Supplier evaluation and selection is one of the most important components of supply chain, which influence the long term commitments and performance of the plant. Supplier selection is a complex multi-criteria problem which includes both qualitative and quantitative factors. In order to select the best suppliers it is essential to make a trade off between these tangible and intangible factors some of which may conflict. In this paper, an AHP-based supplier selection model is formulated and then applied to a real case study for a polyamide fiber plant in China. The use of the proposed model indicates that it can be applied to improve and assist decision making to resolve the supplier selection problem in choosing the optimal supplier combination.
With advent of the postepidemic era, the development of digital logistics operations management is imminent. Among the various logistics delivery methods, same-city delivery is chosen by the vast majority of customers for its timeliness and safety. Online ordering and delivery methods for same-city delivery are also gaining increasing attention from enterprises which need to know the inventory balance of all same-city warehouses in time for early deployment and response. However, in practice, the inventory balance of each warehouse can be affected by other warehouses in the same city, and there is often a lack of data in the inventory management system due to equipment and other issues resulting in a poor response from the company to handle emergencies. To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. The L-curve criterion was used to select hyperparameter values, the spatiotemporal regularize was used to capture the time dependence of the time series, and the model performance was evaluated using root mean square error and mean absolute percentage error. Comparisons with classical interpolation techniques were made to validate the improved performance of the proposed method.
This paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to process the original passenger flow data and generate several eigenmodal functions (IMFs) and residuals with different characteristic scales. Based on the chaos theory, each component of the PSO algorithm was improved by introducing an inertia factor to facilitate the adjustment of its search capability to improve optimization. Each subsequence of the phase-space reconstruction was built into an improved PSO-LSTM prediction model, and the output of each prediction model was summed to determine the final output. Experimental studies were performed using data from the North Railway Station of Chengdu Rail Transit, and the results showed that the proposed model can generate better prediction results. The proposed model obtained root mean square error (RMSE) and mean absolute error (MAE) of 16.0908 and 11.3704, respectively. Compared with the LSTM, the improved PSO-LSTM, the improved EMD-PSO-LSTM, and the model proposed in this paper improved the RMSE values by 25.53%, 29.97%, and 58.76%, respectively, and the MAE values by 30.41%, 40.13%, and 63.08%, respectively, of the prediction results.
Wind tunnel test can not simulate effectively the flow field of fuze electrical machinery at flight test conditions. In order to deeply research the structure of flow field and measure the flow field density out of fuze electrical machinery, the laser dual hologram interferometry was applied and the streaks photos were obtained clearly. To recognize the contours of density field, identification is needed to deal with interference streaks, and the density of local streaks is calculated with interference principle. The hologram interferometry photos of density field were pro-processed in the thesis, for the problem of photos that the left part was much brighter than the right, the method of multiple threshold was used to binary image. It can be seen that the method gives consistent results to a certain extent of human perception and gives ideal results to find uniform regions in the image plane. Then the binary image is thinned by pixels searching algorithm according to the lines to get coordinates of the pixels of each streak and to lay the foundation for extracting density field from density interference streak in future research.
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