An optimization method based on an improved least squares support vector regression machine（LS-SVR） and the technique for order preference by similarity to an ideal solution（TOPSIS） was proposed for the parameter optimization of fused deposition molding（FDM） multiresponse that simultaneously reduced warpage and shortened printing time. Firstly,the print speed was considered as a functional parameter and modeled using a Bezier curve. The curve definition points together with nozzle temperature,hotbed temperature,and fill rate were the design variables.The experimental design was carried out by Latin hypercube sampling. Secondly,the LS-SVR based on the kernel function improved by the Fréchet distance was used to model the action relationship between parameters and each response,so that the functional parameters were embedded in the overall model in a functional form. Finally,the non-dominated sorting genetic algorithm Ⅱ and TOPSIS were used to find and determine the optimal combination of parameters. The optimization results show that the proposed method is better than the LS-SVR method based on Euclidean distance and the LS-SVR based on scalar parameters,which illustrates the effectiveness of the method.
As a critical activity of urban waterlog response,reasonable allocation of pumping equipment can not only decrease economic costs,but also decrease a series of risks,such as the loss of residents’ property and casualties. This paper presented a two-stage stochastic nonlinear programming model with objectives of minimizing operational and transportation costs of pumping equipment,and penalty cost（which is a function of pumping time）. The quantity of pumping equipment,pumping time and transportation time were constructed as a nonlinear term,and piecewise linear approximation was designed. A progressive hedging-based solution methodology was designed for large-scale problems. Pudong District of Shanghai was employed as the research region. Rainfall intensity and scope were considered to design a serial of scenarios for numerical studies. The results show that piecewise intervals have great impact on error of total costs and computational time. Moreover,the proposed algorithm is efficient for large-scale problems.
The objective in this study was to investigate the effects of multitasking type and information modality on task performance and users’ subjective perceptions in multitasking with intelligent assistant. An experiment with a two-factor within-subject design was used to test task performance,perceived ease of use,perceived usefulness,intention to use and cognitive load. Thirty participants were recruited to complete parallel multitasking and sequential multitasking tasks in three information presentation scenarios（visual-auditory bimodality, visual unimodality, and auditory unimodality）. Obtained data was analyzed by repeated measures ANOVA. The results show that parallel multitasking takes significantly less time to complete than sequential multitasking. Visualauditory bimodality is significantly better than visual unimodality and auditory unimodality in terms of task completion time and subjective perceptions. The interaction of multitasking type and information modality is not significant. The experiment provides a theoretical basis and practical guidance for the design of information presentation modes and task organization modes for intelligent assistant.
In an uncertain environment,the project planning and controlling are very important for the smooth implementation of the project. Based on the simulation analysis,how to determine the best combination of project input parameters for robust scheduling optimization under random activity duration was studied. Firstly,the research problem was defined. Then,a robust project scheduling optimization model was constructed. The simulation analysis process was designed. The corresponding relation between project input parameters and simulation indicators was analyzed. Next,the tabu search heuristic algorithm was developed to solve the robust scheduling optimization model.The simulation implementation process was described. Finally,an real case was used to illustrate the research. The results show that the robustness of schedule increases with the increase of project schedule duration and resource availability and remains stable after reaching a certain value. When different simulation indicators are selected,the optimal combination of the input parameters is different. This allows the manager to select the corresponding indices according to their actual needs.Guidance for robust project scheduling under uncertain conditions is provided. Improvement of robustness of project scheduling is made through validation.
Drones have been used to improve the efficiency of urban logistics and distribution "last mile" because they are not restricted by terrain. However,they were limited by the maximum flight time and maximum load. Combining with the characteristics of truck and drone delivery,a class of truck distribution route optimization problem with drone assistance was studied. Considering the factors such as the maximum flight time,maximum load and flight speed of drone,a mixed integer programming model was established to minimize the delivery completion time. A hybrid variable neighborhood search algorithm combined with adaptive K-means clustering search was designed. A calculation example was constructed based on Solomon Benchmark C101,R101,and RC101. The analysis results show that the hybrid variable neighborhood search algorithm embedded with a simple heuristic algorithm can solve the proposed routing optimization problem and improve the timeliness of logistics and distribution services. The less the flight speed is affected by the load and the longer the flight duration,the more conducive to shortening the delivery time.
