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Data-driven Algorithm for Scheduling with Total Tardiness

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arXiv 2020年

作者： Bouška, Michal Novák, Antonín Sůcha, Přemysl Módos, István Hanzálek, Zdeněk Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Prague Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Department of Control Engineering Karlovo náměstí 13 Prague Czech Republic

In this paper, we investigate the use of deep learning for solving a classical N P-hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling algorithms

On idle energy consumption minimization in production: Industrial example and mathematical model

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arXiv 2020年

作者： Benedikt, Ondřej Šůcha, Přemysl Hanzálek, Zdeněk Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Prague Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Department of Control Engineering Karlovo náměstí 13 Prague Czech Republic

This paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. The energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. The energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion

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arXiv 2024年

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio Robotics and Mechatronics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7500AE Netherlands Department of Applied Mathematics University of Twente Enschede7500AE Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. The latent representation is learned using a Cross-Modal Variational Autoencoder, and used to estimate the return for conditioning the trajectory generation of the DDPM. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach is applicable to any velocity-based controlled platform. We test our method with simulated and real-world experiments using generic multi-rotor Uncrewed Aerial Vehicles (UAVs). A video of our experiments can be found at https://***/yu-aTxqceOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

Application of Rna Genetic Algorithm Based on Gradient Descent in Function Optimization and engineering Optimization

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SSRN

SSRN 2023年

作者： Wu, Qiuxuan Chen, Mingming Zhao, Zikai Chi, Xiaoni Luo, Yanbin Zeng, Pingliang Zhang, Botao Wang, Jian Zhilenkov, Anton A. Chepinski, Sergey A. Institute of Electrical Engineering School of Automation Hangzhou Dianzi University Hangzhou China HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Hydrodynamics and Control Processes Saint-Petersburg State Marine Technical University Saint Petersburg Russia Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia

In engineering optimization problems, most are function optimization problems. RNA genetic algorithmhas apparent advantages in function optimization but has low accuracy and is easy to fall into local *** paper proposes an RNA genetic algorithm based on gradient descent. Gradient descent is used as amutation operator to improve the local development ability of the algorithm. Inspired by the inner-loopstructure of RNA molecules, we designed an inner-loop crossover operator and an inner-loop mutationoperator to improve the global exploration ability of the algorithm in the early stage of evolution and theability to jump out of the local extremum. The algorithm uses an RNA genetic algorithm to find individualsnear the optimal region in the pre-evolutionary stage and performs gradient descent variation on theseindividuals in the post-evolutionary stage to improve the quality of the solution. In addition, this algorithmuses the adaptive gradient descent method to alleviate the oscillation in the gradient descent process anduses the niche punishment method to deal with complex constraints. The algorithm is tested on 17benchmark functions and compared with other optimization algorithms, indicating that the algorithm hasbetter performance. Finally, Adam RNA-GA is used to solve four engineering design problems, and theresults obtained show that the algorithm has high solution accuracy. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

State Estimation and Fault Detection for Linear Switched Systems with Unstable Internal Dynamics *

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IFAC Proceedings Volumes 2012年第20期45卷 522-527页

作者： H. Ríos D. Efimov J. Davila T. Raïssi L. Fridman A. Zolghadri National Autonomous University of Mexico Department of Control Division of Electrical Engineering Engineering Faculty C.P. 04510 Mexico D.F The Non-A project at INRIA - LNE Parc Scientifique de la Haute Borne 40 avenue Halley Bt.A Park Plaza 59650 Villeneuve d'Ascq France National Polytechnic Institute Section of Graduate Studies and Research ESIME-UPT C.P. 07340 Mexico D.F University of Bordeaux IMS-Lab Automatic Control Group 351 cours de la libration 33405 Talence France Department of Automatic Control CINVESTAV-IPN Mexico D.F. AP-14-740 on leave on National Autonomous University of Mexico Engineering Department of Control and Robotics Division of Electrical Engineering Engineering Faculty C.P. 04510 Mexico D.F.

The problem of simultaneous estimation of the continuous and discrete state with additive faults in linear switched systems is addressed. Under mild structural conditions a robust estimator is designed to solve the problem. The proposed strategy includes a technique for fault detection. Simulation results support the proposed estimation scheme.

