Title:
Strategic Network Growth with Recruitment Model

dc.contributor.advisor Ammons, Jane C.
dc.contributor.advisor Realff, Matthew J.
dc.contributor.author Wongthatsanekorn, Wuthichai en_US
dc.contributor.committeeMember White, Chelsea C., III
dc.contributor.committeeMember Ergun, Özlem
dc.contributor.committeeMember Thomas, Valerie
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2007-05-25T17:43:40Z
dc.date.available 2007-05-25T17:43:40Z
dc.date.issued 2006-04-10 en_US
dc.description.abstract In order to achieve stable and sustainable systems for recycling post-consumer goods, it is frequently necessary to concentrate the flows from many collection points to meet the volume requirements for the recycler. This motivates the importance of growing the collection network over time to both meet volume targets and keep costs to a minimum. This research addresses a complex and interconnected set of strategic and tactical decisions that guide the growth of reverse supply chain networks over time. This dissertation has two major components: a tactical recruitment model and a strategic investment model. These capture the two major decision levels for the system, the former for the regional collector who is responsible for recruiting material sources to the network, the latter for the processor who needs to allocate his scarce resources over time and to regions to enable the recruitment to be effective. The recruitment model is posed as a stochastic dynamic programming problem. An exact method and two heuristics are developed to solve this problem. A numerical study of the solution approaches is also performed. The second component involves a key set of decisions on how to allocate resources effectively to grow the network to meet long term collection targets and collection cost constraints. The recruitment problem appears as a sub-problem for the strategic model and this leads to a multi-time scale Markov decision problem. A heuristic approach which decomposes the strategic problem is proposed to solve realistically sized problems. The numerical valuations of the heuristic approach for small and realistically sized problems are then investigated. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/14636
dc.publisher Georgia Institute of Technology en_US
dc.subject Strategic Growth Recruitment en_US
dc.subject.lcsh Statistical decision en_US
dc.subject.lcsh Recycling (Waste, etc.) en_US
dc.subject.lcsh Dynamic programming en_US
dc.subject.lcsh Stochastic programming en_US
dc.title Strategic Network Growth with Recruitment Model en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Realff, Matthew J.
local.contributor.advisor Ammons, Jane C.
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
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