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

IPS-RCRA 2020 - Joint Proceedings of the 8th Italian Worksho...

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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

the proceedings contain 7 papers. the topics discussed include: local search for AI planning;in defense of design patterns for AI planning knowledge models;towards plan recognition in hybrid systems;an asp based solution for operating room scheduling with surgical teams in hospital environments;model-based automated flight path planning for an ultralight aircraft;improving the efficiency of euclidean tsp solving in constraint programming by predicting effective nocrossing constraints;and answer set programming in healthcare: extended overview.

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AI4CC-IPS-RCRA-SPIRIT 2024 - Proceedings of the International workshop on Artificial Intelligence for Climate Change, Italian workshop on planning and scheduling, RCRA workshop on Experimental Evaluation of algorithms for Solving problems with Combinatorial Explosion, and SPIRIT workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, co-located with 23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

AI4CC-IPS-RCRA-SPIRIT 2024 - Proceedings of the Internationa...

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1st International workshop on Artificial Intelligence for Climate Change, 12th Italian workshop on planning and scheduling, 31st RCRA workshop on Experimental Evaluation of algorithms for Solving problems with Combinatorial Explosion, and SPIRIT workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024

the proceedings contain 24 papers. the topics discussed include: a novel methodology for ai-based sorting of post-consumer textile using spectrophotometer;what's behind this water table depth forecasting? RISE application for spatial, temporal, and spatio-temporal explanations;deep learning for land cover segmentation in agricultural regions using aerial datasets;assessing the impact of climate change on mineral-associated organic carbon (MAOC) using machine learning models;urban heat island. machine learning models for analysis and maker approach for mitigation;one to rule them all: natural language to bind communication, perception and action;automated PDDL domain file generation for enhancing production system development based on SysML models;a bin-packing formulation for radiotherapy treatment scheduling;and many-valued temporal description logics with typicality: an abridged report.

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IPS 2023, RCRA 2023, and SPIRIT 2023 workshops - Proceedings of the Italian workshop on planning and scheduling, RCRA workshop on Experimental Evaluation of algorithms for Solving problems with Combinatorial Explosion, and SPIRIT workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, co-located with 22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2023

IPS 2023, RCRA 2023, and SPIRIT 2023 Workshops - Proceedings...

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11th Italian workshop on planning and scheduling, 30th RCRA workshop on Experimental Evaluation of algorithms for Solving problems with Combinatorial Explosion, and 2023 SPIRIT workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, IPS-RCRA-SPIRIT 2023 workshops

the proceedings contain 21 papers. the topics discussed include: planning safe collaborative behaviors through risk-aware heuristic search;tabular model learning in Monte Carlo tree search;goal recognition with deep learning and embedded representation of state traces;investigating domain-oriented approaches to optimization in timeline-based planning;heuristic planning for hybrid dynamical systems with constraint logic programming;characterizing nexus of similarity between entities;a simple proof-theoretic characterization of stable models;experimenting an approach to neuro-symbolic RL;planning as theorem proving with heuristics;learning augmented online learning algorithms - the adversarial bandit with knapsacks framework;and optimal rates for online Bayesian persuasion.

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Big Data Analysis and Intelligent Traffic Management for Airport Transportation 24

Big Data Analysis and Intelligent Traffic Management for Air...

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6th International Conference on Big Data Engineering (BDE)

作者： Zhang, Huiru Zhang, Kun China Airport Planning & Design Inst Co Ltd Beijing Peoples R China

ISBN: (纸本)9798400717857

Airports are vital transportation hubs in cities, and the efficiency of their transportation operations directly impacts the overall performance of urban transportation networks. Hocwever, with the rapid growth in demand for air transport, congestion problems in airports and surrounding road traffic are becoming increasingly prominent. Based on the concept of big data analysis and intelligent control, this paper proposes a comprehensive solution. Firstly, the system lays the foundation for subsequent analysis by constructing a framework for the collection, storage, and management of massive heterogeneous traffic data. Building upon this foundation, core algorithm models such as traffic flow prediction, anomaly detection, and route optimization are developed using traffic flow theory, data mining, artificial intelligence, and other methods. these models are applied to key operations such as traffic signal scheduling, traffic guidance, and parking management, forming an integrated intelligent traffic management system that combines monitoring, prediction, scheduling, and control. through practical application in real-world scenarios, the system effectively alleviates airport traffic congestion, improves road traffic efficiency, and provides passengers with more convenient and comfortable services, demonstrating the broad prospects of big data analysis and intelligent control technology in the modern transportation field.

关键词： Airport Transportation Big Data Analysis Intelligent Traffic Management Traffic Flow Prediction

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Development of a modified genetic method for automatic university scheduling 6

Development of a modified genetic method for automatic unive...

