Selecting Online Channel Mode for Green Products in a Capital-Constrained Platform Supply ChainRead the full article
Mathematical Problems in Engineering is a broad-based journal publishing results of rigorous engineering research across all disciplines, carried out using mathematical tools.
Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.
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Credit Rating Model of Family Farms and Ranches Based on Dynamic Dichotomous DEA
The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three-stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three-stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape.
Managing Emergency Procurement Using Option Contract under Supply Disruption and Demand Uncertainty
Effective and flexible procurement and production strategies are capable of alleviating and mitigating supply disruption and demand risk. Considering the price fluctuation caused by environmental change, we investigate the optimal procurement and production strategies under supply disruption and demand uncertainty based on option contract in a two-stage supply chain consisting of a retailer who has two procurement opportunities and a supplier who has the emergency production chance. We explore the value of option contract by comparing it with the optimal decision making under no option contract. The result shows that option ordering and emergency procurement can coordinate the optimal strategies under uncertain environment, improving the economic performance of whole supply chain. When the disruption probability is high or the price of emergency procurement is lower, the higher option price can stimulate the supplier to produce more products to satisfy the retailer’s emergency order at a low price, which is beneficial to both, and the value of option ordering is greater. Otherwise, the emergency procurement is worth more for the core enterprise. The moderate exercise price is conducive to the long-term cooperation of the supplier and the retailer.
A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing
The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.
The Spectral Adomian Decomposition Method for the Solution of MHD Jeffery–Hamel Problem
In this study, the effects of magnetic field on the Jeffery–Hamel problem is studied using a powerful numerical method called the spectral Adomian decomposition method (SADM). The traditional Navier–Stokes equation of fluid mechanics and Maxwell’s electromagnetism governing equations are reduced to nonlinear ordinary differential equations to model the problem. Comparisons with the numerical solutions are made to demonstrate the validity and high accuracy of the present approach. The velocity profile of the inner part of the divergent channel is studied for various values of magnetic field parameter and angle of channel. It was found that an increase in the magnetic field parameter leads to increase in the velocity profile. The results indicated that this technique is more efficient and converges faster than the standard Adomian decomposition method.
Model Construction and Parameters Acquisition of the Predicted Surface Movement Deformation under Thick Loose Layer Mining Area
In China, gas and oil reserves are very scarce, but coal resources are abundant in the energy architecture, which decides that coal will remain the dominant energy source for a long time in the future. The accurate prediction of the size and extent of surface movement after coal seam mining is of great significance for the safe promotion of production activities in the mine area and the safety of people’s lives and properties in the mine area. The surface movement deformation under thick loose seam conditions indicates the phenomenon of a large subsidence value and influence range. To predict the size and range of surface movement deformation under thick loose layer conditions accurately, a hyperbolic secant model is constructed based on the hyperbolic secant function. For high nonlinearity of the model parameters, the adaptive step fruit fly algorithm (ASFOA) is introduced into the process of solving the model parameters. Simulation experiments are conducted in three aspects: monitoring point antideficiency, antigross error, and parameter stability. The simulation results show that the ASFOA algorithm achieves high accuracy in finding the parameters of the hyperbolic secant model. The hyperbolic secant model was applied to the 11111 working face under the mining conditions of thick loose layer geology in the Huainan mine. The engineering application results indicate that the hyperbolic secant model performs well on the prediction of surface movement deformation under thick loose layer conditions.
A Dynamic Reactive Power Allocation Method for Sending-End Power System of the UHVDC Delivering Large Terminal of Renewable Energy
Allocation of reactive power equipment can relieve the transient overvoltage, which is a big threat to the sending-end electric power system of ultrahigh-voltage direct current (UHVDC). However, the dynamic reactive power allocation mostly depends on the trial-and-error method, lacking in an optimal allocation method based on the quantitative evaluation index. To deal with the abovementioned problem, in this study, a dynamic reactive power optimal allocation method is proposed based on the reactive power compensation sensitivity. In detail, first, based on the existing transient overvoltage assessment index, the general form of reactive power optimization problem is proposed. Then, taking the sending-end power system of UHVDC as an example, the reactive power allocation location is determined based on a reactive power compensation sensitivity. Furthermore, combined with the sensitivity, the compensation capacity of each place is determined by particle swarm optimization (PSO). The simulation results show that the proposed method can effectively allocate the dynamic reactive power and suppress the transient overvoltage after fault.