An automated bi-level optimization approach for IMRT

Intensity-modulated radiation therapy is used worldwide to treat cancer patients. The objective of this treatment is to deliver a prescribed radiation dose to the tumor while sparing, as much as possible, all the healthy tissues, especially organs at risk (OAR). This means that the planning of a radiotherapy treatment should take into consideration conflicting objectives: to be able to spare as much as possible the OAR guaranteeing, at the same time, that the desired radiation is delivered to the volumes to treat. While the volumes to treat can be adequately irradiated from almost any set of directions, the radiation directions that are chosen have a determinant impact on the OAR. This means that those directions that provide an improved OAR sparing should be selected. The choice of radiation directions (beam angles) can thus be interpreted as being fundamentally determined by the OAR, with the radiation intensities associated with each of these directions being determined by the needed radiation to be delivered to the volumes to treat. In this work, we interpret the radiotherapy treatment planning problem as a bi-level optimization problem. At the upper level, OAR control the choice of the beam angles, which are selected aiming at OAR sparing. At the lower level, the optimal radiation intensities are decided by the volumes to treat, considering the beam angle ensemble obtained at the upper level. The proposed bi-level approach was tested using 10 clinical head-and-neck cancer cases already treated at the Portuguese Institute of Oncology in Coimbra.     

基于正则化的混合准则放疗规划模型的改进

针对调强放射治疗中基于等效均匀剂量线性目标函数的物理生物混合准则模型的不足,利用正则化理论构造了最大函数的平滑和凸正则化函数,改进了基于等效均匀剂量线性目标函数的混合准则放疗规划模型,解决了原模型限制优化算法寻优能力和难以确定梯度算法步长的问题。实验证明,该方法可以在保证相似靶区剂量覆盖特性的前提下,更好地保护危及器官,提高了放疗计划质量。     

基于启发式信息的非凸放疗规划模型的求解方法

针对调强放疗逆向优化过程中的关键环节——各照射野的强度照射分布图在带有剂量体积曲线限制条件下的非凸数学规划问题,提出了一种新颖的更加科学的启发式信息——正规化空间内的空间距离排序值。与传统的剂量排序启发式信息相比较,新方法可以得到更好的解。一个简单示例和四个测试病例表明了该方法的有效性。     

DFANS: A highly efficient strategy for automated trust negotiation

Automated trust negotiation (ATN) is an approach establishing mutual trust between strangers wishing to share resources or conduct business by gradually requesting and disclosing digitally signed credentials. The digital credentials themselves are usually sensitive, so they have corresponding access control policies to control their disclosure. Therefore, an ATN strategy must be adopted to determine the search for a successful negotiation based on the access control policies. Previously proposed negotiation strategies are either not complete, disclosing irrelevant credentials, or not efficient enough. In this paper, we propose a novel ATN strategy, that is, Deterministic Finite Automaton Negotiation Strategy (DFANS). DFANS is complete and ensures that no irrelevant credentials are disclosed during the negotiation. Furthermore, DFANS is highly efficient. In the worst case, its communication complexity is O(n), where n is the total number of credentials requested, and its computational complexity is O(m) when not involving the cyclic dependencies, where m is the total size of the both sides' policies looked up during the negotiation. When cyclic dependencies exist, a reasonable additional cost of running OSBE protocol that is a provably secure and quite efficient scheme will be added to the computational cost of DFANS to guarantee the negotiation success whenever possible.     

改进的电弧弧长自适应T-S模糊观测器

通常基于Lyapunov方法设计自适应观测器时,要求系统满足Kalman—Yakubovitch—Popov（KYP）定理的约束．因此,通过增加辅助变量放宽了约束条件,对未知参数的一类非线性系统,提出了一种模糊自适应观测器,用来观测熔化极气体保护焊的弧长．在焊枪到工件的距离未知的情况下,可以有效地获得弧长变化信息．仿真结果验证了该方法的有效性．     

An anchorage planning strategy with safety and utilization considerations

Heavy maritime traffic and the subsequent increase in vessel density in anchorages have recently become a focal issue in maritime traffic safety. In this study, we consider the problem of determining the optimal berth locations of incoming vessels in an anchorage area with the goals of maximizing utilization and minimizing the risk of accidents. We introduce novel performance metrics aimed at measuring achievement of these two goals. In this context, we propose a multi-objective optimization strategy and benchmark it against current state-of-the-art anchorage planning algorithms using real-world data as well as Monte Carlo simulations. Our results indicate that the proposed strategy yields much safer berth locations while maintaining similar utilization levels.     

