Show simple item record

dc.contributor.authorDong, Zhijie
dc.date.accessioned2015-10-15T18:02:44Z
dc.date.available2015-10-15T18:02:44Z
dc.date.issued2015-08-17
dc.identifier.otherbibid: 9255329
dc.identifier.urihttps://hdl.handle.net/1813/41054
dc.description.abstractLogistics is a vitally important part of the economy, and it is now a $1.45 trillion industry in the United States representing 8.3 percent of GDP. Efficient design of routes and schedules for moving materials into manufacturing or assembly plants is a central part of inbound logistics operations. This dissertation builds on elements of traditional vehicle routing as well as broader elements of logistics planning. At the core of the process is a mathematical optimization termed capacitated clustering. Two major categories of suppliers are analyzed in this research. The first supplier category includes suppliers with small quantities of materials, so daily pickups may not be required. A new approach is proposed that considers pick-up frequency and spatial design as joint decisions to minimize total logistics (transportation plus inventory) cost. The clustering-based optimization uses an approximation to the actual cost of a routing solution without actual route construction. The problem is shown to be analogous to a single-source fixed-charge facility location problem, and near-optimal solutions can be found using an efficient heuristic algorithm. Computational experiments show the effectiveness of how this model is formulated and a case study demonstrates that substantial total cost savings can be achieved in realistic applications. A second category of suppliers ships moderately large volumes to a single plant but not enough to fill a truck themselves. One commonly used process is to have plant- based collection routes on a daily basis that stop at multiple suppliers and return to the plant. The model developed here is formulated as a two-stage stochastic program, which includes uncertainty in the load quantities at suppliers and controls (either penalties or constraints) designed to improve the "regularity" of service to individual suppliers. Two adaptive decomposition heuristics are explored for solving the stochastic program in large scale, integer L-shaped method (ILSM) and progressive hedging (PH). An application to logistics operations in the automotive industry is used to demonstrate the effectiveness of the model and the PH solution method.
dc.language.isoen_US
dc.subjectinbound logistics
dc.subjectnetwork design
dc.subjectcapacitated clustering
dc.titleEfficient Design Of Inbound Logistics Networks
dc.typedissertation or thesis
thesis.degree.disciplineCivil and Environmental Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Civil and Environmental Engineering
dc.contributor.chairTurnquist,Mark Alan
dc.contributor.committeeMemberTopaloglu,Huseyin
dc.contributor.committeeMemberNozick,Linda K.


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Statistics