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International workshop on Advanced Computational Intelligence (IWACI)

作者： Ke-Jing Du Jia-Quan Yang Limin Wang Xuming Han Hua Wang Zhi-Hui Zhan Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC Australia School of Computer Science and Engineering South China University of Technology Guangzhou China School of Information Science Guangdong University of Finance & Economics Guangzhou China College of Information Science and Technology Jinan University Guangzhou College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

the demand responsive transit (DRT) is a type of bus that does not have a fixed route and provides flexible travel services within a certain area range based on passengers’ needs. Due to its service flexibility and cost economy, DRT has become an important component of smart city construction. Essentially, DRT is an NP-hard combinational optimization problem, and traditional deterministic methods are not suitable for solving practical DRT problems. At the same time, most existing studies concern minimizing operational costs as the main optimization objective, with insufficient consideration of passenger demand satisfaction. this article treats minimizing operational costs and minimizing penalty costs as two optimization objectives, so as to model the DRT as a multi-objective optimization problem. To solve the multi-objective DRT scheduling problem efficiently, this article follows the novel and efficient multiple populations for multiple objectives (MPMO) framework and proposes a multiple populations ant colony system (MPACS) approach. Two ant colony populations are used to optimize the two objectives (i.e., minimize operational costs and minimize penalty costs), and an information share mechanism is designed to achieve co-evolution between the two populations, thereby finding Pareto solutions for the multi-objective DRT problem. the proposed MPACS has been tested on five datasets and compared with the first-come-first-served greedy algorithm and the nearest neighbor greedy algorithm, verifying its effectiveness and efficiency in solving the multi-objective DRT problems.

关键词： Greedy algorithms Costs Smart cities Computational modeling Sociology Mathematical models planning

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Metaheuristics for solving a multi-objective flow shop scheduling problem with sequence-dependent setup times

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JOURNAL OF scheduling 2020年第1期23卷 49-69页

作者： Anjana, V. Sridharan, R. Kumar, P. N. Natl Inst Technol Calicut Dept Mech Engn Calicut 673601 Kerala India Indian Inst Management Kozhikode Quantitat Methods & Operat Management Calicut 673570 Kerala India

Industries such as textiles, paints, chemicals, paper, drugs and pharmaceuticals operate as flow shops with sequence-dependent setup times (SDST). the sequence-dependent setup environment is characterised by the dependence of the setup time on the current job and also on the previous job processed on that machine. To further complicate the problem, in most real-life scenarios, decision-makers have to optimise more than one performance measure while scheduling jobs on machines. this work considers such a multi-objective SDST flow shop environment. the objectives considered in the present study are minimisation of makespan and minimisation of mean tardiness. Four metaheuristics, viz. non-dominated sorting genetic algorithm (NSGA) II, hybrid NSGA II, discrete particle swarm optimisation and hybrid discrete particle swarm optimisation, belonging to the category of intelligent optimisation techniques, are developed to obtain a set of Pareto-optimal solutions. the proposed metaheuristics are applied on benchmark SDST flow shop problems and their performance compared using different measures. Analysis of the results reveals that hybrid NSGA II outperforms the other three algorithms for all problem sizes considered in the present research. the results also indicate that hybridisation of the metaheuristics with variable neighbourhood search improves their performance.

关键词： Permutation flow shop Sequence-dependent setup time NSGA II Discrete particle swarm optimisation Variable neighbourhood search

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Intelligent Route planning Algorithm Considering Wind and Wave Risks

Intelligent Route Planning Algorithm Considering Wind and Wa...

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

作者： Aohui Du Hengshuai Shang Fangzheng Wang Bo Ai Benshuai Li Guannan Lv Wenjun Feng College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China Qingdao Yuehai Information Service Co Ltd Qingdao China Tianjin Key Laboratory of Surveying and Mapping for Water Transport Engineering Tianjin Research Institute for Water Transport Engineering M.O.T. Tianjin Survey and Design Institute for Water Transport Engineering Tianjin China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Ocean shipping has become an indispensable mode of transportation in global trade due to its significant advantages such as large carrying capacity, low freight rates, and natural waterways. However, shipping safety is significantly affected by the marine meteorological environment. To address this challenge, this paper proposes an improved A * algorithm for meteorological risk planning of ship routes. this method comprehensively considers the impact of extreme weather conditions such as wind and waves on route safety, and constructs a dynamic navigation environment model using ocean forecast data. Firstly, a dynamic streamlined search strategy based on node environment information was designed to improve the efficiency of path search; Secondly, a sea state navigation risk model was established, and spherical distance was introduced as a range evaluation index to more accurately assess navigation risks; In addition, this article achieves real-time updates of forecast data by recording the accumulated flight time of nodes, meeting the planning requirements in dynamic environments. Finally, by performing secondary optimization on the navigation path, the safety and efficiency of path planning were further improved. the experimental results show that the improved algorithm can ensure the rationality of the route while effectively avoiding rough seas, reducing the risks caused by adverse weather, and ensuring the safety and feasibility of navigation.

