In order to achieve more optimal resource scheduling effect for logistics networks, it is essential to collaboratively predict throughput amount of different network nodes in future timestamps. However, the logistics networks are actually a kind of connected complex networks, in which a node denotes a single logistics station and all nodes are associated by implicit relationships. When it comes to collaborative forecasting towards logistics network throughput, all the nodes are required to be integrated together as a whole research object. Therefore, this work introduces graph learning to extract graph-level features of logistics networks, and proposes a data-driven collaborative forecasting method for logistics network throughput based on graph learning. Firstly, information characteristics of graph-level logistics networks is defined as vectorized format. Then, the graph learning framework is formulated, so as to fit the nonlinear relationship between logistics networks and dynamic throughput amount. At last, some simulations are also taken to testify performance of the proposal. The research results show that the graph neural network can find the temporal correlation between data and combine preprocessed multi-layer feature vector with temporal attention weight vectors. And the proposal is able to well implement collaborative forecasting towards logistics networks, with the assistance of graph learning.
In the restructured power industry environment, the economic impact assessment on transmission expansion from a societal perspective is indispensable. This paper proposes a method on this subject. Firstly, we introduce the classical economic assessment indices, and then present several basic concepts on power industry economics. Based on these, the paper proposes an integrated economic impact index on transmission expansion. The index takes the equity aspect as well as the efficiency aspect into account. The concepts of the classical utilitarianism social welfare function and the Lorentz curve and the corresponding Gini coefficient are introduced to measure the efficiency impact and the equity impact of a transmission expansion, respectively. The index can be used to guide the search or the selection of an optimal transmission expansion scheme from the societal perspective. Next, we use a six-node system case to illustrate the application of the index proposed. Then, we present a conclusive statement to end the paper.
We consider a supply chain with one manufacture, who sells his products through online dual channels. One selling channel is the traditional brick-and-mortar retail store while the other is an e-channel in which customer places order through Internet. When the manufacturer operates both channels, we aim to see the optimal production and pricing strategies under the centralized control. It is shown that the optimal selling price in traditional channel is always higher than that in E-channel, and both of optimal selling prices in two channels are decreasing function on demand-price elasticity, selling cost in own channel, respectively. Further, there are two ways to improve the system performance, one is to reduce the selling cost and the other is set the optimal selling prices according to the demand-price elasticity. By the comparison between the joint decisions of single channel and online dual channels, we can find that the system performance of online dual channel, which include an online channel with lower selling cost, is always better than that of single channel system.
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