<p> Green supply chain models and carbon emissions tracking have become increasingly prevalent in the supply chain management literature and in corporate strategies. In this dissertation, carbon emissions are integrated into cost-based freight transportation models that can be used to assist operations and supply chain managers in solving the "last mile problem". The models presented herein serve to provide the decision maker with choices on which strategy to implement depending on the strength of the management's desire to reduce carbon emissions. By comparing the optimal solutions that result from using different delivery strategies, this research provides a basis for evaluating an appropriate trade-off between transportation cost and carbon emissions. </p><p> This dissertation contributes to academia and the literature in several ways. The discrete supply chain models provide a method for decision makers to analyze and compare the lowest cost delivery option with the lowest carbon footprint option. The stochastic last mile framework that is introduced provides a method for researchers and practitioners to measure the expected carbon footprint and compare probabilistic costs, carbon emissions, delivery mileage, and delivery times in order to make decisions regarding the most appropriate delivery strategy. This framework is then applied to two different problem settings. The first involves optimizing a delivery fleet to produce the lowest total cost with carbon emissions integrated into the total cost equation. The second compares the carbon footprint resulting from last mile delivery (ecommerce retailing involving a central store delivering to end customers) to customer pick up (conventional shopping at a brick-and-mortar retail location); the break-even number of customers for carbon emissions equivalence provides a basis for companies to determine the environmental impact of last mile delivery and to determine the feasibility of last mile delivery based on objectives related to minimizing carbon emissions.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3618916 |
| Date | 13 June 2014 |
| Creators | Brown, Jay R. |
| Publisher | Kent State University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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