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With the continuous development of e-commerce, the logistics industry is thriving, and logistics delays have become an issue that deserves more and more attention. Genetic EM algorithm is a genetic EM algorithm that is an iterative optimization strategy algorithm that can be used to solve the high-quality algorithm of travel problems with many node...
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Context 1
... Figure 1 randomly selects a point from the known feasible region as the initial stroke, calculate the objective function G(x) of each stroke in a initial stroke, and calculate the optimal stroke S ? of the objective function as the current optimal journey. en set the local search parameters. ...
Context 2
... to the previous formula, take m as 18. After iterative calculation, the better mileage shown in Table 4 is obtained, and the corresponding comparison is shown in Figure 10. e experimental results found that the calculation of the optimal route for the total mileage of the logistics transportation route calculated by the EY model has high accuracy, few iterations of the calculation, and high calculation efficiency. ...
Citations
... Before creating a simulation model, it is necessary to determine the depth to which the process needs to be investigated [11]. The accuracy and correctness of the simulation results are based on the agreement of the parameters of the real system elements with the simulation model. ...
The use of simulation models is currently a necessity, considering the complexity of the problems solved in many areas of engineering and scientific work (including logistics). This fact places high demands on their creation, use, and possible modification. When implementing simulation models, we often encounter limitations (depending on the software used), for which an effective solution is the application of the additional programming method. However, the method’s implementation is often associated with problems, the solution to which is, for example, the use of third-party services. Ultimately, this is an effective but lengthy process. The paper presents a new approach to the method of additional programming, which is based on using an addressable software application as a tool for generating sequences according to defined input parameters. The research was carried out using the simulation software Tecnomatix Plant Simulation and the software tool Simtalk, which is part of it.
... Mugo et al. [31] analyzed the spatial variation of rainfall and temperature in the county, and their effects on green gram production using Analytical Hierarchy Process (AHP) decision making tool in order to determine the perceived weights or influence that rainfall and temperature have on green gram production. In order to build an algorithm model to control or minimize logistics delays, Qia [32] investigated the likelihood of numerous elements that contribute to delays in logistics [21,33]. Their study developed an EY model-a BN model based on the genetic EM algorithm-based on the genetic EM algorithm and carried out associated simulation experiments based on the model to confirm its validity and viability. ...
Three growth models (Richards, Gompertz, and Weibull) were estimated using a computer program employing a modified version of the Levenberg-Marquardt approach for solving non-linear regression models. With both small and high sample sizes, three data sets were collected. The most suitable model for growth studies was added by decomposing the growth models into their component parts: additive and multiplicative error factors. The analysis based on the mathematical properties was conducted using Gretl statistics software. Prior to using an iterative strategy, a third-order polynomial (cubic function) solves the issue of the initial parameters. The final estimate of the parameters, standard errors, p-values, and model adequacy standards used to choose the best growth model are included in the result. Out of the five growth models considered suitable for the agricultural data, this study was able to pinpoint the Weibull Growth Model with Multiplicative Error Term. The Hill Growth Model with Additive Error Term is the best growth model for the engineering data out of the five growth models. Among the Five Growth Models for Population Growth, the Weibull Growth Model with Additive Error Term is a viable growth model. Several growth models are suggested or recommended by this study for use in forecasting this growth behavior.
