Abstract / Description of output
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, xed 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.
a stochastic programming inventory problem of a perishable product.
Finding a solution is a challenge as the problem enhances a perishable
product, xed 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.
Original language | English |
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Title of host publication | Proceedings of the 6th International Conference on Computational Logistics (ICCL’15) |
Publisher | Springer |
Pages | 526-540 |
DOIs | |
Publication status | Published - 2015 |
Event | 6th International Conference on Computational Logistics (ICCL’15) - Delft, Netherlands Duration: 23 Sept 2015 → 25 Sept 2015 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
Conference
Conference | 6th International Conference on Computational Logistics (ICCL’15) |
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Country/Territory | Netherlands |
City | Delft |
Period | 23/09/15 → 25/09/15 |
Keywords / Materials (for Non-textual outputs)
- inventory control
- perishable products
- MINLP
- chance constraint
- Monte Carlo