63 research outputs found
Piecewise linear approximations for the static-dynamic uncertainty strategy in stochastic lot-sizing
In this paper, we develop mixed integer linear programming models to compute
near-optimal policy parameters for the non-stationary stochastic lot sizing
problem under Bookbinder and Tan's static-dynamic uncertainty strategy. Our
models build on piecewise linear upper and lower bounds of the first order loss
function. We discuss different formulations of the stochastic lot sizing
problem, in which the quality of service is captured by means of backorder
penalty costs, non-stockout probability, or fill rate constraints. These models
can be easily adapted to operate in settings in which unmet demand is
backordered or lost. The proposed approach has a number of advantages with
respect to existing methods in the literature: it enables seamless modelling of
different variants of the above problem, which have been previously tackled via
ad-hoc solution methods; and it produces an accurate estimation of the expected
total cost, expressed in terms of upper and lower bounds. Our computational
study demonstrates the effectiveness and flexibility of our models.Comment: 38 pages, working draf
Protective inventories in manufacturing systems
SIGLEAvailable from British Library Document Supply Centre- DSC:D61192 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
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