Application of learning pallets for real-time scheduling by the use of radial basis function network

Analysis of an evolutionary game of pallet pooling with participation of third-party platform

Pallet pooling has been widely recognized as an important part of the green supply chain. The development of pallet pooling is an essential component for the transformation and upgrade of the logistics industry in China. Pallet pooling can, however, lead to a conflict over potential benefits among the players. One of the main problems of pallet pooling in China is the reasonable benefit allocation mechanism has not been formed. The pallet pooling system (PPS) with participation of the third-party platform (PPSWPTPP) is one of the pilot modes of pallet pooling in China. Based on evolutionary game theory and a penalty mechanism for breach of contract, this paper constructs a tripartite evolutionary game model of the PPSWPTPP. Eight propositions are set in two basic scenarios regarding whether pallet pooling is adapted to the logistics market to study the stability and dynamic evolution process of the players in the PPSWPTPP. Theoretical and numerical simulation results indicate that these will affect the smooth development of the PPS. The suitable setting of penalties and bonuses, the precise estimation of the pooling benefits, the intention intensity of the players to the pallet pooling, and whether to adapt to the logistics market demand are explored.

A four-stage hybrid model for hydrological time series forecasting

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.