Difference between revisions of "The anticipative concept in warehouse optimization using simulation in an uncertain environment"

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m (Protected "The anticipative concept in warehouse optimization using simulation in an uncertain environment" ([edit=sysop] (indefinite) [move=sysop] (indefinite)))
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By Davorin Kofjacˇ, Miroljub Kljajic´, Valter Rejec
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'''By Davorin Kofjacˇ, Miroljub Kljajic´, Valter Rejec'''
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'''Abstract'''
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The modern business environment is highly unpredictable. An anticipation approach in a real case study is presented to cope with
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such instability and minimize the total inventory cost without stock-outs occurring and inventory capacity being exceeded. The antici-
 +
pation concept is performed using simulation models supported by inventory control algorithms on a selected sample of representative
 +
items. The inventory control algorithms include Silver–Meal, Part period balancing, Least-unit cost, and Fuzzy inventory control algorithm
 +
based on fuzzy stock-outs, highest inventory level and total cost. Transportation cost is explicitly defined as a discrete function of ship-
 +
ment size. The algorithms are tested on historic data. Simulation results are presented and the risk of accepting them as reliable is dis-
 +
cussed. The process of simulation model implementation is briefly discussed to further validate the model and train order managers to
 +
use the simulation model in their order placement process.
 +
 
  
 
'''Link to material:''' http://ifors.org/developing_countries/downloads/sept2_2011/Kofjac_2009_European-Journal-of-Operational-Research.pdf
 
'''Link to material:''' http://ifors.org/developing_countries/downloads/sept2_2011/Kofjac_2009_European-Journal-of-Operational-Research.pdf
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[[Category:General Articles]]
 
[[Category:General Articles]]

Revision as of 06:47, 3 September 2013

By Davorin Kofjacˇ, Miroljub Kljajic´, Valter Rejec


Abstract

The modern business environment is highly unpredictable. An anticipation approach in a real case study is presented to cope with such instability and minimize the total inventory cost without stock-outs occurring and inventory capacity being exceeded. The antici- pation concept is performed using simulation models supported by inventory control algorithms on a selected sample of representative items. The inventory control algorithms include Silver–Meal, Part period balancing, Least-unit cost, and Fuzzy inventory control algorithm based on fuzzy stock-outs, highest inventory level and total cost. Transportation cost is explicitly defined as a discrete function of ship- ment size. The algorithms are tested on historic data. Simulation results are presented and the risk of accepting them as reliable is dis- cussed. The process of simulation model implementation is briefly discussed to further validate the model and train order managers to use the simulation model in their order placement process.


Link to material: http://ifors.org/developing_countries/downloads/sept2_2011/Kofjac_2009_European-Journal-of-Operational-Research.pdf


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