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With the integration of informatization and industrialization, automated guided vehicles (AGVs) are widely used in material transportation. How to plan the path of multiple AGVs so that multiple AGVs can perform tasks safely and efficiently has become the key to improving the production efficiency of enterprises. In response to this problem, this paper first analyzes the causes of path conflicts and deadlocks; then uses the grid method to build a map model while avoiding deadlocks and path conflicts, and chooses the A* algorithm to plan the path; finally, a multi-AGVs dynamic path planning method is designed with the total default time, total time, and total path length as evaluation indicators. This method searches the paths of multiple AGVs in a rolling manner, stores the paths in the OPEN table and simplifies the paths to improve the actual cost. The design simulation in this paper reduces the total default time by 45.13%, reduces the total time by 1.64%, and reduces the total path length by 0.19%, which verifies the effectiveness of the multi-AGVs dynamic path planning method.

Reheating furnaces are the main energy consumption equipment in hot rolling production line. The reasonable scheduling is very important to reduce energy consumption and production cost. According to the production technology and constraint conditions of reheating process, the mathematic model is established. According to decomposition strategy, a hybrid algorithm based on the combination of mathematical programming and constraints programming is proposed. Computational results demonstrate the effectiveness of proposed approach.

Femtocaching is a distributed caching system designed to assist the macro base station by handling the popular content downloading. Usually, femto base stations (FBSs) have weak backhaul links but large storage capacity. When the requested files are cached, FBSs serve mobile users (MUs) through high-rate wireless link and avoid the backhaul bottleneck. However, the distributed caching problem is NP-complete. Previous works solve this problem with greedy algorithms in a centralized way. In this paper, we formulate this problem as an exact potential game (EPG), where each FBS acts as a game player with the constraint of cache size. The best Nash equilibrium (NE) is the global optimum solution of the distributed caching problem. We design an iterative algorithm in a decentralized way, where only local information exchange is needed. Theoretical analysis shows the best NE can be achieved and simulation results illustrate that the proposed algorithm performs better than the simple popular cache system and the traditional methods.

According to the present works, it is more efficient by carrying out accelerated degradation test (ADT) with step-down stress than constant and step-up stress. However, maximum likelihood estimation (MLE) is currently the main analysis method for step-down stress accelerated degradation test (SDSADT), which is difficult to be applied into small sample size occasion. To solve this problem, the paper proposes a Bayesian analysis method for SDSADT based on Wiener process. Firstly, degradation path of product performance is described by Wiener process, and then data analysis of SDSADT is carried out by a segmented Bayesian method which can update the evaluation values of model parameters under different accelerated stresses. Lastly, recursive algorithm for parameters estimation is established for reliability assessment of SDSADT. Simulation studies show that the method is more accuracy by comparing with MLE in case of small sample size.

The crystallization behavior of amorphous Ni-P nanoparticles produced by liquid pulsed-discharge was studied by using in situ high temperature XRD at beamline 4B9A of Beijing Synchrotron Radiation Facility. Transmission electron microscope (TEM) was used to observe the morphology and Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) was used to analyze the chemical composition of the as-prepared Ni-P nanoparticles. TEM results show that the average size of the as-prepared nanoparticles is about 13.5 nm. ICP-AES identifies the Ni-P nanoparticles contain 13.16 wt. % (21.85 at. %) of P and 86.84 wt. % (78.15% at. %) of Ni. Eight XRD patterns were, respectively, collected at 300, 373, 473, 573, 673, 773, 873 and 973K under low-vacuum condition (0.1 Pa). XRD results show that the as-prepared Ni-P nanoparticles are amorphous, no peaks of crystalline phases can be observed until 573K. Afterwards, the crystallization of the amorphous phase undergoes the formation and decomposition of some metastable phases. Finally, the obtained stable phases are the bct Ni3P and fcc Ni cryatalline phases. Both are randomly distributed in the sample. The crystallization mechanisms of the as-prepared amorphous Ni-P nanoparticles has also been discussed at the end of this paper.

