Control System in a Supply Chain Using RFID

2010 ◽  
Vol 3 (1) ◽  
pp. 68-86 ◽  
Author(s):  
Ibrahim Al Kattan ◽  
Taha Al Khudairi
Keyword(s):  
Supply Chain ◽  
Control System ◽  
Supply Chain Management ◽  
Simulation Model ◽  
Inventory Control ◽  
Radio Frequency Identification ◽  
Unit Cost ◽  
Simulation Software ◽  
Chain Management ◽  
Demand Distribution

This paper employs a simulation model in a Supply Chain Management (SCM) system. This study is one of the first to present simulation model of inventory control system in supply chain management using barcode and Radio Frequency Identification (RFID). The main objective of this model is to compare two inventory systems in a supply chain, one using RFID, versus the barcode. The model will help company to consider moving from a barcode system to the RFID application. A quantitative analysis based on a simulation model is developed. The model runs for both systems using ARENA simulation software with a comparison between the two systems. Furthermore, the simulation model is tested by applying three different types of demand for both scenarios. The results have shown that regardless of demand distribution pattern and customer order rate, the outcomes of the model are consistent and provide promising RFID technology adoption to improve inventory control of the entire supply chain system. The installation and unit cost of RFID implementation were estimated and considered to be the main barrier. Such model can offer the policymakers insight into how RFID might improve SCM system performance. Additional test has been conducted for demand with normal and triangular distributions using real data provided by ABC-Dubai Company. The results obtained from running the two models for these distributions are consistent with the original results.

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

2021 ◽  
Author(s):  
Boxiao Chen ◽  
Xiuli Chao ◽  
Cong Shi
Keyword(s):  
Inventory Control ◽  
Learning Algorithm ◽  
Spline Approximation ◽  
Inventory Level ◽  
Selling Price ◽  
Profit Function ◽  
Sales Data ◽  
Demand Distribution ◽  
Joint Pricing ◽  
Exploration Phase

We consider a joint pricing and inventory control problem in which the customer’s response to selling price and the demand distribution are not known a priori. Unsatisfied demand is lost and unobserved, and the only available information for decision making is the observed sales data (also known as censored demand). Conventional approaches, such as stochastic approximation, online convex optimization, and continuum-armed bandit algorithms, cannot be employed, because neither the realized values of the profit function nor its derivatives are known. A major challenge of this problem lies in that the estimated profit function constructed from observed sales data is multimodal in price. We develop a nonparametric spline approximation–based learning algorithm. The algorithm separates the planning horizon into a disjoint exploration phase and an exploitation phase. During the exploration phase, a spline approximation of the demand-price function is constructed based on sales data, and then the corresponding surrogate optimization problem is solved on a sparse grid to obtain a pair of recommended price and target inventory level. During the exploitation phase, the algorithm implements the recommended strategies. We establish a (nearly) square-root regret rate, which (almost) matches the theoretical lower bound.

Dynamic Inventory Control with Stockout Substitution and Demand Learning

2020 ◽  
Vol 66 (11) ◽  
pp. 5108-5127 ◽  
Author(s):  
Boxiao Chen ◽  
Xiuli Chao
Keyword(s):  
Inventory Control ◽  
Learning Algorithm ◽  
A Priori ◽  
Big Data Analytics ◽  
Inventory Level ◽  
Worst Case ◽  
Sales Data ◽  
Demand Learning ◽  
Demand Distribution ◽  
Demand Information

We consider an inventory control problem with multiple products and stockout substitution. The firm knows neither the primary demand distribution for each product nor the customers’ substitution probabilities between products a priori, and it needs to learn such information from sales data on the fly. One challenge in this problem is that the firm cannot distinguish between primary demand and substitution (overflow) demand from the sales data of any product, and lost sales are not observable. To circumvent these difficulties, we construct learning stages with each stage consisting of a cyclic exploration scheme and a benchmark exploration interval. The benchmark interval allows us to isolate the primary demand information from the sales data, and then this information is used against the sales data from the cyclic exploration intervals to estimate substitution probabilities. Because raising the inventory level helps obtain primary demand information but hinders substitution demand information, inventory decisions have to be carefully balanced to learn them together. We show that our learning algorithm admits a worst-case regret rate that (almost) matches the theoretical lower bound, and numerical experiments demonstrate that the algorithm performs very well. This paper was accepted by J. George Shanthikumar, big data analytics.

Inventory Sharing with Transshipment: Impacts of Demand Distribution Shapes and Setup Costs

2014 ◽  
Vol 23 (10) ◽  
pp. 1779-1794 ◽  
Author(s):  
Chao Liang ◽  
Suresh P. Sethi ◽  
Ruixia Shi ◽  
Jun Zhang
Keyword(s):  
Setup Costs ◽  
Demand Distribution ◽  
Inventory Sharing

scholarly journals Inventory Control with Multiple Setup Costs

2014 ◽  
Vol 16 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Osman Alp ◽  
Woonghee Tim Huh ◽  
Tarkan Tan
Keyword(s):  
Inventory Control ◽  
Setup Costs
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