A genetic algorithm to optimize a multi-product EOQ model with limited warehouse-space and capital limitation under VMI

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Vendor-managed inventory (VMI) system is a mechanism where the
supplier creates the purchase orders based on the demand information
exchanged by the retailer/customer. In this paper, the performance of
the traditional and VMI system is compared by using EOQ model.
Mathematical modeling is applied and total inventory cost in the supply
chain is used as the performance measure. The supply chain is
considered in two levels, i.e., buyer and supplier, with the assumption
that the supplier faces n buyers and more products as the contract party.
Results of proposed model of VMI are clearly better than traditional
model. In order to make the model more applicable to real-world
production and inventory control problems, we expand this model by
assuming a multi-product economic order quantity problem with limited
warehouse-space and capital limitation. Under this condition, we
formulate the problem as a non-linear integer programming model and
propose a genetic algorithm to solve it. Moreover, design of experiments
is employed to calibrate the parameters of the algorithm for different
problem sizes. At the end, we present a numerical example to
demonstrate the application of the proposed methodology.


Vendor managed inventory, Genetic algorithm, Economic order quantity, Design of experiments, Mathematical modeling

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