Abstract
The fleet deployment problem is a tactical planning problem in the shipping industry that seeks to efficiently assign ships to predetermined shipping services to meet the uncertain and seasonal changing demands caused by a highly fluctuating market. Once the fleet deployment plan is fixed, the allocated capacity on each service affects the possible flows transported and the profit obtained. This paper proposes a two-stage robust optimization model for ship fleet deployment and shipping revenue management of a liner shipping network under demand uncertainty. The randomness of demand in our model is represented by probability-free uncertain sets. A column-and-constraint generation based exact algorithm is designed to solve our model based on the analysis of its structural properties. To further accelerate the convergence of our algorithm, an M-tightening technique has been exploited. Finally, extensive computational experiments based on realistic instances are conducted to validate the effectiveness of our model and the efficiency of the algorithm; further, managerial insights are acquired based on the numerical results.
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