In transportation and logistics, the goal is often to optimize various aspects of the system, such as travel time, fuel efficiency, and emissions.

Graph theory is commonly used to model transportation networks, as it allows for the representation of nodes (e.g., cities) and edges (e.g., roads) and the analysis of connectivity and flow.

Mathematical modeling is widely used in logistics to optimize various aspects, including inventory management, warehouse location, and transportation routing.

In transportation models, the objective function often represents the total cost associated with the transportation process, which may include factors such as fuel consumption, tolls, and driver wages.

Transportation models often incorporate various constraints, such as capacity constraints (e.g., vehicle or road capacity), demand constraints (e.g., customer demand), and time constraints (e.g., delivery deadlines).

Lagrangian relaxation, column generation, and Benders decomposition are all common heuristics used to solve large-scale transportation models, as they can help break down the problem into smaller, more manageable subproblems.

Safety stock is the extra inventory held in a logistics network to protect against unexpected fluctuations in demand or supply.

The center-of-gravity method, median method, and P-median method are all common techniques used to determine optimal locations for warehouses, considering factors such as customer demand, transportation costs, and facility capacities.

Deadheading in a transportation routing problem refers to the situation where a vehicle travels without a load, typically when returning to a depot or moving between pickup and delivery locations.

Branch-and-bound, dynamic programming, and genetic algorithms are all common methods used to solve vehicle routing problems, which aim to find efficient routes for vehicles to deliver goods or services.

Lead time in a supply chain network encompasses the time it takes for an order to be processed, manufactured, and shipped to its final destination.

Average travel time, average fuel consumption, and average emissions are all common metrics used to assess the performance and efficiency of a transportation system.

The economic order quantity (EOQ) model, safety stock model, and reorder point model are all commonly used approaches for determining optimal inventory levels in a logistics network, considering factors such as demand, lead time, and holding costs.

Transshipment models, assignment models, and the traveling salesman problem are all examples of common types of transportation network models used to optimize the flow of goods or people.

Cross-docking in a logistics network involves the direct transfer of goods from one vehicle to another without the need for warehousing or storage.