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6th workshop for Young Scientists in Computer Science and Software Engineering, CS and SE@SW 2023

作者： Fedorchenko, Ievgen Oliinyk, Andrii Zaiko, Tetiana Miedviediev, Kyrylo Fedorchenko, Yuliia Khokhlov, Mykola National University "Zaporizhzhia Polytechnic" 64 Zhukovsky Str. Zaporizhzhia69063 Ukraine

the paper investigates the problem of optimizing the university class schedule. Sequential and parallel methods for scheduling based on genetic search are developed. the proposed methods use adapted initialization, crossover, and selection operators. the algorithms minimize conflicts and the time interval between classes, taking into account recommendations for time and place. the developed methods contribute to effective planning of the educational process and avoidance of errors in scheduling. A comparative analysis of the classical and modified genetic algorithms is carried out, confirming the faster and more efficient functioning of the modified approach. the modified algorithm is also compared with different operators and parameters of the genetic algorithm to determine the optimal conditions. the obtained results indicate effective methods for improving the quality of the schedule and optimizing the educational process at the university. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Genetic algorithms

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CS and SE@SW 2023 - Proceedings of the 6th workshop for Young Scientists in Computer Science and Software Engineering

CS and SE@SW 2023 - Proceedings of the 6th Workshop for Youn...

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6th workshop for Young Scientists in Computer Science and Software Engineering, CS and SE@SW 2023

the proceedings contain 18 papers. the topics discuss include: an approach to assessing the reliability of software systems based on a graph model of method dependence;methodology of implementation of modern information systems at commercial enterprises;information system module for analysis viral infections data based on machine learning;designing a cross-platform user-friendly transport company application;research of the route planning algorithms on the example of a drone delivery system software development;implementing E2E tests with Cypress and page object model: evolution of approaches;design and development of a game application for learning Python;application of Daubechies wavelet analysis in problems of acoustic detection of UAVs;and data processing method for multimodal distribution parameters estimation.

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Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time

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OPERATIONAL RESEARCH 2022年第5期22卷 5055-5105页

作者： Hosseinzadeh, Mohammad Reza Heydari, Mehdi Mazdeh, Mohammad Mahdavi Iran Univ Sci & Technol Dept Ind Engn 5th FloorChamran Bldg Tehran Iran

the advancement of technology enables manufacturing companies to employ multifunction machines to increase the flexibility of a system in producing miscellaneous products in a short time. In this situation, goods can be usually produced through different process plans, and considering process planning and scheduling in an integrated framework would be essential. Furthermore, group processing is regarded to overcome the difficulty of long setup times and consequently increase the productivity of a manufacturing system. this paper deals with the integrated process planning and group scheduling problem with sequence-dependent setup time between each group of jobs. Two mixed-integer linear programming models with different approaches are presented. Moreover, two metaheuristic algorithms are proposed to solve the problems heuristically. the experiments show the high performance of the combination-based mathematical model for small-size problems as well as the proposed metaheuristic algorithms for medium-size and large-size instances.

关键词： Process planning Group scheduling Sequence-dependent setup time Mathematical modeling Genetic algorithm Water cycle algorithm

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Multi Robot Path planning based on Reinforcement Learning 6

Multi Robot Path Planning based on Reinforcement Learning

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

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

ISBN: (纸本)9798350316520

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. © 2024 IEEE.

关键词： Reinforcement learning

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planning Safe Collaborative Behaviors through Risk-Aware Heuristic Search 11

Planning Safe Collaborative Behaviors through Risk-Aware Heu...

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作者： Bonini, Alex Mayer, Marta Cialdea Cesta, Amedeo Orlandini, Andrea Umbrico, Alessandro ROMA TRE University Italy Italy

Deploying collaborative robots in manufacturing presents diverse challenges. Rapid adaptability to the environment while ensuring user safety and engagement is paramount. Existing human-aware task sequencing solutions often lack explicit risk modeling and management. International standards emphasize severity, exposure, and avoidance as critical risk factors. To enhance intelligent risk awareness control, we propose integrating multiple risk factors into task sequencing models. this forms the basis for a cutting-edge planning framework-backed risk-aware task sequencing system. Our approach's evaluation across various scenarios showcases its efficacy and adaptability to diverse risk levels. Experimental results show a positive equilibrium between productivity and safety, achieving both high throughput and low operator risk. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Collaborative robots

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Exploring the Application of Swarm Intelligence algorithms to Nonlinear and Non-Convex problems in the Real-World

Exploring the Application of Swarm Intelligence Algorithms t...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Wei Shen Shanghai Open University Jingan Branch Shanghai Jingan District College Shanghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In the real world, when solving problems based on advanced artificial intelligence (AI) technology, the problem is usually first transformed into an optimization model and corresponding algorithms are designed to solve it. Usually, such problems belong to non-convex nonlinear problems with numerous parameters, which are usually solved using swarm intelligence algorithms. therefore, in this study, we summarized the three most common non convex nonlinear problems in the real world, including drone path planning, desert crossing problem, and workshop scheduling problem. We have summarized the three problems encountered and established corresponding mathematical models. the problem of drone path planning is based on highway or infrastructure detection tasks. Crossing the Desert is a real-life game. the workshop scheduling problem is based on the background of automobile factories. On this basis, we developed an improved dragonfly algorithm (DA) and applied the improved DA algorithm to three mathematical models.

关键词： Road transportation Conferences scheduling Path planning Deserts Resource management Particle swarm optimization Artificial intelligence Optimization Drones

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