Scarf's State Reduction Method, Flexibility, and a Dependent Demand Inventory Model.

Abstract

The author considers a finite horizon inventory model where the holding, shortage and ordering costs are linear. The demand random variables are dependent and average demand is described by an exponential smoothing formula. This model can be formulated as a two state variable (inventory level, weighted past demands) dynamic program. By using a procedure first developed by Scarf for a Bayesian inventory model, the author is able to reformulate the model as a one state variable dynamic program. This, of course, results in a considerable computational saving over the two state variable formulation. The author also shows that this dependent demand model orders less than or equal the amount that a comparable independent demand model orders. This result is established under the assumption that all demand is returned by the beginning of the next period. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1983
Accession Number
ADA127483

Entities

People

  • Bruce L. Miller

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Business Administration
  • California
  • Classification
  • Distribution Functions
  • Dynamic Programming
  • Engineering
  • Inventory
  • Inventory Control
  • Lead Time
  • Operations Research
  • Random Variables
  • Scientific Research
  • Security
  • Social Sciences
  • Standards
  • Universities

Readers

  • Logistics and Supply Chain Management.
  • Mathematical Modeling and Probability Theory.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms