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A variable window multi-interval rescheduling optimization algorithm for dynamic flexible job shop problem

APPLIED SOFT computing 2025年 176卷

作者： Guo, Zeyin Wei, Lixin Li, Xin Yang, Shengxiang Zhang, Jinlu Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China De Montfort Univ Ctr Computat Intelligence CCI Sch Comp Sci & Informat Leicester LE1 9BH England

The dynamic flexible workshop scheduling problem (DFJSP) requires the generation of new scheduling plans after being subjected to dynamic disturbances. Due to the reconfigurability of chromosomal gene, scheduling schemes have a large search space, which poses challenges for solving scheduling schemes. Therefore, a variable window multi-interval optimization (VWMI) rescheduling algorithm is proposed to solve the DFJSP. A nonlinear adaptive crossover probability and mutation probability function is proposed to address the issue of combinatorial optimization easily getting stuck in local optima. Based on the mapping relationship between individual space and objective space, a spatial joint selection method is proposed to select diverse individuals. Compared with other algorithms in dynamic workshop test cases, the rescheduling strategy achieved 7 optimal performance values in 15 test cases, with a maximum time efficiency improvement of 30.2%. In addition, the VWMI achieved 11 good performances in test cases, outperforming other optimization methods.

关键词： Dynamic flexible job shop scheduling Multi-objective optimization Window multi interval scheduling Adaptive cross mutation probability Neighborhood structure search

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The search Futures workshop 46th

The Search Futures Workshop

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46th European Conference on Information Retrieval (ECIR)

作者： Azzopardi, Leif Clarke, Charles L. A. Kantor, Paul B. Mitra, Bhaskar Trippas, Johanne R. Ren, Zhaochun Univ Strathclyde Glasgow Lanark Scotland Univ Waterloo Waterloo ON Canada Rutgers State Univ New Brunswick NJ USA Microsoft Res Montreal PQ Canada RMIT Univ Melbourne Vic Australia Leiden Univ Leiden Netherlands

ISBN: (纸本)9783031560682;9783031560699

The field and community of Information Retrieval (IR) are changing and evolving in response to the latest developments and advances in Artificial Intelligence (AI) and research culture. As the field and community re-oriented and re-consider its positioning within computing and information sciences more generally - it is timely to gather and discuss more seriously our field's vision for the future - the challenges and threats that the community and field faces - along with the bold new research questions and problems that are arising and emerging as we re-imagine search. This workshop aims to provide a forum for the IR community to voice and discuss their concerns and pitch proposals for building and strengthening the field and community.

关键词： search Information retrieval Artificial intelligence

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AI Agent for Information Retrieval: Generating and Ranking 24

AI Agent for Information Retrieval: Generating and Ranking

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhang, Yongfeng Liu, Zhiwei Wen, Qingsong Pang, Linsey Liu, Wei Yu, Philip S. Rutgers State Univ New Brunswick NJ 08901 USA Salesforce AI Res Palo Alto CA USA Squirrel AI Learning Seattle WA USA Salesforce San Francisco CA USA Univ Technol Sydney Sydney NSW Australia Univ Illinois Chicago IL USA

ISBN: (纸本)9798400704369

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.

关键词： Information Retrieval AI agent Large Language models (LLMs) Recommender Systems

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MIND (Mixed-Initiative Next-gen Design): workshop on Blending Agents and Direct Manipulation for Harnessing LLMs 25

MIND (Mixed-Initiative Next-gen Design): Workshop on Blendin...

