Optimizing safety stock placement in general network supply chains
Name
60405699-MIT.pdf
Description
Full printable version
Size
7.46 MB
Format
Adobe PDF
Checksum (MD5)
fc3a34d7234aebcca092fdb1ecadf828
Author(s)
Lesnaia, Ekaterina.
Advisor(s)
Stephen C. Graves.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
The amount of safety stock to hold at each stage in a supply chain is an important problem for a manufacturing company that faces uncertain demand and needs to provide a high level of service to its customers. The amount of stock held should be small to minimize holding and storage costs while retaining the ability to serve customers on time and satisfy most, if not all, of the demand. This thesis analyzes this problem by utilizing the framework of deterministic service time models and provides an algorithm for safety stock placement in general-network supply chains. We first show that the general problem is NP-hard. Next, we develop several conditions that characterize an optimal solution of the general-network problem. We find that we can identify all possible candidates for the optimal service times for a stage by constructing paths from the stage to each other stage in the supply chain. We use this construct, namely these paths, as the basis for a branch and bound algorithm for the general-network problem. To generate the lower bounds, we create and solve a spanning-tree relaxation of the general-network problem. We provide a polynomial algorithm to solve these spanning tree problems. We perform a set of computational experiments to assess the performance of the general-network algorithm and to determine how to set various parameters for the algorithm. In addition to the general network case, we consider two-layer network problems. We develop a specialized branch and bound algorithm for these problems and show computationally that it is more efficient than the general-network algorithm applied to the two-layer networks.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.
Includes bibliographical references (p. 207-214).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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