Abstract
This paper studies the computation of so-called 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 [7] 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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Hendrix, E.M.T., Pauls-Worm, K.G.J., Rossi, R., Alcoba, A.G., Haijema, R. (2015). A Sample-Based Method for Perishable Good Inventory Control with a Service Level Constraint. In: Corman, F., Voß, S., Negenborn, R. (eds) Computational Logistics. ICCL 2015. Lecture Notes in Computer Science(), vol 9335. Springer, Cham. https://doi.org/10.1007/978-3-319-24264-4_36
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DOI: https://doi.org/10.1007/978-3-319-24264-4_36
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