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Maximizing the number of jobs scheduled at their baseline starting times in case of machine failures

JOURNAL OF scheduling 2020年第1期23卷 135-143页

作者： Chretienne, Philippe Univ Paris 06 Sorbonne Univ UMR 7606 CNRSLIP6 4 Pl Jussieu F-75005 Paris France

We investigate the problem of keeping the maximum number of starting times of a baseline schedule if some machines happen to be out of order when the baseline schedule is to be implemented. If the machines are identical, we show that the problem is polynomially solved when no deadline is imposed on the reactive schedule and is strongly NP-hard otherwise. If the number of unrelated machines is fixed and if no deadline is imposed on the reactive schedule, a polynomial algorithm, based on a state graph, has been developed. We conclude with an open complexity question and some further research directions for this class of problems.

关键词： scheduling Complexity algorithms State graph

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Variable Neighborhood Search for scheduling of Cold Rolling Annealing Process of Silicon Steel*

Variable Neighborhood Search for Scheduling of Cold Rolling ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Ying Huang Shixin Liu Informatization Business Department Shanghai Baosight Software Limited Company Shanghai China College of Information Science & Engineering Northeastern University Shenyang China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Aiming at the production scheduling problem of cold rolling annealing process of silicon steel, a multi-objective optimization model of production scheduling is established, which considered various process constraints such as steel grade switch, width jump, annealing temperature jump, etc. the multi-objective functions are transformed into a single objective function by weighted sum method. Based on the characteristics of the problem, four kinds of neighborhood structures for local search are proposed. And a variable neighborhood search algorithm is proposed to solve the problem. the correctness of the model and the effectiveness of the algorithm are verified by practical production instances. the experimental results show that the approximate Pareto solution set with good distribution can be obtained by using the weighted sum method and solved by the variable neighborhood search algorithm proposed in this paper.

关键词： Annealing Job shop scheduling Optimization models Production Approximation algorithms Search problems Linear programming Silicon Steel Optimization

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A PSO-Assisted Reinforcement Learning Algorithm for Job Shop scheduling

A PSO-Assisted Reinforcement Learning Algorithm for Job Shop...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Peng Yue Yaochu Jin Qi Shi Xuewu Dai Dongliang Cui State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Engineering Westlake University Hangzhou China Northumbria University Newcastle upon Tyne U.K

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

the Job Shop scheduling Problem (JSSP) is a critical and complex optimization challenge in manufacturing. Recently, Reinforcement Learning (RL)-based methods have garnered significant attention due to their promising performance in improving scheduling efficiency. However, traditional RL algorithms often suffer from inefficiencies in exploring the vast solution space, resulting in suboptimal policies. this paper introduces a novel Reinforcement Learning algorithm assisted by particle swarm optimization (PSO), called PSO-RL, to address these limitations. We design a multiple-particle searching framework for the RL algorithm, where multiple solutions can be improved synchronously by an RL model. During the training process, PSO is periodically applied to guide the worst solutions towards the global and local optima identified so far. By integrating PSO's global search capability with RL's adaptive learning, our PSO-RL algorithm achieves a balanced exploration-exploitation tradeoff. Experimental results on benchmark JSSP instances demonstrate that PSO-RL outperforms state-of-the-art methods, yielding lower makespans. the adaptability of the PSO-RL framework makes it a versatile tool for various industrial applications, enhancing the performance of production planning and intelligent manufacturing systems.