An enhanced genetic algorithm for automated assembly planning

Automated assembly planning reduces manufacturing manpower requirements and helps simplify product assembly planning, by clearly defining input data, and input data format, needed to complete an assembly plan. In addition, automation provides the computational power needed to find optimal or near-optimal assembly plans, even for complex mechanical products. As a result, modern manufacturing systems use, to an ever greater extent, automated assembly planning rather than technician-scheduled assembly planning. Thus, many current research reports describe efforts to develop more efficient automated assembly planning algorithms. Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present assembly planners based upon traditional genetic algorithms. Although prior genetic assembly planners find improved assembly plans with some success, they also tend to converge prematurely at local-optimal solutions. Thus, we present an assembly planner, based upon an enhanced genetic algorithm, that demonstrates improved searching characteristics over an assembly planner based upon a traditional genetic algorithm. In particular, our planner finds optimal or near-optimal solutions more reliably and more quickly than an assembly planner that uses a traditional genetic algorithm.     

Buffer planning for application-specific networks-on-chip design

Networks-on-chip (NoC) is a promising communication architecture for next generation SoC. The size of buffer used in on-chip routers impacts the silicon area and power consumption of NoC dominantly. It is important to plan the total buffer-size and each router buffer-allocation carefully for an efficient NoC design. In this paper, we propose two buffer planning algorithms for application-specific NoC design. More precisely, given the traffic parameters and performance constraints of target application, the proposed algorithms automatically determine minimal buffer budget and assign the buffer depth for each input channel in different routers. The experimental results show that the proposed algorithms can significantly reduce total buffer usage and guarantee the performance requirements. Supported by the National Natural Science Foundation of China (Grant No. 60803018)     

An improved heuristic for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is an important and practical problem in the OR literature. In short, the problem is to identify routes to service (e.g., pickup or deliver) demand located along the edges of a network such that the total cost of the routes is minimized. In general, a single route cannot satisfy the entire demand due to capacity constraints on the vehicles. CARP belongs to the set of NP-hard problems; consequently numerous heuristic and metaheuristic solution approaches have been developed to solve it. In this paper an “ellipse rule” based heuristic is proposed for the CARP. This approach is based on the path-scanning heuristic, one of the mostly used greedy-add heuristics for this problem. The innovation consists basically of selecting edges only inside ellipses when the vehicle is near the end of each route. This new approach was implemented and tested on three standard datasets and the solutions are compared against: (i) the original path-scanning heuristic; (ii) two other path-scanning heuristics and (iii) the three best known metaheuristics. The results indicate that the “ellipse rule” approach lead to improvements over the three path-scanning heuristics, reducing the average distance to the lower bound in the test problems by about 44%.     

A novel model for arc territory design: promoting Eulerian districts

The problem of district design for the implementation of arc routing activities is addressed. The aim is to partition a road network into a given number of sectors to facilitate the organization of the operations to be implemented within the region. This problem arises in numerous applications such as postal delivery, meter readings, winter gritting, road maintenance, and municipal solid waste collection. An integer linear programming model is proposed where a novel set of node parity constraints to favor Eulerian districts is introduced. Series of instances were solved to assess the impact of these parity constraints on the objective function and deadhead distance. Networks with up to 401 nodes and 764 edges were successfully solved. The model is useful at a tactical level as it can be used to promote workload balance, compactness, deadhead distance reduction and parity in districts.     

