Multiperiod Stock Allocation via Robust Optimization
Author(s) -
P JACKSON,
John A. Muckstadt,
Yuexing Li
Publication year - 2018
Publication title -
management science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.954
H-Index - 255
eISSN - 1526-5501
pISSN - 0025-1909
DOI - 10.1287/mnsc.2017.2969
Subject(s) - pooling , stock (firearms) , mathematical optimization , robust optimization , time horizon , computer science , holding cost , operations research , economics , mathematics , engineering , mechanical engineering , artificial intelligence
We consider a one-warehouse, N-retailer, multiperiod, stock allocation problem in which holding costs are identical at each location and no stock is received from outside suppliers for the duration of the planning horizon. No shipments are allowed between retailers. The only motive for holding inventory at the central warehouse for allocation in future periods is the so-called risk pooling motive. We apply robust optimization to this problem extending the inventory policy to allow for an adaptive, nonanticipatory shipment policy. We consider two alternatives for the uncertainty set, one in which risk pooling is implicit and another for which risk pooling is explicit. The explicit risk pooling uncertainty set grows by no more than the square of the number of retailers. The general problem can be solved using Benders’ decomposition. A special case gives rise to closed-form solutions for both uncertainty set alternatives. The explicit risk pooling uncertainty set leads to a square root law in which the optim...
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