Search

The quality of the container ocean shipping network design directly affects the revenue of liner companies. In this paper, a container ocean shipping network design problem (COND) is investigated. Considering that the governments of many countries have proposed carbon-neutral initiatives, coupled with the constant fluctuations in the shipping market, the carbon tax and cargo owners' choice inertia factors are taken into consideration simultaneously. To model the COND, a bi-level programming model is developed to jointly optimize the liner type, calling sequence, calling frequency, and sailing speed. The upper model aims to choose the network design scheme that maximizes the liner company's revenue. The lower model optimizes the freight scheme of the liner company to evaluate the network design scheme selected by the upper model. To solve the abovementioned model, a genetic algorithm is designed, and a real-world trade shipping route between Asia and Western Europe is introduced to perform numerical experiments. Sensitivity analyses are conducted for different carbon tax rates and transportation demands. The experimental results show that the abovementioned model and algorithm can effectively address the COND and provide certain useful managerial insights for the liner company's daily operation. [Display omitted] • A liner shipping network design problem is investigated. • The carbon tax and choice inertia of cargo owners are jointly considered. • A bi-level programming model is established. • A genetic algorithm is designed to solve the abovementioned model. • The conclusions provide guidance to liner companies' daily operations. [ABSTRACT FROM AUTHOR]

Relatively recently, neural network is introduced into the tourist forecasting field [1, 2, 3]. The forecasting in this field is important for the corporation in the industry to improve its financial earnings. Results show that the hybrid forecasting system is more robust and able to select variables automatically and makes more accurate prediction than the stand-alone neural network. Applying Vector Autoregression (VAR) and genetic algorithm (GA) in hybrid systems with neural network can improve the NN's prediction capability. Case studies have been carried out to demonstrate how to build our VAR-NN-GA system and its advantages. Another case study is the Asian Pacific stock markets, which is more complicated. There exist strong time-dependent correlations between the US market and the Asian markets. While it is difficult to model the markets' interactions with a static model, VAR and GA are adopted for the dynamic model-selection process. The results for the stock market case study show that the hybrid system is about 30% better than the best individual NN for this case study. We can see that, as the domain of prediction become more complicated, the advantage of our system is clearer. [ABSTRACT FROM AUTHOR]