A new intelligent approach for air traffic control using gravitational search algorithm

Aircraft landing planning (ALP) is one of the most important challenging problems in the domain of air traffic control (ATC). Solving this NP-hard problem is a valuable aid in organizing air traffic in terminal control area (TCA), which itself leads to a decrease in aircraft fuel consumption, costs of airlines, and workload undertaken by air traffic controllers. In the present paper, the ALP problem is dealt with by applying effective rich knowledge to the optimization process (to remove obvious non-optimal solutions), and the first use of Gravitational Search Algorithm (GSA) in resolving such a case. In this regard, while the specific regulations for safe separation have been observed, the optimal landing time, the optimal runway, and the order of consecutive landings have been determined so that the main goal (minimizing total flight delays) would be best met. Results of simulations show that this approach, compared to previous ones, which are based on Genetic and Bionomic algorithms, GLS, and Scatter search method, considerably decreases total flight delays. Attaining zero in the total flight delays in three scenarios with real data shows that the suggested intelligent approach is more decisive than others in finding an optimal solution.     

A safety assessment methodology applied to CNS/ATM-based air traffic control system

In the last decades, the air traffic system has been changing to adapt itself to new social demands, mainly the safe growth of worldwide traffic capacity. Those changes are ruled by the Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) paradigm , based on digital communication technologies (mainly satellites) as a way of improving communication, surveillance, navigation and air traffic management services. However, CNS/ATM poses new challenges and needs, mainly related to the safety assessment process. In face of these new challenges, and considering the main characteristics of the CNS/ATM, a methodology is proposed at this work by combining “absolute” and “relative” safety assessment methods adopted by the International Civil Aviation Organization (ICAO) in ICAO Doc.9689 [14], using Fluid Stochastic Petri Nets (FSPN) as the modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed (in analysis) and the legacy system models. To demonstrate its usefulness, the proposed methodology was applied to the “Automatic Dependent Surveillance-Broadcasting” (ADS-B) based air traffic control system. As conclusions, the proposed methodology assured to assess CNS/ATM system safety properties, in which FSPN formalism provides important modeling capabilities, and discrete event simulation allowing the estimation of the desired safety metric.     

Solving short-term electric hydrothermal scheduling problems by exponential multiplier methods†

The short-term electric hydrothermal scheduling (STEHS) problem consists in optimizing the production of hydro and thermal electric generation units over a short time period (up to one week long). The problem described in this work can be modelled as a nonlinear network flow problem with linear and nonlinear side constraints. The minimization of this kind of problem can be performed by exploiting the efficiency of network flow techniques. It lies in minimizing approximately a series of augmented Lagrangian functions including only the side constraints, subject to balance constraints in the nodes and capacity bounds. One of the drawbacks of the multiplier methods with quadratic penalty function is that the augmented Lagrangian is not twice differentiable when it is applied to problems with inequality constraints. This article overcomes this difficulty by using the exponential multiplier method. In order to improve the performance some parameters are tuned. The efficiency of this method over STEHS test problems is illustrated by comparing its CPU-times with those of the quadratic multiplier method and with those of the general purpose codes MINOS, SNOPT, and KNITRO. Numerical results are promising.     

基于Plant Simulation的航空综合机加厂房布局仿真研究

科学的生产设置布局规划对航空制造业降低生产成本、提高产品质量尤为重要.以某民用航空发动机传动系统的齿轮和机匣综合加工厂房的规划设计为例,根据厂房设施布置的一般原则,以齿轮和机匣的年产量目标、产品加工工艺、单工艺加工面积需求为设计输入,并考虑各加工区的加工特点而带来的位置约束性,采用遗传算法与模拟退火算法相结合的混合遗传算法为优化工具,将特定的功能区固化在基因串特定的位置上来满足位置约束．计算得出优化方案后,以Plant Simulation为仿真平台,建立该综合机加厂房的仿真模型,从产量满足率、设备利用率、在制品库存量和生产线稳健性等多个指标进行了设施布局的仿真评价．结果表明,优化后的系统能够很好满足生产纲领,各关键设备负载比较均衡,同时维持低水平的在制品库存量,且生产线稳健性较好．因此,综合运用混合遗传算法与Plant Simulation仿真可以为生产设施布局问题给出一种有效、直观的解决方案,且由离散事件仿真获取的评价指标能深刻体现方案的优劣．     

Analysis of steel‐reinforced masonry walls regarding maximum shear loads / Traglastberechnung von vertikal bewehrten Mauerwerksscheiben

Loadings on masonry for the earthquake case pose particular challenges for the material. In order to improve the load‐bearing and deformation behaviour, masonry building elements can be strengthened with reinforcement. This article presents an analytical model for the calculation of the load‐bearing capacity of vertically reinforced masonry panels. The masonry is modelled as a homogeneous and anisotropic material and failure conditions are based on the plastic theory. Using uniaxially loaded stress fields and considering the structural constraints, a lower load‐bearing threshold can be given. In order to verify the model, three shear tests on reinforced sand‐lime block masonry were recalculated regarding their load‐bearing capacity. The test panels each contained vertical steel reinforcement in the blocks. The blocks were laid in thin bed mortar.     

