In this thesis, we study three periodic-review, finite-horizon inventory systems in the
presence of supply and demand uncertainties. In the first part of the thesis, we study
a multi-period single-station problem in which supply uncertainty is modeled by partial
supply. Formulating the problem under a robust optimization (RO) framework, we
show that solving the robust counterpart is equivalent to solving a nominal problem
with a modified deterministic demand sequence. In particular, in the stationary case
the optimal robust policy follows the quasi-(s, S) form and the corresponding s and S
levels are theoretically computable. In the second part of the thesis, we extend the RO
framework to a multi-period multi-echelon problem. We show that for a tree structure
network, decomposition applies so that the optimal single-station robust policy remains
valid for each echelon in the tree. Furthermore, if there are no setup costs in the network,
then the problem can be decomposed into several uncapacitated single-station
problems with new cost parameters subject to the deterministic demands. In the last
part of the thesis, we consider a periodic-review Assemble-To-Order (ATO) system with
multiple components and multiple products, where the inventory replenishment for each
component follows an independent base-stock policy and product demands are satisfied
according to a First-Come-First-Served (FCFS) rule. We jointly consider the inventory
replenishment and component allocation problems in the ATO system under stochastic
component replenishment lead times and stochastic product demands. The problems
are formulated under the stochastic programming (SP) framework, which are difficult
to solve exactly due to a large number of scenarios. We use the sample average approximation (SAA) algorithms to find near-optimal solutions, which accuracy is verified by
the numerical experiment results. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24067 |
| Date | January 2018 |
| Creators | Chu, Jie |
| Contributors | Huang, Kai, Business |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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