Modelling and advanced optimisation methods for the multi-shift full truckload vehicle routing problem

This thesis is concerned with a real-world multi-shift drayage problem at a large international port with multiple docks being operated simultaneously. Several important issues in the drayage problem are identified and a set covering model is developed based on a novel route representation. The model adopts an implicit solution representation to reduce the problem size and aims to find a set of vehicle routes with minimum total cost to deliver all commodities within their time windows. As accurate travel time prediction is necessary to construct the vehicle routes, a short-haul travel time prediction model and an algorithm using real-life GPS data are studied. The output of the prediction model can be used as an input for the set covering model. The set covering model for the multi-shift full truckload transportation problem can be directly solved by a commercial solver for small problems, but results in prohibitive computation time for even moderate-sized problems. In order to solve medium- and large-sized instances, we proposed a 3-stage hybrid solution method and applied it to solve real-life instances at a large international port in China. It was shown that the method is able to find solutions that are very close to the lower bounds. In addition, we also proposed a more efficient hybrid branch-and-price approach. Results show the method performed well and is more suited for solving real-life, large-sized drayage operation problems

Modelling and advanced optimisation methods for the multi-shift full truckload vehicle routing problem

This thesis is concerned with a real-world multi-shift drayage problem at a large international port with multiple docks being operated simultaneously. Several important issues in the drayage problem are identified and a set covering model is developed based on a novel route representation. The model adopts an implicit solution representation to reduce the problem size and aims to find a set of vehicle routes with minimum total cost to deliver all commodities within their time windows. As accurate travel time prediction is necessary to construct the vehicle routes, a short-haul travel time prediction model and an algorithm using real-life GPS data are studied. The output of the prediction model can be used as an input for the set covering model. The set covering model for the multi-shift full truckload transportation problem can be directly solved by a commercial solver for small problems, but results in prohibitive computation time for even moderate-sized problems. In order to solve medium- and large-sized instances, we proposed a 3-stage hybrid solution method and applied it to solve real-life instances at a large international port in China. It was shown that the method is able to find solutions that are very close to the lower bounds. In addition, we also proposed a more efficient hybrid branch-and-price approach. Results show the method performed well and is more suited for solving real-life, large-sized drayage operation problems