Research and Applications of Scenario Tree Compression for Stochastic Freight Service Network Design

Service network design is one of the core problems in freight logistics. Compared to vehicle routing problem, service network design can readily model freight transfers and consolidation and, therefore, is more suitable for large transportation network optimization. Due to an uncertain environment in which freight transportation operates and reliability requirements from users, stochastic service network design is becoming a focused research area recently. However, the introduction of stochastic scenario tree in the stochastic model has further increased the complexity of the deterministic service network design, which is already NP-Hard. In contrast to the other relevant research which concentrates on optimization algorithms, this project mainly focuses on the scenario tree compression in the context of service network design. In this way, we can reduce the number of iterations between the two stages of stochastic programming. To achieve this, we will investigate both the dynamic scenario clustering methods and evolutionary based scenario classification approaches. These will be combined with decomposition based methods and latest meta-heuristics to improve the performance of algorithms for the stochastic service network design. These algorithms will be tested on the international benchmark instances as well as the real world container transportation problem at Ningbo Port. The project will advance the state-of-the-art algorithms for stochastic service network design significantly. It will also improve the performance of the container transportation network at Ningbo Port as well as other practical transportation networks.