Simulation-Based Rail Traffic Optimizations Applying Multicriterial Evaluations of Variants

Simultaneous locomotive assignment and train scheduling on a single-track railway line: A simulation-based optimization approach

Graph matching problem, also known as quadratic assignment problem (QAP) in a practical context, aims at finding the best correspondence between two given graphs. If two nodes are connected in one graph, it would be ideal if their corresponding images are connected in the other. The optimal solution should maximize the number of matches between the two graphs, while minimizing the weight of mismatches. Graph isomorphism is a particular case where an exact correspondence with no mismatches could be found. The quadratic assignment problem itself is hard to solve due to its high computational complexity. In this paper, the researcher analyzes two optimization approaches in detail: convex relaxation and the Fast Approximate QAP algorithm, both trying to solve the problem with lower computational effort. The performances of these two methods are further compared through numerical experiments. This paper focuses on pairs of equal-sized undirected graphs. ρ correlated graphs are constructed to simulate the noise in inexact graph matching problems. After comparing two existing methods, we propose an even faster optimization approach with comparable accuracy. This new approach provides a particular way to approximate an ideal initialization for the traditional Fast Approximate QAP algorithm. The significant reduction in the runtime makes obtaining good correspondence between large graphs feasible.

In this study, an optimal adaptive control approach is established to solve the robust output tracking problem of a class of continuous time uncertain linear systems based on the policy iteration (PI) in actor-critic algorithm. First, by augmenting the integral variables of the tracking error into state variables, the robust tracking problem is transformed into a robust control problem of an augmented uncertain linear system. It is proven that the robust control law of the augmented system enables the output of the considered system to track a polynomial time signal asymptotically. Second, an optimal control method in the corresponding auxiliary nominal system is established, and based on the Bellman optimality principle, PI algorithms are proposed to solve online tracking controllers for the matched and the mismatched uncertain systems. Finally, for testing the availability of the proposed approach and theoretical results, two numerical experiments are provided.

Load shedding and distributed resource control of stream processing networks

This article describes an optimization design of a sugar evaporative crystallizer using numerical simulation experiments. Computational fluid dynamics (CFD) is used to simulate the circulation flow of sugar juice in the crystallizer, and the numerical experiments are designed according to orthogonal experiment matrix. First, based on the specific working characteristics of sugar evaporative crystallizer, major factors influencing the cane sugar evaporative crystallization process were identified. Then CFD software was utilized to establish various three‐dimensional CFD models of the sugar evaporative crystallization with different factors and levels. Statistics software (IBM SPSS) is used to analyze the CFD simulation results. In order to measure circulation velocity under different conditions, physical experiments were conducted in the experiment platform in our laboratory. The circulation velocity measured under the optimal condition was increased by 128%, and the crystallization efficiency could be guaranteed.Practical ApplicationsComputation fluid dynamics (CFD) enables studying the details within the crystallizer, making the invisible become visible. The orthogonal test replaces all tests with partial tests. The orthogonal test coupled CFD technique can hugely decrease the cost of optimization design. The application of this method will benefit the optimization design of food engineering.

There are many eyewitness reports on unexpected water level change called ‘strange tide’ around the area of the radial sand ridges off the coast of Jiangsu Province, China, which has caused several fatal accidents in history. This study aims to prove whether a fast-moving atmospheric pressure disturbance can be a possible cause of the ‘strange tide.’ Based on the nonlinear shallow water model, a large number of scenarios for waves induced by a fast-moving pressure disturbance are numerically simulated and the maximum of the wave amplification ratio in each scenario is discussed. In each scenario, we assume an idealized pressure disturbance passing through the concerned area. The heading angle and translational speed of atmospheric disturbances are considered as variables. The results show that a fast-moving pressure disturbance can induce large wave level fluctuation in this area. In some special situations, the water level rise can be amplified more than 40 times than the water level change caused by a pressure disturbance with equivalent intensity under static equilibrium state. Both the heading angle and translational speed of atmospheric disturbances play a very important role in the occurrence of dangerous situation. The most dangerous combination of the two parameters is discussed, and the disaster prone locations in this area are identified considering the historical accidents and the numerical experiments.

In this paper the indoor wireless localization problem is addressed both from the theoretical and application standpoints. The main result of the paper is on the theoretical side: the topological definition of regular and irregular nodes is introduced, and formal results are presented to support regularity as a desirable network property for the attainment of precise node localization. In force of this definition, a mixed convex/non-convex optimization approach has been derived for the solution of the positioning problem. The two procedures, suitably combined, allow the achievement of better convergence towards the best positioning of a multitude of blind wireless nodes. A completely decentralized, partially asynchronous algorithm is presented, which proceeds locally on each node based on the sole knowledge of the distances measured from, and of the estimated positions of the connected nodes only. Its repeated asynchronous application on each nodes guarantees the convergence of the algorithm to the positioning of the whole network, even in presence of a limited number of peripheral reference points. Indeed, no global information is required for the proper functioning of the algorithm. Simulations of relevant case studies have been performed to qualify the proposed scheme in realistic conditions, and the results are presented.

