Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    We will mail you new results for this query: keywords==Order policy
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Order policies for a perishable product in retail
Pauls-Worm, K.G.J. ; Hendrix, E.M.T. - \ 2018
Retail - Order policy - perishable product - non-stationary demand - service level constraint
A challenge of inventory control of perishable products in retail is that in general the age distribution of the items in stock is not known. Only the total numbers of items delivered and sold are recorded, resulting in an estimate of the total items in stock. The exact number may be different from the inventory status according to the checkout system due to damaged items and more waste than expected. We investigate order policies for a product with a maximum shelf life of 3 days at delivery. Demand is non-stationary during the week, but stationary over the weeks. Lead time is one day.
For planning purposes in the supermarket, we search for order policies with fixed reorder days during the week, so we order at least 3 times a week, and at most every day. It is likely to have items of different ages in stock. Customers can pick the items in front of the shelf (FIFO), as preferred and stimulated by the store, or search for the freshest items (LIFO). The store has a target α-service level to meet demand.
A Stochastic Programming (SP) model is presented of the situation in the retailer practice. Several policies to determine the order quantity are studied and compared to a policy from literature. The base is a YS order policy where the reorder days Y are fixed and order-up-to levels S are used, with parameter values generated by an MILP approximation of the SP model. Numerical experiments compare the effectiveness of the policies with respect to costs and reached service levels.
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