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30th International Conference on Intelligent User Interfaces-IUI

作者： Dinakar, Karthik Lieberman, Henry Wu, Sonia Icahn Sch Med Mt Sinai Dept Med New York NY 10029 USA MIT CSAIL Cambridge MA USA Pienso New York NY USA

ISBN: (纸本)9798400714092

Since the 1980s, a key debate in human-centered computing involving machine learning at IUI is between agent-driven systems and direct manipulation. The explosion of Large Language Models (LLMs), particularly auto-regressive as agents serving as chatbots, generative search, and work automation tools, has also brought with it inherent limitations. We posit that efforts to address and alleviate these LLM challenges-hallucinations, unpredictable outputs, lack of transparency, and difficulties in customization-cannot be solved through algorithmic improvements alone but require elevated mixed-initiative interface design at the heart of the IUI community. Thisworkshop aims to bridge the gap between agent-driven automation and direct manipulation by exploring mixed-initiative interaction models that blend the strengths of both paradigms to empower end-users seeking to harness LLMs.

关键词： Direct manipulation Agents Human-Centered Design Mixed-Initiative Interfaces

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Visualizing Literary Linked Data for Public Library Users in the new User Interface for BookSampo – Finnish Fiction Literature on the Semantic Web 8

Visualizing Literary Linked Data for Public Library Users in...

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8th International workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs, VOILA! 2023

作者： Ahola, Annastiina Hyvönen, Eero Aalto University Finland University of Helsinki Finland

The BookSampo Linked Data (LD) portal was deployed in 2011 by the Public Libraries of Finland and has today nearly 1.6 million annual users. Its large knowledge graph (KG) covers virtually all Finnish Fiction literature but has not been fully exploited in Digital Humanities. This paper discusses how the KG can be used for literary search, data exploration, and research by presenting a new BookSampo user interface (UI) based on faceted semantic search and browsing with seamlessly integrated data-analytic visualization tools. This application makes it possible for the first time to analyze the BookSampo data in versatile ways without programming skills. The analysis results presented suggest interesting spatial, temporal, and topical trends in how the Finnish fiction literature has evolved during the last decades. © 2023 Copyright for this paper by its authors.

关键词： Linked data

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SLA-based task scheduling in cloud computing using randomized PSO algorithm

SLA-based task scheduling in cloud computing using randomize...

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2024 International Symposium on computing and Intelligent Systems, SCI 2024

作者： Lipsa, Swati Dash, Ranjan Kumar School of Computer Sciences Odisha University of Technology and Research Odisha Bhubaneswar751029 India

In SLA-based cloud computing environments, efficient task scheduling is critical for optimizing resource utilization and meeting performance objectives outlined in Service Level Agreements (SLAs). The existing literature highlights several limitations in this domain, including the complexity of scheduling tasks across heterogeneous Virtual Machines (VMs) while satisfying various constraints such as profit maximization or makespan minimization. These limitations underscore the need for innovative scheduling algorithms capable of addressing the dynamic nature of cloud environments. These challenges motivated us to develop a randomized Particle Swarm Optimization (PSO) algorithm designed specifically for the task scheduling problem in SLA-based cloud computing environments. This algorithm aims to efficiently allocate diverse tasks to available VMs while adhering to the critical constraints defined by the SLAs. The PSO algorithm employs randomized search strategies to efficiently explore the solution space and determine the optimal task for VM assignments. Further, to assess the effectiveness of the proposed scheduling algorithm, comparative analyses are performed against existing scheduling approaches such as Shortest Job First (SJF) and First Come First Serve (FCFS). These comparisons evaluate the performance, efficiency, and scalability of the randomized PSO algorithm in terms of profit maximization and makespan minimization. The outputs of this comparison provide valuable insights into the efficacy of the proposed algorithm in SLA-based cloud computing environments. Therefore, the findings of this study offer practical implications for enhancing the reliability, scalability, and performance of cloud-based services while ensuring compliance with SLA commitments and customer expectations. © 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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Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

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IEEE ACCESS 2022年 10卷 12678-12701页

作者： Naghavipour, Hadi Soon, Tey Kok Bin Idris, Mohd Yamani Idna Namvar, Morteza Bin Salleh, Rosli Gani, Abdullah Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Queensland Business Sch Brisbane Qld 4000 Australia Univ Malaysia Sabah Fac Comp & Informat Labuan 88400 Malaysia

With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms, such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

关键词： Metaheuristics Quality of service Internet of Things Optimization Cloud computing search problems Systematics Service computing cloud computing quality of service service composition metaheuristics hybrid metaheuristics mapping study

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Towards supervised real-time human activity recognition on embedded equipment

Towards supervised real-time human activity recognition on e...

