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NOVEL STOCHASTIC PROGRAMMING FORMULATIONS FOR ASSEMBLE-TO-ORDER SYSTEMSLIANG, HONGFENG January 2017 (has links)
We study a periodic review assemble-to-order (ATO) system introduced by Akcay
and Xu (2004) which jointly optimizes the base stock levels and the component allocation
with an independent base stock policy and a first-come- first-served allocation
rule. The formulation is a non-smooth and thus theoretically and computationally
challenging. In their computational experiments, Akcay and Xu (2004) modified the
right hand side of the inventory availability constraints by substituting linear functions
for piece-wise linear ones. This modification may have a significant impact on
low budget levels. The optimal solutions obtained via the original formulation, i.e.,
the formulation without modification, include zero base stock levels for some components
and thus indicate a bias against component commonality. We study the impact
of component commonality on periodic review ATO systems. We show that lowering
component commonality may yield a higher type-II service level. The lower degree of
component commonality is achieved via separating inventories of the same component
for different products. We substantiate this property via computational and theoretical
approaches. We show that for low budget levels the use of separate inventories
of the same component for different products can achieve a higher reward than with
shared inventories. Finally, considering a simple ATO system with one component
shared by two products, we characterize the budget ranges such that either separate
or shared inventory component (i.e., component commonality) is beneficial. / Thesis / Doctor of Philosophy (PhD)
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Multi-Tier Inventory Systems With Space ConstraintsJernigan, Stephanie A. 12 April 2004 (has links)
In the warehouse of a large cosmetics company, a mechanized order picker is restocked from nearby shelving, and the shelving is restocked from bulk storage, forming a three-tier inventory system. We consider such multi-tier inventory systems and determine the storage areas to which to assign items, and the quantities in which to store them, in order to minimize the total cost of picking items and restocking storage locations. With this research, we increase the number of inventory systems for which simple search algorithms find a provably near-optimal solution. The model and method were tested on data from the Avon Products distribution center outside Atlanta; the solution identified by the model reduced picking and restocking costs there by 20%.
The sales forecasts of items stored in the warehouse may change, however, and new items will be introduced into the inventory system and others removed. To account for these changes, some warehouses may periodically reassign items to storage areas and recompute their storage quantities. These reassignment activities account for additional costs in the warehouse. The second focus of this research examines these costs over several time periods in a simple multi-tier inventory system. We develop heuristics to determine the storage areas to which to assign items and the quantities in which to store them in each time period, in order to minimize the total cost of picking items, restocking storage locations, and reassigning skus over multiple periods.
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