Logistics distribution vehicle planning is an important issue in logistics transportation activities, and it is also a research hotspot in theoretical circles at home and abroad. At present, many studies have focused on establishing vehicle planning models and optimizing vehicle planning in different environments and have achieved rich results. As an important part of transportation production process, the efficiency of logistics distribution is very important to the whole production process. Especially for emergency logistics, every minute is very critical for emergency situations such as disaster relief. In order to improve the efficiency of emergency logistics, this paper applies multiagent technology to emergency logistics and puts forward an integrated modeling method of enterprise macromodeling, business process mesomodeling, and micromodel design. Using the agent-oriented system development method, an emergency logistics distribution vehicle planning model system is established. The development process of multiagent automatic trading system is described. The results show that it is feasible and effective to use multi-intelligent fuselage technology for emergency logistics distribution vehicle planning and decision-making. The algorithm proposed in this paper has advantages over the container order sequence processing scheme, and the total cost of order acceptance decreases sharply in the initial stage, which shows the practical convergence of the algorithm. The adjacency search method and Tabu search method deal with the calculation of total labor cost, and the Tabu neighborhood search algorithm obtains better results with lower labor cost.

Occupational stress suffered by care workers is supposed to be related with work performances and turnover rates of care facilities, which is highly significant for the development of nursing care industry. Through a comparative analysis of occupational stress suffered by long-term care workers in Japan and China, the present study attempted to discover the kinds of job-related stress care workers tended to have and what the principal underlying factors resulting in high levels of stress may be in each country. Data were collected by a questionnaire survey using the Brief Job Stress Questionnaire (BJSQ) from 3 facilities in China and 3 facilities in Japan, and then the obtained data were analyzed by cutoff point method and k-clustering method to discover the difference of stress suffered by nursing care workers in the two countries. Besides, discriminant analyses were also used to find out how stress may differ by country, gender, working night shift or not. According to the results, the occupational stress suffered by Chinese respondents was averagely lower than Japanese respondents, and the occupational stress is different for workers with different gender and workers working with night shift or not. Generally, the present study discovered the difference of occupational stress suffered by different crowds working in Japanese and Chinese long-term care facilities using the method of cutoff point method, k-clustering method, and discriminant analysis method. Besides, key related factors are identified for different crowds of respondents, and corresponding suggestions are presented for improving occupational stress management.

No abstract

Work analysis is one of critical tools for improving order picking efficiency in logistics centers. Traditionally, video analysis has been used to analyze work time for various tasks. However, video analysis generally requires a substantial amount of time and labor, which makes it too inefficient for real-time work improvement. To address this, a novel automatic work analysis method is proposed for analyzing the order picking process in logistics centers in this study. The automatic work analysis method can be divided into two parts: dynamic positioning and motion analysis. With the proposed method, an ultrasonic network and a tracking camera are used for measuring a worker's real-time position while he/she is moving, and an acceleration sensor is used for checking the acceleration of the dominant working hand. Using the position and acceleration data, the worker's motion is then estimated by an estimation model pre-tested for accuracy. To test the effectiveness of the proposed method, an experiment was conducted in which the measurement error of positioning was found to be approximately 0.01±0.19 m for the 𝑥-coordinate values and 0.03±0.45 m for the 𝑦-coordinate values. The total measurement error was confirmed to be within a one-step stride. The results of the motion analysis were more than 90% consistent with those obtained by traditional video analysis.