As an important component of industrial robot, the motion control of rotary flexible joint (RFJ) system is of great significance, especially when the system has unmodeled dynamics or is seriously disturbed. This paper presents an experimental robustness study on a kind data-driven optimal control approach based on an underactuated rotary flexible joint system. The data-driven approach combines the off-policy optimal control algorithm and the popular integral reinforcement learning technique. Through literature review, we find that the key step of the control design lies in that it learns the optimal value function and control policy simultaneously from the input and output (I/O) data. However, the I/O data are often disturbed by the system uncertainty or environmental noise, and then it will indirectly affect the optimal control performance. To investigate the robustness of the data-driven optimal control approach, we artificially set different experimental scenarios and take numerous control experiments on a RFJ experimental setup. The experimental results show that the data-driven optimal control method is quite robust against the system uncertainties in terms of maintaining the stability and delivering satisfactory tracking performance, even when the uncertainty is not a small quantity. In addition, the disturbance originating from environmental noise has certain impact on the controlling of RFJ system, but as long as the noise power is not too large, the control algorithm can converge to a satisfactory result. Finally, we find that the probing signal up has strong influence to this control algorithm, which reminds us to be cautious when selecting the probing signal.

Non-contact optical measurement is a potential approach to on-orbit vibration measurement for flexible appendages, providing dynamic information for spacecraft control system. Binocular photogrammetry system is a practical configuration to achieve this measurement. In this paper, optimization approach and strategy for configuration parameters of this system are raised. Measurement matrix is specially defined to obtain the objective function for the optimization. Successive linear programming algorithm is used for optimization iteration. Transient responses of flexible appendages calculated by finite element model and corresponding images generated by OpenGL help to achieve this simulation-based optimization. The feasibility and effectiveness of the optimization are verified both by numerical study and experiment. Error analysis of the optimal system reveals great improvement in accuracy and robustness after optimization. This optimization is a promising approach to designing the configuration of binocular photogrammetry system and helping to achieve reliable on-orbit dynamic measurement results.

This paper proposes a computationally efficient method for pose-graph optimization that makes use of a multi-resolution representation of pose-graph transformation constructed on a spanning tree. It is shown that the proposed spanning tree-based hierarchy has a number of advantages over the previously known serial chain-based hierarchy in terms of preservation of sparsity and compatibility with parallel computation. It is demonstrated in numerical experiments using several public datasets that the proposed method outperforms a state-of-the-art solver for large-scale datasets.

A needle or a knife?

In this work we consider the regularization of vectorial data such as color images. Based on the observation that edge alignment across image channels is a desirable prior for multichannel image restoration, we propose a novel scheme of minimizing the rank of the image Jacobian and extend this idea to second derivatives in the framework of total generalized variation. We compare the proposed convex and nonconvex relaxations of the rank function based on the Schatten-q norm to previous color image regularizers and show in our numerical experiments that they have several desirable properties. In particular, the nonconvex relaxations lead to better preservation of discontinuities. The efficient minimization of energies involving nonconvex and nonsmooth regularizers is still an important open question. We extend a recently proposed primal-dual splitting approach for nonconvex optimization and show that it can be effectively used to minimize such energies. Furthermore, we propose a novel algorithm for efficiently evaluating the proximal mapping of the l q norm appearing during optimization. We experimentally verify convergence of the proposed optimization method and show that it performs comparably to sequential convex programming.

The performance of the simultaneous‐modular approach on four process optimization problems is studied, and numerical experiments using different computational strategies are also performed. The simultaneous‐modular approach is found to be very effective on process optimization problems, and the strategies used in our implementation SIMMOD are found to be more efficient than those used in previous implementations of the simultaneous‐modular approach for process optimization.

We introduce a new framework for the global optimization of computationally expensive multimodal functions when derivatives are unavailable. The proposed Stochastic Response Surface (SRS) Method iteratively utilizes a response surface model to approximate the expensive function and identifies a promising point for function evaluation from a set of randomly generated points, called candidate points. Assuming some mild technical conditions, SRS converges to the global minimum in a probabilistic sense. We also propose Metric SRS (MSRS), which is a special case of SRS where the function evaluation point in each iteration is chosen to be the best candidate point according to two criteria: the estimated function value obtained from the response surface model, and the minimum distance from previously evaluated points. We develop a global optimization version and a multistart local optimization version of MSRS. In the numerical experiments, we used a radial basis function (RBF) model for MSRS and the resulting algorithms, Global MSRBF and Multistart Local MSRBF, were compared to 6 alternative global optimization methods, including a multistart derivative-based local optimization method. Multiple trials of all algorithms were compared on 17 multimodal test problems and on a 12-dimensional groundwater bioremediation application involving partial differential equations. The results indicate that Multistart Local MSRBF is the best on most of the higher dimensional problems, including the groundwater problem. It is also at least as good as the other algorithms on most of the lower dimensional problems. Global MSRBF is competitive with the other alternatives on most of the lower dimensional test problems and also on the groundwater problem. These results suggest that MSRBF is a promising approach for the global optimization of expensive functions.

AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning