Many-objective optimisation of heterogeneous transport network under multitask scenarios

The complex scenario of interweaving multimodal transport and direct transport in the actual transport network poses challenges for optimising transport scheme under many-objectives. We establish a many-objective optimisation model for heterogeneous networks with the objectives of transit time connection, economies of scale, total transport network cost and carbon emissions. In terms of high-level strategy, selection strategies based on Genetic Algorithm (GA) and simulated annealing algorithm (SA), adaptive re-heating by Simulated Annealing (SAARH), and supplementary solution by Simulated Annealing (SASS) are incorporated as the receiving criteria, and seven low-level heuristic operators are designed from three aspects. Taking six different transportation tasks of twelve city node networks along the Yangtze River Economic Belt as an example, we draw the following conclusions: compared with GA and NSGA-II, the hyper-heuristic algorithm under SAARH strategy is feasible and effective; in different scenarios, heterogeneous transport networks will also generate new transport schemes, and it may not be as effective as expected to increase the railway market share by simply improving the railway travel speed; A more favorable cost coefficient of economies of scale can significantly reduce the overall cost of the transport network. Nevertheless, its impact on transit time connection fluctuates considerably while exerting minimal influence on network carbon emissions.