Performance of Heuristic Priority Rule for Multiskilled Resource-Constrained Scheduling

Nowadays, without a classification system that helps the project manager to decide which heuristic applies when mitigating the multiskilled resource-constrainedscheduling, they must try several of rules until they find one that compares favorably (shortest duration) with the results of the other heuristic priority rules. This study explored the twenty-three existing heuristics’ performance for multiskilled resource-constrained scheduling. The results found that the heuristics with good performance are TIMROS, TIMRES, ACROS, WCS ACS and ACTRES. Overall, they outperform the others in shortening the project duration. It should be pointed out that the heuristics dealing with the use of several information are likely better to get shorter project duration. There are enough statistical evidences to conclude that their criterions have a significant effect on reducing project duration by approximately 1-2 times of the standard deviation. The top four heuristics: TIMROS, TIMRES, ACROS and ACTRESS classified into the composite rule produced the lowest average of project duration. It is also found that Serial Schedule Scheme (SSS) underperformthe Partial Schedule Scheme (PSS). This study has the contribution for the project managers to decide which heuristic applies when mitigating the multiskilled resource overallocation problem in term of minimum project duration

A High-Efficiency FPGA-Based Accelerator for Binarized Neural Network

Although the convolutional neural network (CNN) has exhibited outstanding performance in various applications, the deployment of CNN on embedded and mobile devices is limited by the massive computations and memory footprint. To address these challenges, Courbariaux and co-workers put forward binarized neural network (BNN) which quantizes both the weights and activations to [Formula: see text]1. From the perspective of hardware, BNN can greatly simplify the computation and reduce the storage. In this work, we first present the algorithm optimizations to further binarize the first layer and the padding bits of BNN; then we propose a fully binarized CNN accelerator. With the Shuffle–Compute structure and the memory-aware computation schedule scheme, the proposed design can boost the performance for feature maps of different sizes and make full use of the memory bandwidth. To evaluate our design, we implement the accelerator on the Zynq ZC702 board, and the experiments on the SVHN and CIFAR-10 datasets show the state-of-the-art performance efficiency and resource efficiency.

Application Research of BIM Technology in Green Building Construction

Due to the complexity of the building and the comprehensiveness of multiple majors, a large number of uncertain factors, such as collision problems and construction schedule, often occur in the construction and lead to many resource waste problems which cannot be solved. The introduction of BIM technology into the construction of engineering projects can well overcome the collision in construction and complete the process of optimized construction schedule scheme through construction simulation to realize green building construction.

Real-Time Forecast of Tourists Distribution Based on the Improvedk-Means Method

Tourist distribution, a vector to reflect the tourist number of every scenic spot in a certain period of time, serves as the foundation for a scenic spots manager to make a schedule scheme. In this paper, a forecast model is offered to forecast tourist distribution. First of all, based on the analysis of changing mechanism of tourist distribution, it is believed that the possibility for a scenic spot to have the same tourist distribution next time is high. To conduct this forecast, we just need to research on the similar tourist distributions of which time and tourist scale are close. Considering that it is time-consuming, an improvedK-means cluster method is put forward to classify the historical data into several clusters so that little time will be needed to search for the most similar historical data. In the end, the case study of Jiuzhai Valley is adopted to illustrate the effectiveness of this forecast model.