Crowdsourced last mile delivery using social network
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Last mile delivery is of key importance in today’s competitive environment as it is one of the major cost in the supply chain. Most major retailers and organizations working towards providing speedy and quality delivery of products are analyzing alternatives to reduce last mile product delivery cost. Crowd logistics is one of the subjects of crucial importance and is being constantly researched upon to improve delivery times and reduce costs. This thesis details the benefits of implementing crowdsourcing using members in a social network to assist in last mile product delivery. A survey is conducted to analyze the friend levels required for a successful product delivery. A logistic regression model is then developed to determine the probability of a particular package being delivered and the travel distances are calculated using tools for Traveling Salesman Problem. Performing a case study on Alexandria, VA using TRANSIMS activity-based simulation, we find that, retailers can reduce last mile delivery costs by around 8600USD per day in a small urban area by using crowdsourcing, with friends taking about an average of extra 10 minutes per delivery. Also, there is an expectation of nearly 55% reduction in pollutants such as NOx, PM 2.5 and PM 10 emitted from delivery trucks.