关键词： Training Job shop scheduling Processor scheduling Heuristic algorithms Production planning Reinforcement learning Benchmark testing Search problems Robustness Optimization

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Multi Robot Path planning based on Reinforcement Learning

Multi Robot Path Planning based on Reinforcement Learning

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Xi Liu Zhonghua Wang Shun Liu School of Electrical Engineering University of Jinan China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

In order to improve the success rate of path finding in the MAPF(Multi Agent Path planning) system and solving quality of large scale MAPF problems, this paper designs a multi robot deep reinforcement learning algorithm based on the ISAC(Independent Soft Actor Critic) mechanism to solve the multi robot path planning problem. In order to overcome the problem of difficult training of multi robot models, a deep neural network with a communication layer was designed based on the parameter sharing ISAC algorithm. ISAC was combined with IL(imitation learning) to construct the ISAC-IL algorithm. the simulation results show that compared with precise algorithms and heuristic algorithms, the ISAC-IL algorithm has a higher success rate, shorter completion time, and can calculate higher quality optimization solutions.

关键词： Training Heuristic algorithms Scalability Simulation Artificial neural networks Deep reinforcement learning Path planning

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Design and Optimization of Power System Day-Ahead scheduling Based on Kantorovich Algorithm

Design and Optimization of Power System Day-Ahead Scheduling...

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Energy Systems and Electrical Power (ICESEP), International Conference on

作者： Xin Ai China Aviation Planning and Design Institute (Group) CO. LTD. Beijing China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

In this study, we enhance the k-means clustering algorithm for the initial classification of raw scenes. this process is complemented by a synchronized back generation reduction algorithm, which leverages Kantorovich probability distance to streamline scene processing, thereby reducing the computational demands of scene analysis. We have developed a power system day-ahead optimal dispatch model including wind power, photovoltaic (PV), thermal power, and pumped storage, which is based on refined scenario analyses and investigates the role of pumped storage units in improving the rate of wind and solar energy consumption. the effectiveness of our proposed model and methodology is demonstrated through simulation analysis using real data from a provincial grid.

关键词： Analytical models Image analysis Wind energy Computational modeling Clustering algorithms Wind power generation scheduling

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Approximation algorithms for the twin robot scheduling problem

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JOURNAL OF scheduling 2020年第1期23卷 117-133页

作者： Jaehn, Florian Wiehl, Andreas Helmut Schmidt Univ Management Sci & Operat Res Holstenhofweg 85 D-22043 Hamburg Germany Stettenstr 28 D-86150 Augsburg Germany

We consider the NP-hard twin robot scheduling problem, which was introduced by Erdogan et al. (Naval Res Logist (NRL) 61(2):119-130, 2014). Here, two moving robots positioned at the opposite ends of a rail have to perform automated storage and retrieval jobs at given positions along the gantry rail with a non-crossing constraint. the objective is to minimize the makespan. We extend the original problem by considering pickup and delivery times and present exact and approximation algorithms with a performance ratio of approximate to 1.1716 for large instances. Further, we compare the presented algorithms in a comprehensive numerical study.

关键词： Automated storage and retrieval systems Non-crossing constraints Approximation algorithms Crane scheduling

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Harris Hawks Optimization for Solving Flexible Jobshop scheduling Problem Considering Energy Consumption 6

Harris Hawks Optimization for Solving Flexible Jobshop Sched...

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6th International Conference on Robotics and Automation Engineering, ICRAE 2021

作者： Wu, Mingliang Yang, Dongsheng Liu, Tianyi Northeastern University Intelligent Electrical Science and Technology Research Institute Shenyang China

ISBN: (纸本)9781665406970

Industrial scheduling problem has always been one of the key problems in manufacturing and management planning enterprises. the previous studies mainly concentrate on the optimization of maximum completion time. With the increasing shortage of non-renewable resources and the increasing demand for industrial energy, the energy crisis increasingly restricts production and living needs. Consider the above issues, this paper designs a FJSP with consideration of energy consumption (EFJSP). Meanwhile, an advanced swarm intelligence optimization algorithm: Harris Hawks Optimization (HHO), is introduced to solve the EFJSP. In the experiment, we formulated 4 FJSP instances to examine the performance of the HHO. the results obtained by the HHO compared with the other five classic metaheuristic algorithms. the comparison result displays that our algorithm far exceeds other algorithms in solving EFJSP. © 2021 IEEE.

