Strategic Network Growth with Recruitment Model

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.