Purpose: – The purpose of this paper is to identify global trends in the third-party logistics (3PL) industry, and with that to find out where the opportunities and challenges lie, what the critical success factors are, and how companies can position themselves well in China. As there is currently very limited knowledge about the 3PL industry in China, this study also intends to shed light in this area. Design/methodology/approach: – The study is conducted through focus group interviews with senior executives of leading 3PL companies in China followed by a survey with 70 logistics companies. Findings: – The key strengths of the logistics industry in China include numerous factors such as good connectivity and new infrastructure, while its main weaknesses are a shortage of qualified staff and slow adoption of technologies. There are a number of concerns and issues raised, such as lack of qualified staff, oversupply of warehousing space in China, competition from the influx of foreign 3PL companies to China, and regulations on free trade zones, seaports and airports, all of which may have policy implications. Research limitations/implications: – As most of the logistics companies in the study are located and dominating in the Eastern and Southern China, it may be useful to conduct similar study in the new emerging western and central regions of China for comparison. Practical implications: – Managers and investors will appreciate the challenges and opportunities in logistics services in China and have a better insight into Chinese logistics development. Originality/value: – While China has been viewed as a cost effective place for manufacturing, the logistical costs in China are still very high as compared to other developed countries. This research will highlight the key reasons for the high logistical cost in China.

Simulation is widely used to evaluate the performance and optimize the design of a complex system. In the past few decades, a great deal of research has been devoted to solving simulation optimization problems, perhaps owing to their generality. However, although there are many problems of practical interests that can be cast in the framework of simulation optimization, it is often difficult to obtain an understanding of their structure, making them very challenging. Direct search methods are a class of deterministic optimization methods particularly designed for black-box optimization problems. In this paper, we present a class of direct search methods for simulation optimization problems with stochastic noise. The optimization problem is approximated using a sample average approximation scheme. We propose an adaptive sampling scheme to improve the efficiency of direct search methods and prove the consistency of the solutions.

This article reports a trajectory tracking control technique for snake robots with sideslip elimination and coefficient approximation. By introducing an integral part and virtual input variables to optimize the line-of-sight guidance law, a closed-loop trajectory tracking system with the functions of canceling disturbance and sideslip is designed. Besides, the method constructs the time-varying predicted values of virtual model variables and viscous friction coefficients to approximate the system's unmeasurable states. The approximation value can compensate for a snake robot's joint offset and torque input. Then, it is proved via the Lyapunov approach that the designed system is stable. The remarkable advantage of this strategy is that the accuracy of a snake robot tracking the ideal trajectory is optimized, which can improve the error's stability and the body's adaptability to the surroundings. The simulation and experimental results confirm the usefulness of the proposed technique.