A genetic algorithm for a bi-objective capacitated arc routing problem

The capacitated arc routing problem (CARP) is a very hard vehicle routing problem for which the objective—in its classical form—is the minimization of the total cost of the routes. In addition, one can seek to minimize also the cost of the longest trip.In this paper, a multi-objective genetic algorithm is presented for this more realistic CARP. Inspired by the second version of the Non-dominated sorted genetic algorithm framework, the procedure is improved by using good constructive heuristics to seed the initial population and by including a local search procedure. The new framework and its different flavour is appraised on three sets of classical CARP instances comprising 81 files.Yet designed for a bi-objective problem, the best versions are competitive with state-of-the-art metaheuristics for the single objective CARP, both in terms of solution quality and computational efficiency: indeed, they retrieve a majority of proven optima and improve two best-known solutions.     

A robust dynamic planning strategy for lot-sizing problems with stochastic demands

We consider the question of generating robust plans for production planning problems under uncertainty. In particular, we present an alternative approach to generate robust solutions for lot-sizing problems with stochastic demand. The proposed approach is dynamic and includes a decision rule that guides the planner. The decision rule parameters are determined so that the number of expected planning adaptations and their magnitudes are under control. The robust approach and its related models are presented together with some computational results to show how it performs compared to other approaches.     

Memetic algorithm with route decomposing for periodic capacitated arc routing problem

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles (nv), and the other is the total cost (tc). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified.     

An operating strategy for high-availability multi-station transfer lines

Economic efficiency of a multi-station transfer line (TL) is evaluated directly by the quantity of parts produced; therefore, each single manufactured part counts. The contribution presents an approach which applies a reliability-adaptive operating strategy in combination with tool derating. It is the objective to hold the system harmonisation of tool changes as maintenance actions. The significant effectiveness of the approach is demonstrated by different configurations and contexts. The output-time function of a TL without reliability-adaptive control is compared with functions of a system with reliability-adaptive control.     

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems

To the average driver, the concept of automation in driving infers that they can become completely ‘hands and feet free’. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems.

Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.     

An adaptive system for modelling and simulation of electrical arc furnaces

Modelling and simulator development for electric arc furnaces (EAFs) are of significant importance in designing control systems and in performance optimisation of EAFs. This paper presents a method based on adaptive neuro-fuzzy inference systems (ANFIS) for modelling and simulating EAFs with the focus on the regulator loop that is used for positioning the electrodes. The effectiveness of the simulator is shown through experiments by comparing the simulator outputs with actual plant data, using the EAF of Gerdau Ameristeel Whitby (GAW) in Ontario, Canada.     

基于grey Markov--支持向量机的电弧炉终点参数预报

考虑电弧炉终点参数既受定量因素的影响,又受非定量因素的影响.将灰色马尔可夫 (grey Markov)与支持向量机 (SVM)相结合.建立了电弧炉终点参数grey Markov-SVM预报模型,其中grey Markov模型反映非定量因素对电弧炉终点参数预测值的影响,SVM模型反映电弧炉各种定量输入对终点参数预测值的影响.建立grey Markov-SVM模型的方法是:首先建立反映非定量因素的GM (1,1)模型,然后用Markov链修正其预测值:由于grey Markov模型对定量输入的影响无法准确反映,因此grey Mar-kov模型必然存在预测偏差,此预测偏差通过建立反映定量输入与终点参数预测偏差之间关系的SVM模型方法加以补偿,并采用粒子群算法 (PSO)对SVM的参数进行寻优,最终得到电弧炉终点参数的预报值,同时实现滚动预测.仿真实验表明grey Markov-SVM模型与grey-SVM模型、Markov-SVM相比较,具有很高的终点预报精度.     

Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary.In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm.     

基于Frenet坐标系的自动驾驶轨迹规划与优化算法

针对公路场景下作速度保持的自动驾驶汽车实时轨迹规划问题,提出一种基于Frenet坐标系的优化轨迹规划算法.首先,利用Frenet坐标系将车辆运动解耦, 构建无约束横向/纵向独立积分系统;然后,根据初始配置和可内嵌到行为层的目标配置,通过采样生成有限的4次、5次多项式候选轨迹集合;最后,利用以高斯卷积、加速度变化率为核心...