A Stochastic Capacity Expansion Model: Modular Temporary Facilities.

Abstract

This paper considers optimal decision strategies with regard to capacity expansion in an environment where demand arrivals and departures can be characterized as independent Poisson processes. Two types of facilities are considered in the model: permanent and temporary. Permanent facilities represent the means by which demand is normally served, while temporary facilities represent the extraordinary measures taken in order to serve excess demand (prior to an expansion of permanent facilities). Examples of temporary facilities include backlogging, overloading and jobletting. This paper considers modular temporary facilities which typically are available in a fixed increment size (a module) and incur instantaneous installation and removal charges in addition to normal usage costs. Because of these additional charges, a sub-optimization problem arises with regard to how modules should be used so as to properly balance the instantaneous charges with the usage costs in order to minimize overall expected costs. It is shown that an optimal module removal policy has the form: 'remove a model only if at least s* additional units of unused capacity will remain available.' A simple algorithm is given for determining the optimal parameter s*.

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Document Details

Document Type
Technical Report
Publication Date
Oct 04, 1976
Accession Number
ADA033343

Entities

People

  • R. Scott Shipley

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Coding
  • Costs
  • Decoding
  • Determinants (Mathematics)
  • Equations
  • Intervals
  • Lead Time
  • Operations Research
  • Optimization
  • Probability
  • Random Variables
  • Random Walk
  • Stationary
  • Telephone Lines
  • Time Intervals
  • Transitions

Readers

  • Economics
  • Environmental Impact Assessment (EIA) of Proposed Air Force Base Actions.
  • Statistical inference.