关键词： Navigation Heuristic algorithms Oceans Aerodynamics Search problems Data models planning Safety Wind forecasting Marine vehicles

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An Enhanced Harris Hawks Optimization Algorithm for GRU Hyperparameter Tuning: Application to Mooring System Tension Prediction

An Enhanced Harris Hawks Optimization Algorithm for GRU Hype...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yang An Changsheng Zhang Jintao Shao Software College Northeastern University Shenyang China Lightspeed Studio Tencent Co. Shenzhen China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

the accuracy of tension prediction for mooring systems affects the safety and efficiency of production planning for offshore platforms as well as ship systems. With the development of deep learning, tension prediction based on neural networks has been widely used and has shown excellent performance. However, the performance of neural networks depends largely on the setting of hyper-parameters, and frequent manual adjustments of hyper-parameters are required in use to obtain the best neural network performance results, which is undoubtedly time-consuming and affects the performance of the task at the same time. To address this problem, this paper proposes a Harris-Hawkes optimization algorithm (ADLS-HHO) based on an adaptive two-layer search strategy and an improved Levy flight mechanism to automatically optimize the GRU hyperparameters. Specifically, ADLS-HHO employs an adaptive two-layer search framework to enhance the dynamic balance between exploitation and exploration, and Levy flights to enhance the global exploration capability while guiding the local search strategy through a dynamically maintained memory pool. Experimental results show that the prediction accuracy of the proposed ADLS-HHO-GRU model is better than that of the conventional model, and in addition, the number of convergence iterations required by ADLS-HHO-GRU is less than that of the existing classical algorithms. these results indicate that ADLS-HHO-GRU provides an effective method while maintaining computational efficiency.

关键词： Deep learning Accuracy Computational modeling Heuristic algorithms Neural networks Predictive models Search problems Prediction algorithms Optimization Tuning

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In defence of design patterns for AI planning knowledge models? 8

In defence of design patterns for AI planning knowledge mode...

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Joint 8th Italian workshop on planning and scheduling and the 27th International workshop on Experimental Evaluation of algorithms for Solving problems with Combinatorial Explosion, IPS-RCRA 2020

作者： Vallati, Mauro McCluskey, Lee University of Huddersfield Huddersfield United Kingdom

Design patterns are widely used in various areas of computer science, the most notable example being software engineering. they have been introduced also for supporting the encoding of automated planning knowledge models, but with little success. In this paper, we argue about the merits of design patterns, as an example of the broader class of reusable abstractions, in the automated planning context;particularly, we aim at drawing attention to their potential usefulness for the explainability of domain-independent planning systems. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Software engineering

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A Job scheduling Algorithm for Edge Computing Based on Modified Monte Carlo Tree Search

A Job Scheduling Algorithm for Edge Computing Based on Modif...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Haobin Wang Chen Li Tianyuan Wang Fenfen Huang Network Planning Research and Innovation Center China Telecom Research Institute Beijing China

ISBN: (纸本)9781665447300

Job scheduling is a crucial and promising task for maximizing computing resources utilization, which significantly improves the efficiency of edge cloud. Nevertheless, the existing job scheduling algorithms mainly ignore the scarce computing resources, i.e., heterogeneous resources, in edge cloud. A novel job scheduling algorithm based on Modified Monte Carlo Tree Search is proposed to take full advantage of the computing resources. the experiment results manifest that the proposed algorithm is valid in job scheduling for edge computing.

