Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function (NDDF) method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) In general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Capacity Allocation and Pricing for Take-or-Pay Reservation Contracts

This paper uses a bi-level optimization model to formulate a specific type of capacity reservation contracts, namely take-or-pay contracts, where a buyer reserves a portion of a supplier’s capacity before demand is realized with discounted price. At first, we formulate the lower-level problem and solve a non-linear optimization model where a buyer decides on the amount of capacity to be reserved given the discounted and normal unit capacity price, demand probability distribution and maximum available capacity. Afterwards, we construct the upper-level model where there are a supplier and multiple buyers and the supplier must choose the discounted price and maximum available capacity for each of the buyers. Enforced by the behaviour of the model, we create a bi-level real-valued genetic algorithm to find good solutions for the model

Capacity Allocation and Pricing for Take-or-Pay Reservation Contracts

This paper uses a bi-level optimization model to formulate a specific type of capacity reservation contracts, namely take-or-pay contracts, where a buyer reserves a portion of a supplier’s capacity before demand is realized with discounted price. At first, we formulate the lower-level problem and solve a non-linear optimization model where a buyer decides on the amount of capacity to be reserved given the discounted and normal unit capacity price, demand probability distribution and maximum available capacity. Afterwards, we construct the upper-level model where there are a supplier and multiple buyers and the supplier must choose the discounted price and maximum available capacity for each of the buyers. Enforced by the behaviour of the model, we create a bi-level real-valued genetic algorithm to find good solutions for the model

Experimental and numerical study of the salt dissolution in porous media

Dissolution in porous media is a complex phenomenon. In most of the approaches, density variations are ignored but they generate important convection structures like those presented in [5]. In this chapter, our aim is to develop an experimental approach in order to validate our numerical tool. To do so, we will make use of local measurments using microtomography in order to get the surface evolution of the dissolved medium. We will also get some results about the weight losses of salt dissolved and then try to compare to those predicted by numerical simulations