Authors :
ONYENIKE K; OJARIKRE, H.I
Volume/Issue :
Volume 7 - 2022, Issue 3 - March
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3DH4NyM
DOI :
https://doi.org/10.5281/zenodo.6414944
Abstract :
This work considers inventory systems that
models uncertainties in demand and various fuzzy
inventory cost parameters. However, in many cases where
there is little or no historical data available to decision
makers due to recent changes in the supply chain
environment, probability distribution may simply not be
available, or may not easily or accurately be estimated. In
this paper we consider fuzzy inventory model with three
fuzzy parameters and shortages not permitted. Fuzziness
in this model occurs in the demand of product, holding
costs and ordering costs. The inventory lead time is
assumed to be zero. To obtain the total fuzzy costs, the
three fuzzy parameters have been represented using
Pentagonal fuzzy numbers (PFN). The economic ordered
quantity (EOQ) and total inventory costs has also been
computed by defuzzification of the fuzzy economic
ordered quantity and total fuzzy costs using the graded
mean integration approach. A numerical example is
provided to compare our model with the four fuzzy set
parameters with those with crisp parameters.
Keywords :
Demand, Defuzzification, Inventory cost, pentagonal fuzzy numbers, Graded mean integration representation method.
This work considers inventory systems that
models uncertainties in demand and various fuzzy
inventory cost parameters. However, in many cases where
there is little or no historical data available to decision
makers due to recent changes in the supply chain
environment, probability distribution may simply not be
available, or may not easily or accurately be estimated. In
this paper we consider fuzzy inventory model with three
fuzzy parameters and shortages not permitted. Fuzziness
in this model occurs in the demand of product, holding
costs and ordering costs. The inventory lead time is
assumed to be zero. To obtain the total fuzzy costs, the
three fuzzy parameters have been represented using
Pentagonal fuzzy numbers (PFN). The economic ordered
quantity (EOQ) and total inventory costs has also been
computed by defuzzification of the fuzzy economic
ordered quantity and total fuzzy costs using the graded
mean integration approach. A numerical example is
provided to compare our model with the four fuzzy set
parameters with those with crisp parameters.
Keywords :
Demand, Defuzzification, Inventory cost, pentagonal fuzzy numbers, Graded mean integration representation method.
