Evaluating the benefits of picking and packing planning integration in e-commerce warehouses

Motivated by recent claims on the potential value of integration in warehouse management, this study evaluates the benefits arising from integrating the planning of order picking and packing processes in e-commerce warehouses. A set of research questions are proposed for exploring various benefits under different operational conditions and an experimental study is designed to answer them. In order to have a concrete model to represent the integrated planning method, a mixed-integer nonlinear programming model is developed, and then compared against a non-integrated variation. The experimental study makes the comparisons by analysing the collected empirical data from a real-life warehouse. Our findings indicate that integrated picking and packing planning can yield improved performance in different aspects under different configurations of objectives, order quantities, order categories or workforce allocation

Evaluating the benefits of picking and packing planning integration in e-commerce warehouses

Motivated by recent claims on the potential value of integration in warehouse management, this study evaluates the benefits arising from integrating the planning of order picking and packing processes in e-commerce warehouses. A set of research questions are proposed for exploring various benefits under different operational conditions and an experimental study is designed to answer them. In order to have a concrete model to represent the integrated planning method, a mixed-integer nonlinear programming model is developed, and then compared against a non-integrated variation. The experimental study makes the comparisons by analysing the collected empirical data from a real-life warehouse. Our findings indicate that integrated picking and packing planning can yield improved performance in different aspects under different configurations of objectives, order quantities, order categories or workforce allocation

Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging

High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work’s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results

Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R2) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R2) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change

A model for soil moisture dynamics estimation based on artificial neural network

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study

Identification and comprehensive analyses of the CBL and CIPK gene families in wheat (Triticum aestivum L.)

The interaction analysis of wheat TaCBL and TaCIPK proteins were performed by Y2H method. (PDF 191 kb