关键词： Monte Carlo methods scheduling algorithms Computational modeling Signal processing algorithms Signal processing Search problems scheduling

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A Remaining Useful Life Model for Optimizing Maintenance cost and Spare-parts replacement of Production Systems in the Context of Sustainability 10th

A Remaining Useful Life Model for Optimizing Maintenance cos...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Rebaiaia, Mohamed-Larbi Ait-Kadi, Daoud Univ Laval Engn Mech Dept Pavillon Adrien Pouliot1065Av MedLocal 3515 Quebec City PQ G1V 0A6 Canada

In reliability and maintenance engineering, predictive methods play a major role for estimating the remaining useful life (RUL) of equipment. However, in the most cases the RUL estimation is based on two types of approaches: model-based solution and data-driven solution. Data-driven solution is a very realistic solution, but requires the availability of large quantity of collected data using sensors networks, big storage capacity, supercomputers for processing, and high-level algorithms such as machines learning, convolutional Neural Networks, Hidden Markov models. While model-based solution methods are less difficult to deploy, they use techniques based on the mean residual life (MRL) value. this paper proposes an integrated decision support model for minimizing the maintenance expected cost, and for reducing the excess of spare-parts usage of multi-component production systems. this decision support model includes three performance indicators that are: the renewal function (RF), the mean residual lifetime (MRL) and the renewal mean residual lifetime (RMLR). the contribution of this research consists of (1) introducing the MRL function for a class of useful probability distributions, (2) proposing the Renewal MRL (RMRL) as a predictive maintenance strategy, (3) applying the approach-MRL to maintenance scheduling problems, (4) dressing a comparative numerical study between the different proposed models using an industrial case study. Besides, the comparative study performing the RF, MRL, and RMRL is carried out using basic and modified replacement strategies consisting of an age replacement policy (ARP) and an opportunistic maintenance solution. In addition, a case study from an Electrical power system is proposed by providing numerical results that discuss and illustrate the outcome gains in terms of maintenance costs saving and spare parts replacements' minimization. However, the proposed solution is demonstrated to have a positive effect to enhance sustainable de

关键词： Maintenance planning Optimization Mean residual life Renewal theory Sustainability

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

Harris Hawks Optimization for Solving Flexible Jobshop Sched...

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International Conference on Robotics and Automation Engineering (ICRAE)

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

ISBN: (纸本)9781665406987

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.

关键词： Energy consumption Job shop scheduling Automation Metaheuristics Production planning Manufacturing

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ROS-Tiplex: A collaborative design tool for timeline-based planning & scheduling applications with ROS 6

ROS-Tiplex: A collaborative design tool for timeline-based p...

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6th Italian workshop on Artificial Intelligence and Robotics, AIRO 2019

作者： la Viola, Carlo Orlandini, Andrea Umbrico, Alessandro Cesta, Amedeo Institute of Cognitive Science and Technology National Research Council of Italy Via San Martino della battaglia 44 Rome00185 Italy

this paper provides a quick overview of a Knowledge Engineering system, called ROS-TiPlEx (Timeline-based planning and Execution with ROS), to provide a shared environment in which experts in robotics and planning can easily interact to, respectively, encode information about low-level robot control and define task planning and execution models. ROS-TiPlEx aims at facilitating the interaction between both kind of experts, thus, enhancing and possibly speeding up the process of an integrated control design. ROS-TiPlEx is the first tool addressing the connection of ROS and timeline-based planning. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Robot programming

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Research on Heterogeneous UAVs Task Assignment Based on Improved Contract Net Algorithm

Research on Heterogeneous UAVs Task Assignment Based on Impr...

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International Conference on Robotics and Automation Engineering (ICRAE)

作者： Shaokun Yan Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9781665406987

the traditional contract network algorithms has problems such as unbalanced load, low distribution efficiency, and lack of real-time dynamic planning ability when solving the task assignment of UAVs. In response to the above problems, this paper proposes a heterogeneous UAV cooperative task allocation model based on the improved contract network algorithm. Combined with the actual application scenarios of UAV task allocation, this paper carries out laboratory simulation verification. the simulation results show that, compared with the traditional contract network algorithm, the improved contract network algorithm proposed in this paper can effectively improve the efficiency of task allocation, improve the balance of task allocation, and has dynamic task planning capabilities, which proves the effectiveness of the algorithm.

关键词： Heuristic algorithms Simulation Dynamic scheduling Real-time systems planning Delays Resource management

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