A Sample-Based Method for Perishable Good Inventory Control with a Service Level Constraint
نویسندگان
چکیده
This paper studies the computation of order up to levels for a stochastic programming inventory problem of a perishable product. Finding a solution is a challenge as the problem enhances a perishable product, fixed ordering cost and non-stationary stochastic demand with a service level constraint. An earlier study [6] derived order-up-to values via an MILP approximation. We consider a computational method based on the so-called Smoothed Monte Carlo method using sampled demand to optimize values. The resulting MINLP approach uses enumeration, bounding and iterative nonlinear optimization.
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