Supply Chain Design (CTL.SC2x)
by Chris Caplice, Yossi Sheffi, Jarrod Goentzel, James B. Rice · MITx / MIT Center for Transportation & Logistics
Follow-on MIT course on designing physical networks: transportation and transshipment problems, facility location modeling, and network optimization, extended to production and demand planning, distribution strategy, supplier management, and supply chain finance.
More resources on Supply Chain Engineering
Google OR-Tools: Vehicle Routing
Documentation and runnable code for solving traveling salesman and vehicle routing problems, including capacity limits, time windows, pickups and deliveries, resource constraints, and penalties for dropped visits. Examples in Python, C++, Java, and .NET.
Facility Location in Supply Chain Design
Survey chapter formulating the fixed-charge facility location problem and its supply chain extensions: location combined with LTL vehicle routing, joint location-inventory models with risk pooling, robust location under demand uncertainty, and reliable designs against facility failure.
Inventory and Production Management in Supply Chains (4th Edition)
Reference text on inventory and production control: order-point/order-quantity systems, coordinated replenishment, lot sizing, forecasting, multi-echelon stock allocation, and production planning. Derives replenishment policies and their cost consequences under deterministic and probabilistic demand. It covers the messy implementation cases (coordinated items, capacity constraints, forecast error) that pure theory texts skip.
Fundamentals of Supply Chain Theory (2nd Edition)
Graduate textbook deriving the mathematics of supply chains: EOQ and newsvendor models, multi-echelon inventory, facility location, network design, the bullwhip effect, process flexibility, supply disruptions, TSP and vehicle routing, with worked examples and problem sets.
Supply Chain Fundamentals (CTL.SC1x)
MIT's core supply chain course covering demand forecasting, EOQ and safety-stock inventory models, transportation modal selection and routing, customer and product segmentation, and risk. Weekly problem sets apply probability and optimization to minimizing total logistics cost.