The interest in collaborative logistics is fuelled by the ever increasing pressure on companies to operate more efficiently, the realization that suppliers, consumers, and even competitors, can be potential collaborative partners in logistics, and the connectivity provided by the Internet. Logistics exchanges or collaborative logistics networks use the Internet as a common computing platform to implement strategies designed to reduce “hidden costs ” such as asset reposition costs. Through collaboration shippers may be able to identify and submit tours with little or no asset repositioning to a carrier, as opposed to submitting individual lanes, in return for more favorable rates. In this paper, we focus on finding a set of tours connecting regularly executed truckload shipments so as to minimize asset repositioning. Mathematically, the truckload shipper collaboration problem translates into covering a subset of arcs in a directed Euclidean graph by a minimum cost set of constrained cycles. We formulate the lane covering problem, propose several solution algorithms, and conduct a computational study on the effectiveness of these methodologies. 1 1

We address a number of operational challenges encountered in two emerging types of practices in the procurement of truckload transportation services: collaboration and auctions. The main objective in these two types of procurement strategies is identifying and exploiting synergies between the lanes of a carrier's network. In shipper collaboration, we take the perspective of a shipper who collaborates with other shippers to seek synergy between his lanes and other participants' lanes. On the other hand, in procurement auctions, although we take the carriers' perspective in our work, a shipper tries to discover the carrier (or carriers) whose network has the most synergy with his lanes. The first part of the thesis concerns the solution of optimization problems arising in collaborative transportation procurement networks where a group of shippers comes together and jointly negotiates with carriers for the procurement of transportation services. Through collaboration, shippers may be able to identify and submit sequences of continuous loaded movements to carriers, reducing the carriers' need for repositioning, and thus lowering the carriers' costs. A portion of the carriers' cost savings may be returned to the shippers in the form of lower prices. We discuss optimization technology that helps identify repeatable, dedicated truckload continuous move tours with little truck repositioning. Timing considerations are critical to practical viability and are a key focus of our efforts. We demonstrate the effectiveness of our algorithms on randomly generated instances as well as on instances derived from real-world data. The second part concerns the pricing of transportation services offered by the trucking companies (carriers). We look at the bid determination problem faced by carriers in transportation procurement auctions where a shipper requests quotes from multiple carriers and purchases the services of the lowest bidder. The specific problem being studied is the bid valuation problem in the case where the carrier must place bids on multiple lanes simultaneously. We model the problem as a stochastic optimization problem and propose a coordinate search algorithm for solving this problem. Then, we conduct a simulation study to demonstrate the positive impact of the approach on carrier profits.

We address the problem of approximating a minimum cycle cover in parallel. We give the first efficient parallel algorithm for finding an approximation to a minimum cycle cover. Our algorithm finds a cycle cover whose size is within a factor of O(1 + n log n m+n ) of the minimum sized cover using O(log² n) time on (m + n)= log n processors.