Stochastic inventory control modelling for large satellite constellations

2020 ◽  
Author(s):  
Richard Kim
Keyword(s):  
Inventory Control ◽  
Satellite Constellations ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Large Satellite

scholarly journals Metamodelling of Inventory-Control Simulations Based on a Multilayer Perceptron

2019 ◽  
Vol 20 (3) ◽  
pp. 251-259 ◽  
Author(s):  
Ilya Jackson ◽  
Jurijs Tolujevs ◽  
Sebastian Lang ◽  
Zhandos Kegenbekov
Keyword(s):  
Control System ◽  
Inventory Control ◽  
Multilayer Perceptron ◽  
Simulation Optimization ◽  
Activation Function ◽  
Control Problems ◽  
Perishable Products ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Rectified Linear Unit

Abstract Inventory control problems arise in various industries, and each single real-world inventory is replete with non-standard factors and subtleties. Practical stochastic inventory control problems are often analytically intractable, because of their complexity. In this regard, simulation-optimization is becoming more and more popular tool for solving complicated business-driven problems. Unfortunately, simulation, especially detailed, is both time and memory consuming. In the light of this fact, it may be more reasonable to use an alternative cheaper-to-compute metamodel, which is specifically designed in order to approximate an original simulation. In this research we discus metamodelling of stochastic multiproduct inventory control system with perishable products using a multilayer perceptron with a rectified linear unit as an activation function.

Approximation Algorithms for Stochastic Inventory Control Models

2007 ◽  
Vol 32 (2) ◽  
pp. 284-302 ◽  
Author(s):  
Retsef Levi ◽  
Martin Pál ◽  
Robin O. Roundy ◽  
David B. Shmoys
Keyword(s):  
Approximation Algorithms ◽  
Inventory Control ◽  
Stochastic Inventory ◽  
Stochastic Inventory Control ◽  
Control Models

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.

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