In maritime transportation, liner shipping accounts for over 60\% of the value of goods shipped. However, very limited literature is available on the study of various problems in liner shipping. In this thesis we focus on problems related to this industry. Given a set of cargo to be transported, a set of ports and a set of ships, a common problem faced by carriers in liner shipping is the design of their service network. We develop an integrated model to design service network for the ships and to route the available cargo, simultaneously. The proposed model incorporates many relevant constraints, such as the weekly frequency constraint on the operated routes, and emerging trends, such as obtaining benefits from transshipping cargo on two or more service routes, that appear in practice but have not been considered previously in literature. Also, we design exact and heuristic algorithms to solve the integer program efficiently. The proposed algorithms integrate the ship scheduling problem, a tactical planning level decision, and the cargo routing problem, an operational planning level decision, and provide good overall solution strategy. Computational experiments indicate that larger problem instances, as compared to the literature, can be solved using these algorithms in acceptable computational time. Alliance formation is very common among global liner carriers however a quantitative study of liner alliances is missing from literature. We provide a mathematical framework for the quantitative study of these alliances. For the formation of a sustainable alliance, carriers need to agree on an overall service network and resolve issues concerning distribution of benefits and costs among the members of the alliance. We develop mechanisms to design a collaborative service network and to manage the interaction among the carriers through the allocation of profits in a fair way. The mechanism utilizes inverse optimization techniques to obtain resource exchange costs in the network. These costs provide side payments to the members, on top of the revenue generated by them in the collaborative solution, to motivate them to act in the best interest of the alliance while satisfying their own self interests.

A load plan specifies how freight is routed through a linehaul terminal network operated by a less-than-truckload (LTL) carrier. Determining the design of the load plan is critical to effective operations of such carriers. This dissertation makes contributions in modeling and algorithm design for three problems in LTL load plan design: (1) Refined execution cost estimation. Existing load plan design models use approximations that ignore important facts such as the nonlinearity of transportation costs with respect to the number of trailers, and empty travel beyond what is required for trailer balance that results from driver rules. We develop models that more accurately capture key operations of LTL carriers and produce accurate operational execution costs estimates; (2) Dynamic load planning. Load plans are traditionally revised infrequently by LTL carriers due to the difficulty of solving the associated optimization problem. Technological advances have now enabled carriers to consider daily load plan updates. We develop technologies that efficiently and effectively adjust a nominal load plan for a given day based on the actual freight to be served by the carrier. We present an integer programming based local search procedure, and a greedy randomized adaptive search heuristic; and (3) Stochastic load plan design. Load plan design models commonly represent origin-destination freight volumes using average demands, which do not describe freight volume fluctuations. We investigate load plan design models that explicitly utilize information on freight volume uncertainty and design load plans that most cost-effectively deal with varying freight volumes and lead to the lowest expected cost. We present a Sample Average Approximation approach and a variant of the method for solving the stochastic integer programming formulations.

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