Existing Sytem
a fuzzy continuous-review multi-product in-ventorymodel is considered with improvement in quality and
reduction in setup cost. Previously, the continuous-review
inventory model was considered for the single-product by
Tajbakhsh [19] and others. However, none of the researchers
considered continuous-review for the multi-product with
stochastic lead time in the fuzzy environment for imperfect
production system.
All defective products are being imme-diately replaced at a fixed cost. To reduce the lead time, an
additional cost is added to the total cost, which is known as
lead time crashing cost. Manufacturing setups have a maxi-mum limit for the storage space. Therefore, maximum space
constraint is considered in this model. Demand is considered uncertain based on realistic scenarios. In next sections, this
problem will be solved and analyzed
Proposed System
The proposed model considers the continuous-review in-ventory policy for inventory control. This study considers the
cost minimization for multi-product production system. To
make the model more realistic and real-life based, space and
a service level constraints are considered as well.
Optimal
quantity for multiple products is calculated with the setup
cost reduction and product’s quality improvement. Loga-rithmic expressions for setup cost reduction and product
quality improvement are used in this model
Conclusion
The proposed model extended a continuous-review inventory
model considering multiple products with stochastic lead
time, fuzzy demand, and space constraint. The important
and major aim of this model was to develop and investigate
model for multi-product continuous-review inventory system
with the stochastic fuzzy demand to minimize the total cost.
For the setup cost reduction and to decrease imperfect production, two logarithmic expressions were used. Managers
could reduce both, the imperfect production as well as the
setup cost, by using the given strategy for the multi-product.
A mathematical model was provided to obtain the optimal
solution with the distribution-free approach