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IEEE International workshop on Metrology for Living Environment (MetroLivEnv)

作者： Najeh, Houda Lohr, Christophe Leduc, Benoit IMT Atlantique Lab STICC Brest France Delta Dore Co Bonnemain France

ISBN: (纸本)9781665408936

In recent years, real-time human activity recognition (HAR) has reached importance due to its applications in various domains such as assistive services for the elderly in smart buildings, monitoring, well-being, comfort and security. Various techniques, researched within the image processing and computer vision communities, have been established to recognize human activities in real-time, but all of them are based on wearable sensors and there is no much attention for ambient sensor based approaches. In the literature, deep learning (DL) is one of effective and cost-efficient supervised learning model and different architectures haves been investigated for real-time HAR. However, it still struggles with the quality of data as well as hardware implementation issues. This paper presents two contributions. Firstly, an intensive analysis of DL architectures and its characteristics along with their limitations in the framework of real time HAR are investigated. Secondly, existing hardware architectures and related challenges in this field are highlighted (adaptation of DL architectures towards microcontrollers, difficulty to provide a smart home with numerous sensors and trends regarding cloud-bases approaches). Then, new research directions and solutions around the real-time data quality assessment, the study of main performance factors for DL on microcontrollers, the concept of minimal sensors set up for the employment of IoT devices and the distributed intelligence are suggested to solve them respectively and to improve this field.

关键词： smart building real time human activity recognition deep learning software and hardware architectures edge computing

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workshop on the Future of Digital Libraries 21

Workshop on the Future of Digital Libraries

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Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries

作者： George Buchanan Dana McKay David Bainbridge Melbourne iSchoool University of Melbourne Parkville Australia School of Computing Technologies RMIT University Melbourne Australia School of Computing University of Waikato Hamilton New Zealand

ISBN: (纸本)9781665417709

This workshop will examine the future landscape of digital library research and practice. While conventional facilities of digital libraries, for example indexation, search and browsing of collections of static texts are well understood, there is growing demand for a richer range of content including dynamic data streams, linking of heterogeneous content and automated analysis. We aim to uncover the common agenda for the features of future digital libraries, and the corresponding challenges for research and practice. Contributions from both theory and practice, and from technologists, information scientists and researchers in human information behaviour will all contribute to this workshop.

关键词： digital libraries

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BrandCrafter AI, an AI-Based Brand Identity Generation Platform

BrandCrafter AI, an AI-Based Brand Identity Generation Platf...

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IEEE Annual computing and Communication workshop and Conference (CCWC)

作者： Selim Gawad Mira Kim Department of Computer Science College of Engineering and Computer Science California State University - Fullerton

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In today's dynamic and highly competitive market, brand differentiation has become both essential and complex. The growth of social media and enhanced digital accessibility have transformed brand promotion into a multifaceted challenge, requiring a strategic and ongoing connection with target audiences to build loyalty and deter them from migrating to competitors. The constant evolution of social media trends and search engine optimization has added layers of complexity, creating an ongoing challenge for brands to remain visible and relevant. For new entrepreneurs, the cost of professional branding consultants is often beyond reach. This paper explores the potential of large language models (LLMs) as a cost-effective, automated solution for generating comprehensive, customized brand identity guidelines. We investigate the effectiveness of LLMs in creating cohesive branding strategies, encompassing brand tone, visual elements, and audience alignment, thus offering a scalable alternative to traditional consultancy services. We conducted a pilot study involving two participants, demonstrating positive outcomes, showing that LLM-generated brand identity guidelines were relevant and consistent and provided valuable support in the branding process.

关键词： Visualization Costs Social networking (online) Brand management search engines Market research Artificial intelligence Optimization Guidelines Business

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