We study a logistics network where shippers collaborate and bundle their shipment requests in order to negotiate better rates with a common carrier. In this setting, shippers are able to identify collaborative routes with decreased overall empty truck movements. After the optimal routes that minimize total cost of covering all the shippers' demand are determined, this cost is allocated among the shippers. Our goal is to devise cost allocation mechanisms that ensure the sustainability of the collaboration. We first develop cost allocation mechanisms with well-known properties from the cooperative game theory literature, such as budget balance, stability and cross monotonicity. Next, we define a set of new properties, such as a guaranteed discount from the stand alone cost for each shipper, desirable in our setting and propose several cost allocation schemes that could lead to implementable solutions. We also perform a computational study on randomly generated and real-life data to derive insights on the performance of the developed allocation schemes.

Because of historically high fuel prices, the trucking industry's operating expenses are higher than ever and thus profit margins are lower than ever. To cut costs, the trucking industry is searching for and exploring new ideas. We investigate the potential of collaborative opportunities in truckload transportation. When carriers serve transportation requests from many shippers, they may be able to reduce their repositioning costs by exchanging one or more of them. We develop optimization models to determine the maximum benefit that can be derived from collaborating. We also develop various exchange mechanisms which differ in terms of information sharing requirements and side payment options that allow carriers to realize some or all of the costs savings opportunities.

Vendor managed inventory (VMI) replenishment is a collaboration between a supplier and its customers where the supplier is responsible for managing the customers' inventory levels. In the VMI setting we consider, the supplier exploits synergies between customers, e.g., their locations, usage rates, and storage capacities, to reduce distribution costs. Due to the intricate interactions between customers, calculating a fair cost-to-serve for each customer is a daunting task. However, cost-to-serve information is useful when marketing to new customers, or when revisiting routing and delivery quantity decisions. We design mechanisms for this cost allocation problem and determine their characteristics both analytically and computationally.

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