Optimizing the configuration of a keyboard assembly cell

This paper describes how the configuration of a three station serial keyboard assembly cell can be optimized using a combination of discrete event simulation modelling and experimental design techniques. The investigation is based on a discrete event simulation model written in ARENATM. Optimum buffer sizes and number of pallets are initially determined by factorial design to identify the most significant factors affecting the throughput of the cell. Response surface methodology is then used to determine the optimal settings. The paper illustrates the methodology of using a combination of discrete event simulation modelling and experimental design techniques to design the optimal configuration of serial assembly cells and similar configurations in the manufacturing environment.     

Discrete event control of production flows: Make-to-stock and make-toorder environments

This paper focuses on discrete event control of production flows. The modelled manufacturing system, based on multi-level bills of materials, is characterized by flexible machines with negligible setups and fixed production rates. Two formulations of the production flow control problem are stated as dynamic models for two contrasting make-to-stock and make-to-order environments. Study of these models with the aid of the maximum principle reveals that there are common properties for their optimal solution despite the inherent differences in the two production environments. As a result, a fast time-decomposition algorithm is suggested which takes advantage of those analytical properties to solve the problem.     

Advanced control in factory automation: a survey

This paper provides a survey of the main advanced control techniques currently adopted in factory automation. In particular, it focuses on five classes of control approaches, namely: model-based control, control based on computational intelligence, adaptive control, discrete event systems-based control and finally event-triggered and self-triggered control. A particular focus is put on the most significant and recent contributions in these areas with attention to their application in the factory automation domain. Finally, open issues, challenges and the requirements of further research efforts for each class are pointed out.     

Derived heuristics-based consistent optimization of material flow in a gold processing plant

Material flow in a chemical processing plant often follows complicated control laws and involves plant capacity constraints. Importantly, the process involves discrete scenarios which when modelled in a programming format involves if–then–else statements. Therefore, a formulation of an optimization problem of such processes becomes complicated with nonlinear and non-differentiable objective and constraint functions. In handling such problems using classical point-based approaches, users often have to resort to modifications and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, these facts are demonstrated by showing results from MATLAB®'s well-known fmincon routine. Thereafter, a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem is developed. Although the evolutionary approach produced results with comparatively less variance over multiple runs, the performance has been enhanced by introducing derived heuristics associated with the problem. In this article, the development and usage of derived heuristics in a practical problem are presented and their importance in a quick convergence of the overall algorithm is demonstrated.     

Developing a fuzzy proportional–derivative controller optimization engine for engineering design optimization problems

In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.     

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.     

An optimization technique using the characteristics of genetic algorithm

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.     

A Cumulative Sum scheme for monitoring frequency and size of an event

This article proposes a Cumulative Sum (CUSUM) scheme, called the TC‐CUSUM scheme, for monitoring a negative or hazardous event. This scheme is developed using a two‐dimensional Markov model. It is able to check both the time interval (T) between occurrences of the event and the size (C) of each occurrence. For example, a traffic accident may be defined as an event, and the number of injured victims in each case is the event size. Our studies show that the TC‐CUSUM scheme is several times more effective than many existing charts for event monitoring, so that cost or loss incurred by an event can be reduced by using this scheme. Moreover, the TC‐CUSUM scheme performs more uniformly than other charts for detecting both T shift and C shift, as well as the joint shift in T and C. The improvement in the performance is achieved because of the use of the CUSUM feature and the simultaneous monitoring of T and C. The TC‐CUSUM scheme can be applied in manufacturing systems, and especially in non‐manufacturing sectors (e.g. supply chain management, health‐care industry, disaster management, and security control). Copyright © 2009 John Wiley & Sons, Ltd.     

The British Airways human factors reporting programme

This paper describes three safety programmes that provide BA flight operations with feedback on operational quality. Two of these are flight data recording and air safety reporting. These earlier programmes are described primarily to set in context the development of the most recent of the three, the human factors reporting programme (HFR). This is a confidential programme that records and analyses data using a distinctly different process from most reporting programmes. It requests not only information about ‘What’ happened in an event but ‘Why’ the event occurred and ‘How’ the crew dealt with the problem. Analysis uses ‘factors’ describing crew actions and the influences on crew actions. Actions and influences can describe safety positive as well as negative events and the analysis produces causal event sequence diagrams of each reported event. The paper describes these processes and offers some illustrative examples taken from the database.     

