A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.     

Experimental and numerical study on compressive performance of perforated brick masonry after fire exposure

This paper discusses the compressive performance of perforated brick masonry after fire exposure. Compressive strength tests of the mortar, clay perforated brick, and perforated brick masonry specimens were performed in accordance with ISO834 fire tests of different durations. The temperature distribution of the masonry materials and specimens was simulated using the finite element software ABAQUS, with the thermal parameters of masonry materials recommended by European standard Eurocode 6 and related literature. The compressive strength reduction factors of mortar and clay perforated brick exposed to different fire durations were calculated via the layered method suggested by European standard Eurocode 1. In addition, the compressive strength reduction factors after cooldown were obtained from the experimental data of the masonry materials, and by considering further reductions in the compressive strength after cooling from high temperatures. Experimental data of the masonry specimens were compared with the numerical results obtained using the reduction factors proposed in this work. The comparison revealed an overall acceptable approximation. Thus, the method presented in this paper can be used to evaluate the residual capacity of masonry structures after fire.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

Covariance matrix adaptation evolution strategy based design of fixed structure robust H∞ loop shaping controller

This paper proposes the application of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in fixed structure H_∞ loop shaping controller design. Integral Time Absolute Error (ITAE) performance requirement is incorporated as a constraint with an objective of maximization of stability margin in the fixed structure H_∞ loop shaping controller design problem. Pneumatic servo system, separating tower process and F18 fighter aircraft system are considered as test systems. The CMA-ES designed fixed structure H_∞ loop-shaping controller is compared with the traditional H_∞ loop shaping controller, non-smooth optimization and Heuristic Kalman Algorithm (HKA) based fixed structure H_∞ loop shaping controllers in terms of stability margin. 20% perturbation in the nominal plant is used to validate the robustness of the CMA-ES designed H_∞ loop shaping controller. The effect of Finite Word Length (FWL) is considered to show the implementation difficulties of controller in digital processors. Simulation results demonstrated that CMA-ES based fixed structure H_∞ loop shaping controller is suitable for real time implementation with good robust stability and performance.     

Nb和时效时间对热轧中锰TRIP钢中P偏聚的影响

热轧态中锰TRIP钢首先经650 ℃退火2 h,随后在550 ℃进行等温时效热处理,采用场发射扫描电镜(FE-SEM)研究该钢中P的偏聚和时效析出行为的变化情况。结果表明,中锰TRIP钢中P在晶界的偏聚是一种非平衡偏聚现象,临界时间约为50 h,与理论计算结果48 h较为吻合。在局部偏聚区域内,C与P存在共偏聚的关系,即P偏聚量高的地方,C含量也高。而合金元素Nb具有抑制P偏聚的效果,在20~70 h时效时间内,可以相对降低6.57%~19.5%的最大P偏聚量。根据电子背散射衍射(EBSD)菊池线分析,P偏聚量低于2.28at%时,P为固溶状态,高于2.28at.%时,P为析出状态。     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Use of conditioning time to investigate the mechanisms of interactions of selected fatty acids on hematite Part II laboratory investigations

It is the aim of this paper to examine the effects of conditioning time on the flotation of hematite using three technical grade fatty acid reagents as providing additional evidence on their mechanism of interaction with the hematite surface. Various mechanisms have been postulated as occurring as conditioning time is increased. Both physical (e.g. conditioning time and power input) and chemical (nature, dispersion and solubility of the adsorbing species) contribute to the mechanisms of attachment of collector. In this paper, the mechanism of attachment of oleate to hematite can be readily explained by chemisorption, but the mechanism of attachment of lauric acid appears to be physical adsorption at neutral pH. The flotation of hematite with a mixture of tallow-type fatty acids (palmitic, stearic and oleic acids) is very sensitive to conditioning time, and suggests that, even though flotation is very effective at short conditioning times, it is very susceptible to the presence of fines and their associated high surface areas. It is therefore obvious that both the physical and chemical conditions contribute to the mechanisms of adsorption of fatty acids on iron-containing oxide minerals and must be understood in order to optimise the flotation of these minerals in an industrial situation.     

A multi-start central force optimization for global optimization

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.