Accelerated life testing (ALT) can be utilized to shorten the test duration for demonstrating the high reliability target with a long mission time under the use condition in reliability demonstration test (RDT). In this paper, a reliability acceptance sampling plan (RASP) for Weibull distribution with a nonconstant shape parameter under the acceleration condition is designed to demonstrate the reliability target with the lifetime at the specified reliability as the reliability measure. An example is illustrated to validate the proposed test method. Finally, the effects of the model parameter on the RASPs are analyzed.

Accelerated degradation test can be used to demonstrate the reliability target of highly reliable products with fully utilizing the degradation data within feasible test duration. There are a few works dealing with reliability demonstration based on degradation data. Most existing methods are applications with respect to linear degradation models and Wiener process models, which cannot describe the monotonic degradation process, such as fatigue cracks development of alloy products, wear of bearings, erosion of pipes, while Gamma processes are suitable. A method for accelerated degradation demonstration test based on Gamma process models is proposed in this paper. Firstly, reliability target is translated into the requirement of performance degradation. Then, a demonstration test plan based on Gamma process models is designed according to the requirements of the consumer risk and producer risk. The method is illustrated and validated using a simulated example.

Accelerated degradation testing (ADT) provides an effective way to assess the reliability of products. In engineering application, reliability of some complex products is often characterized by multiple performance parameters. However, current methods for ADT are mostly focused on the case which involves single performance parameter. This paper proposes an analysis method for ADT based on multiple performance parameters, by considering the correlation among performance parameters. From the results of application examples, the analysis procedure of the proposed method is more reasonable and the estimates are more exact.

The greater hinggan mountains in China is the green treasure, containing enormous economic, ecological and social benefits. However, due to the years of unreasonable deforestation structure, gradual degradation of the forest function and forest ecological environment hinders the mountains' development. Speed up constructing the mountains' ecological function areas becomes the primary task of promoting the mountains' sustainable development. Huge funds required for ecological reconstruction becomes another urgent need to address the current problems. This paper combines the mountains' regional characteristics and analyze the carbon sequestration value from the point of environmental ecology and ecological economics and puts forward strengthening the research of the mountains' carbon resource analysis and the financial strategy ,exploring new ways for the carbon finance .It contributes to raising funds for the forests' construction, accelerating the mountains' ecological function areas' development and promoting the mountains' rapid development of forestry.

Accelerated testing (AT) is widely used to demonstrate and assess product reliability and is especially useful for products with long life and high reliability requirements. Currently, research is primarily focused on test planning, acceleration model and parameter estimation for AT of specific products, such as electronic and mechanical components, etc. In our application, for several components of the system undergoing AT we desire to obtain the system reliability at the lower limit confidence, which is a considerable issue. However little research on this issue is presently available. This paper proposes a feasible method, which converts component data from AT to equivalent binomial component data and then estimates the approximate lower limit confidence (ALLC) of system reliability using a Bayesian method. A numerical example is illustrated to verify that the proposed method is better than the alternative methods by Monte Carlo (MC) simulation. Eventually, the method is applied to the assessment of storage reliability for a safety valve.

In recent years, constraint programming (CP) has been widely applied to solved scheduling problems. As one of the key elements in CP, constraint propagation has been proved to be an efficient methodology to speed up the search process. In this paper, we develop a hybrid branch and bound algorithm with CP technique, called HCPBAB, which focuses on branches cutting to achieve outstanding performance tuning and constraints handling ability compared with the original method. Moreover, we extend the popular input/output negation, input-or-output constraint propagation process into HCPBAB to extensively test our proposed method. Experimental results on 40 instances taken from standard job shop benchmarks show that 33 instances are optimally solved while HCPBAB obtains 11 better solutions compared with Rego C's results in 2009.

A Bayesian method is proposed for analyzing incomplete data obtained from constant stress accelerated life testing when there are two or more failure modes, or competing failure modes. The incompleteness is mainly due to censoring, as well as masking which might be the case that the failure time is observed, but its corresponding failure mode is not identified. Furthermore, sensitivity analysis is performed to check model sensitivity. The method is illustrated by an example for validation, which also shows that the method provides better estimators than MLE by incorporating available prior information in the case of small sample sizes.