关键词： Energy utilization

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Modelling and Dynamic Simulation of Palletizing Robot System Based on Multibody

Modelling and Dynamic Simulation of Palletizing Robot System...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zhang Zhuo Zhang Cheng Deng Zhenjie Feng Haijie College of Robotics Guangdong Polytechnic of Science and Technology School of Electronic and Information Engineering Zhuhai College of Science and Technology

In order to solve the problems of lack of port for motion planning and motion control, difficulty of changing kinetic parameters depending on the robot structure, and lack of visualization ability of kinetic simulation in the conventional modeling and simulation of palletizing robots, a hybrid 4-axis multi-joint palletizing robot system modeling method is designed and simulated. the kinematic analysis of the palletizing robot is carried out, the MBS model of the robot is created using Simscape, the motion trajectory is planned, and the dynamics simulation is carried out to verify the correctness of the system model. the experimental results show that the system model has better adaptability and functionality than conventional modeling and simulation methods, and can provide a complete R&D platform for the design of similar robots and the study of control algorithms, which is of good scientific significance.

关键词： Adaptation models Analytical models Visualization Heuristic algorithms Dynamics planning Kinetic theory

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Two-level data middle platform flow-batch integrated transmission strategy based on dynamic priority

Two-level data middle platform flow-batch integrated transmi...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Jiangbo Yin Longgang He Beijing China-Power Information Technology Co. LTD Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

State Grid two-level data center is a data management and service platform for smart grid, which can realize the functions of data collection, storage, processing, analysis and application. However, its data link transmission faces the challenges of high transmission delay, poor quality of service and difficult to transmit batch data cooperatively on the same data link. In order to solve these problems, this paper proposes an integrated flow and batch transmission configuration model. According to the characteristics and requirements of the data, the model comprehensively considers the factors such as data priority, real-time, urgency and data size, as well as the load and bandwidth requirements of the link, and realizes the data classification and priority division, weighted hybrid scheduling and link allocation transmission control. In order to verify the validity of the model, NS3 is used as the simulation platform, and simulation experiments are carried out in a network topology including headquarters data center, provincial data center and nodes of each business department. the simulation results show that under the same network conditions, compared with the traditional FIFO queue scheduling algorithm and WFQ queue scheduling algorithm, the model can increase the data link transmission efficiency by 20%, reduce the total delay by 21%, ensure the quality of service, and realize the coordinated transmission of stream and batch data.

关键词： Data centers Job shop scheduling scheduling algorithms Heuristic algorithms Quality of service Dynamic scheduling Data models Delays Smart grids Load modeling

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Production scheduling Optimization enabled by Digital Cognitive Platform

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Procedia Computer Science 2022年 204卷 424-431页

作者： Konstantinos Georgiadis Alexandros Nizamis thanasis Vafeiadis Dimosthenis Ioannidis Dimitrios Tzovaras Information Technologies Institute Center for Research and Technology Hellas 6th km Charilaou-Thermi Road Thessaloniki 57001 Greece

Automation and optimization are two key concepts for smart factories in industry 4.0. the production planning and scheduling optimization models are widely used to support the aforementioned key concepts. Due to the wide availability of various Industrial Internet of things (IIoT) devices and predictive models, the factories of the future would be self-learned and capable to act in monetary situations. To achieve this, the planning optimization, dynamic rescheduling and production line balancing solutions should be available to todays’ production lines. In this paper, we are introducing a real-world example of a solution for production scheduling optimization and production line balancing based on genetic algorithm. the introduced application was developed over an established Cognitive Analytics Platform for Anomaly Detection. the application uses deployment, security and visualization services available by the platform. therefore, this work presents alongside the optimization solution, the way the platform can be modified for serving other use cases besides the anomaly detection, in order to provide a complete tool for factory automation and optimization.

关键词： Industry 4.0 scheduling optimization data platform genetic algorithms system integration real-world application

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