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Industrial Engineering & Operations Research
Master of Science in Industrial Engineering and Operations Research (M.S.I.E.O.R.)
Year Degree Awarded
Month Degree Awarded
As the interest in value recovery of used products increases, industries such as the automotive industry are starting to rethink their product policy. In this thesis we analyze a reverse logistics network for electric vehicle batteries. We develop a cost model that includes the major factors driving the costs in the handling and remanufacturing of failed electric vehicle batteries. Also, we capture the value of time in terms of value decay of returned batteries in the network over time.
Using this model we study alternative reverse logistics network designs and identify under what conditions each of them would be optimal.
First, we develop a mathematical formulation to describe the network we take into focus. Second, we run a computational study and compare different scenario settings to gain insights about the performance of different network configurations and how it is affected by the various factors: returns volume, how it changes over time, repair capability, decay rate and transportation costs.
The results of the study highlight the fact that the network has to adjust its structure over time to reduce costs. Due to the increasing volume certain configurations appear to be more cost-effective at some point in time. Any changes need to be made under consideration of the continuous growth of the demands on the reverse logistics network to prepare for the next optimal configuration.
In this thesis we present appropriate network adjustments to optimize the performance in terms of costs.