Finding all global optima of engineering design problems with discrete signomial terms

ABSTRACT

Many engineering design problems are frequently modelled as nonlinear programming problems with discrete signomial terms. In general, signomial programs are very difficult to solve for obtaining the globally optimal solution. This study reformulates the engineering design problem with discrete signomial terms as a mixed-integer linear program and finds all alternative global optima. Compared with existing exact methods, the proposed method uses fewer variables and constraints in the reformulated model and therefore efficiently solves the engineering problem to derive all global optima. Illustrative examples from the literature are solved to demonstrate the usefulness and efficiency of the proposed method.     

Modelling road accident blackspots data with the discrete generalized Pareto distribution

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003–2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ + 1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov–Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed.     

Designing for safety: the ‘free flight’ air traffic management concept

This paper describes the conceptual design and validation of an air traffic management (ATM) concept and the role the safety and human factors played in this design and validation process. The free flight (FF) concept is characterised by being a direct route concept where the pilots, instead of the air traffic controller, are responsible for the separation assurance. Moving this task to the cockpit has consequences for the man machine interface in the cockpit, which needs to be modified to accommodate this new task (micro level design). On top of that, a set of rules and procedures are required to ensure an efficient and safe traffic flow (macro level design). Both the micro and macro aspect of this design are intertwined and require an accurate tuning to arrive at an overall acceptable solution. Both micro-level (flight simulator) experiments and macro-level (traffic simulations) experiments have been conducted to investigate the feasibility of this concept after optimising the initial conceptual design.     

Optimal series–parallel topology of multi-state system with two failure modes

In this paper, an algorithm for optimal allocation of multi-state elements (MEs) in acyclic transmission networks (ATNs) is suggested. The ATNs consist of a number of positions (nodes) in which MEs capable of receiving and sending a signal are allocated. Each network has a root position where the signal source is located, a number of leaf positions that can only receive a signal, and a number of intermediate positions containing MEs capable of transmitting the received signal to some other nodes. Each ME that is located in a nonleaf node can have different states determined by a set of nodes receiving the signal directly from this ME. The probability of each state is assumed to be known for each ME. The ATN reliability is defined as the probability that a signal from the root node is transmitted to each leaf node.The optimal distribution of MEs with different characteristics among ATN positions provides the greatest possible ATN reliability. The suggested algorithm is based on using a universal generating function technique for network reliability evaluation. A genetic algorithm is used as the optimization tool. Illustrative examples are presented.     

A stochastic programming approach to manufacturing flow control

This paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing flow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for the finite horizon continuous time stochastic control problem are discretized over a set of sampled times; this defines an associated discrete time stochastic control problem which, due to the finiteness of the sample path set for the Markov disturbance process, can be written as a stochastic programming problem; and (ii) the very large event tree representing the sample path set is replaced with a reduced tree obtained by randomly sampling over the set of all possible paths. It is shown that the solution of the stochastic program defined on the randomly sampled tree converges toward the solution of the discrete time control problem when the sample size increases to infinity. The discrete time control problem solution converges to the solution of the flow control problem when the discretization mesh tends to zero. A comparison with a direct numerical solution of the dynamic programming equations is made for a single part manufacturing flow control model in order to illustrate the convergence properties. Applications to larger models affected by the curse of dimensionality in a standard dynamic programming techniques show the possible advantages of the method.     

Vision-based horizon extraction for micro air vehicle flight control

Recently, more and more research has been done on micro air vehicles (MAVs). An autonomous flight control system is necessary for developing practical MAVs to be used for a wide array of missions. Due to the limitations of size, weight, and power, MAVs have the very low payload capacity and moments of inertia. The current technologies with rate and acceleration sensors applied on larger aircrafts are impractical to MAVs, and they are difficult to be scaled down to satisfy the demands of MAVs. Since surveillance has been considered as the primary mission of MAVs, it is essential for MAVs to be equipped with on-board imaging sensors such as cameras, which have rich information content. So vision-based techniques, without increasing the MAVs payload, may be a feasible idea for flight autonomy of MAVs. In this paper, a new robust horizon extraction algorithm based on the orientation projection method is proposed, which is the foundation of a vision-based flight control system. The horizon extraction algorithm is effective for both color images and gray images. The horizon can be extracted not only from fine images captured in fair conditions but also from blurred images captured in cloudy, even foggy days. In order to raise the computational speed to meet real-time requirements, the algorithmic optimization is also discussed in the paper, which is timesaving by narrowing the seeking scope of orientations and adopting the table look-up method. According to the orientation and position of the horizon in the image, two important angular attitude parameters for stability and control, the roll angle and the pitch angle, could be calculated. Several experimental results demonstrate the feasibility and robustness of the algorithm.