Less-than-truckload (LTL) carriers operate hub-and-spoke networks (typically referred to as linehaul networks) to satisfy customer demand for origin-destination service. In recent years, there has been enormous pressure on LTL carriers to deliver improved service, e.g., to provide lanes with 2-day service as opposed to the traditional 5-day service. This has further complicated an already difficult dynamic resource management problem. We discuss decision technology to assist planners with managing drivers, tractors and trailers. The technology is capable of handling driver regulations, pup-matching, meet-and-turn points, and a variety of other complexities. The technology has been tested on real-life carrier data involving about 5,000 moves per day.

We consider a transportation problem where several products have to be shipped from an origin to a destination by means of vehicles with given capacity. Due to organizational reasons, the time between consecutive shipments must be greater than a given minimum time. The problem is to determine a shipping policy that minimizes the average sum of the transportation and the inventory costs at the origin and at the destination over an infinite horizon. We show the worst-case performance of two classes of practical shipping policies, the Zero Inventory Ordering (ZIO) policies, where a shipment is performed only when the inventory level at the destination is zero, and the Frequency Based Periodic Shipping policies, where shipments are performed only at integral multiples of the minimum inter-shipment time, based on a set of shipping frequencies. Then, we evaluate the performance of each policy on the basis of a set of randomly generated problem instances.

In this work, we address the problem where customer demands can be delivered by either a private fleet or an external transporter. We have to select the right transportation modes while routing the vehicles of the private fleet in a way to minimize total transportation cost.

We introduce a travel planning problem which is solved by computing time-dependent shortest paths through a fixed sequence of nodes. Given a predetermined itinerary, our travel planning problem consists in finding the best travel plan, involving planes and hotels, based on the traveler's preferences. Our time-dependent framework therefore models plane flights, hotels, stays in each city as well as global time constraints. Given the large size of time-dependent networks, an exact decomposition algorithm is devised to solve instances of realistic size in reasonable computation times.