Data Science for e-Retail and the Sharing Economy

3.0 credits · Graduate

Many online and tech businesses face logistics challenges that require optimal management of physical resources. These challenges may take the form of opening fulfillment centers at the right locations, stocking the right amount of inventory, running optimal number of servers to satisfy computing needs, repositioning bikes in urban bike-sharing systems, and dispatching and repositioning vehicles in online ride-sharing systems. Addressing these challenges often requires building and deploying large-scale optimization models that can make decisions on the fly. We will cover logistics models that allow firms to optimally use its physical resources. From an application perspective, our models will cover the inventory and supply chain theory, network design and transportation logistics. From a methodology perspective, we will use linear and integer programming, stochastic programming, and Markov decision processes. The course will include a number of large case studies that focus on practical implementations.