关键词： Switched systems Fault detection Unstable internal dynamics Sliding-Mode

State Estimation for Linear Switched Systems with Unknown Inputs *

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IFAC Proceedings Volumes 2012年第9期45卷 271-276页

作者： H. Ríos D. Efimov J. Davila T. Raïssi L. Fridman A. Zolghadri National Autonomous University of Mexico Department of Control Division of Electrical Engineering Engineering Faculty C.P. 04510 Mexico D.F The Non-A project at INRIA - LNE Parc Scientifique de la Haute Borne 40 avenue Halley Bt.A Park Plaza 59650 Villeneuve d'Ascq France Department of Automatic Control CINVESTAV-IPN Mexico D.F. AP-14-740 on leave on National Autonomous University of Mexico Engineering Department of Control and Robotics Division of Electrical Engineering Engineering Faculty C.P. 04510 Mexico D.F University of Bordeaux IMS-Lab Automatic Control Group 351 cours de la libration 33405 Talence France National Polytechnic Institute Section of Graduate Studies and Research ESIME-UPT C.P. 07340 Mexico D.F

The continuous and discrete state estimation problem in linear switched systems with unknown inputs and unstable internal dynamics is addressed. A robust observer based on High-Order Sliding-Mode is proposed to solve ... 详细信息

关键词： Switched systems State estimation Unknown inputs Sliding-Mode

Hardware-in-the-Loop Simulation of Vehicle-Manipulator Systems for Physical Interaction Tasks

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Hardware-in-the-Loop Simulation of Vehicle-Manipulator Syste...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hemjyoti Das Bjørn Kåre Sæbø Kristin Y. Pettersen Christian Ott Faculty of Electrical Engineering and Information Technology Automation and Control Institute (ACIN) Technical University of Vienna TU Wien Vienna Austria Department of Engineering Cybernetics Centre for Autonomous Marine Operations and Systems (NTNU AMOS) NTNU Norwegian University of Science and Technology Trondheim Norway German Aerospace Center Institute of Robotics and Mechatronics Wessling Germany

Hardware-in-the-loop simulation (HILS) allows a more realistic evaluation of control approaches than what is possible with pure software simulations, but without the actual complexity of the complete system. This is important for some complex systems such as orbital robots, where testing of the system is typically not possible after its launch, and an on-ground replica is used to validate the performance of such a system. In this article, an impedance-matching approach is presented to match the end-effector dynamics of a fixed-base robot manipulator with that of a target vehicle-manipulator system (VMS), while taking into account the redundant nullspace dynamics in a connected real-time simulation framework. This approach ensures that the forces and torques exerted by the system on the environment matches with that of the simulated system. The contact wrenches used in our approach are not obtained from numerical simulations, but rather from real physical interaction, which is one of the main advantages of our approach. The effectiveness of our method is validated by demonstrating various physical interaction tasks with the environment, using a suspended aerial manipulator as the target system.

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Roster evaluation based on classifiers for the nurse rostering problem

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arXiv 2018年

作者： Václavík, Roman Šůcha, Přemysl Hanzálek, Zdeněk Department of Control Engineering Faculty of Electrical Engineering Czech Technical University Karlovo náměstí 13 Prague 2121 35 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Zikova street 1903/4 Prague 6166 36 Czech Republic

The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100% correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Heuristic methods

Energy optimization of robotic cells

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arXiv 2018年

作者： Bukata, Libor Šůcha, Přemysl Hanzálek, Zdeněk Burget, Pavel Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Zikova 1903/4 Prague 6166 36 Czech Republic Faculty of Electrical Engineering Department of Control Engineering Czech Technical University in Prague Karlovo náměstí 13 Prague 212135 Czech Republic

This study focuses on the energy optimization of industrial robotic cells, which is essential for sustainable production in the long term. A holistic approach that considers a robotic cell as a whole toward minimizing energy consumption is proposed. The mathematical model, which takes into account various robot speeds, positions, power-saving modes, and alternative orders of operations, can be transformed into a mixed-integer linear programming formulation that is, however, suitable only for small instances. To optimize complex robotic cells, a hybrid heuristic accelerated by using multi-core processors and the Gurobi simplex method for piecewise linear convex functions is implemented. The experimental results showed that the heuristic solved 93 % of instances with a solution quality close to a proven lower bound. Moreover, compared with the existing works, which typically address problems with 3 to 4 robots, this study solved real-size problem instances with up to 12 robots and considered more optimization aspects. The proposed algorithms were also applied on an existing robotic cell in Škoda Auto. The outcomes, based on simulations and measurements, indicate that, compared with the previous state (at maximal robot speeds and without deeper power-saving modes), the energy consumption can be reduced by about 20 % merely by optimizing the robot speeds and applying power-saving modes. All the software and generated data sets used in this research are publicly available.90C10, 90C25 Copyright © 2018, The Authors. All rights reserved.

关键词： Industry 4.0