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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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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 Companion, IUI 2025

作者： Dinakar, Karthik Lieberman, Henry Wu, Sonia Department of Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States CSAIL MIT CambridgeMA United States Pienso New YorkNY United States

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. This workshop 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. © 2025 Copyright held by the owner/author(s).

关键词： Chatbots

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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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RelWeb 2025: The International workshop on Resource-Efficient Learning for the Web 25

RelWeb 2025: The International Workshop on Resource-Efficien...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Chuxu Zhang Kaize Ding Jundong Li Dongkuan Xu Haoyu Wang Derek Zhiyuan Cheng Huan Liu University of Connecticut Storrs CT USA Northwestern University Evanston IL USA University of Virginia Charlottesville VA USA North Carolina State University Raleigh NC USA University at Albany Albany NY USA Google DeepMind Mountain View CA USA Arizona State University Tempe AZ USA

ISBN: (纸本)9798400713316

Deep learning techniques, such as large language models and graph neural networks, have demonstrated impressive effectiveness across various web applications. Despite their success, the advancement of these methods is frequently hampered by different resource constraint challenges. Key challenges include the scarcity of labeled data (data-level constraints), the need for smaller model sizes that fit real-world computing environments (model-level constraints), and the integration of neural network design with system and hardware for energy efficiency (system-level constraints). Tackling these issues is essential for the effective and efficient deployment of models in various real-world web systems and applications, including social networks, search engines, recommender systems, question answering, and content analysis. Therefore, there is an urgent need to develop innovative and efficient learning techniques that can overcome these resource limitations. The proposed international workshop on ''Resource-Efficient Learning for the Web (RelWeb 2025)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning. Our workshop objectives are threefold: (1) to establish an engaging platform where experts and participants can share their latest research findings and innovative practices in resource-efficient learning; (2) to explore emerging technologies and trends that could shape the future of this field; and (3) to foster a collaborative environment that encourages partnerships and exchanges among participants. Together, we can advance resource-efficient learning and propel future research in this area.

关键词： deep learning

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The 3rd workshop on AI Agent for Information Retrieval: Generating and Ranking 25

The 3rd Workshop on AI Agent for Information Retrieval: Gene...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Qingsong Wen Yongfeng Zhang Zhiwei Liu Julian McAuley Hua Wei Linsey Pang Wei Liu Philip S. Yu Squirrel Ai Learning Bellevue WA USA Rutgers University New Jersey USA Salesforce AI Research Palo Alto CA USA UCSD San Diego CA USA Arizona State University Arizona USA Salesforce San Francisco CA USA University of Technology Sydney Sydney NSW Australia University of Illinois at Chicago Chicago IL USA

ISBN: (纸本)9798400713316

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 and 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 and emerging trends, and foster knowledge exchange and collaboration within the community.

关键词： information retrieval

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Tools Supporting search computing Application Development

Tools Supporting Search Computing Application Development

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workshop on new trends in search computing/workshop on search computing challenges and directions

作者： Brambilla, Marco Tettamanti, Luca Politecn Milan Dipartimento Elettron & Informaz I-20133 Milan Italy

ISBN: (纸本)9783642196676

search computing provides a solution to the problem of multidomain, exploratory search. To manage the complex set of subsystems and configurable options, the proper set of development and configuration tools is needed. In this chapter we describe the development process phases envisioned for designing search computing applications and also a unified tool suite that aggregates a set of design and configuration tools. The tools cover the phases of service registration, service annotation, and application configuration. The latter in turn is organized in query specification, query plan refinement, and definition of user interface options.

关键词： search computing tools software engineering mashup development process

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Bio-SeCo: Integration and Global Ranking of Biomedical search Results

Bio-SeCo: Integration and Global Ranking of Biomedical Searc...

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workshop on new trends in search computing/workshop on search computing challenges and directions

作者： Masseroli, Marco Ghisalberti, Giorgio Politecn Milan Dipartimento Elettron & Informaz I-20133 Milan Italy

ISBN: (纸本)9783642196676

This chapter presents how well known bioinformatics resources can be described as search services in the search computing framework and how integrated analyses over such services can be carried out. An initial set of bioinformatics services has been described and registered in the search computing framework and a bioinformatics search computing (Bio-SeCo) application using these services has been created. This current prototype application, the available services which it uses, the queries which are supported, the kind of interaction which is therefore made available to the users, and the future scenarios are here described and discussed.

关键词： search bioinformatics data integration ranked data

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Run-Time Adaptivity for search computing

Run-Time Adaptivity for Search Computing

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workshop on new trends in search computing/workshop on search computing challenges and directions

作者： Braga, Daniele Grossniklaus, Michael Paton, Norman W. Politecn Milan Dipartimento Elettron & Informaz Milan Italy Univ Manchester Sch Comp Sci Manchester Lancs England

ISBN: (纸本)9783642196676

In search computing, queries act over internet resources, and combine access to standard web services with exact results and to ranked search services. Such resources often provide limited statistical information that can be used to inform static query optimization, and correlations between the values and ranks associated with different resources may only become clear at query runtime. As a result, search computing seems likely to benefit from adaptive query processing, where information obtained during query evaluation is used to change the way in which a query is executing. This chapter provides a perspective on how run-time adaptivity can be achieved in the context of search computing.

关键词： Web services

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The new Frontier of Web search Technology: Seven challenges

The New Frontier of Web Search Technology: Seven Challenges

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workshop on new trends in search computing/workshop on search computing challenges and directions

作者： Baeza-Yates, Ricardo Broder, Andrei Z. Maarek, Yoelle Yahoo Res Barcelona Spain Yahoo Res Santa Clara CA USA Yahoo Res Haifa Israel

ISBN: (纸本)9783642196676

The classic Web search experience, consisting of returning "ten blue links" in response to a short user query, is powered today by a mature technology where progress has become incremental and expensive. Furthermore, the "ten blue links" represent only a fractional part of the total Web search experience: today, what users expect and receive in response to a "web query" is a plethora of multi-media information extracted and synthesized from numerous sources on and off the Web. In consequence, we argue that the major technical challenges in Web search are now driven by the quest to satisfy the implicit and explicit needs of users, continuing a long evolutionary trend in commercial Web search engines going back more than fifteen years, moving from relevant document selection towards satisfactory task completion. We identify seven of these challenges and discuss them in some detail.

关键词： search engines

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Proximity Rank Join in search computing

Proximity Rank Join in Search Computing

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workshop on new trends in search computing/workshop on search computing challenges and directions

作者： Martinenghi, Davide Tagliasacchi, Marco Politecn Milan Milan Italy

ISBN: (纸本)9783642196676

Rank join can be generalized to sets of relations whose objects are equipped with a score and a real-valued feature vector. Such vectors can be used to compare the objects to one another so as to join them based on a notion of "proximity". The problem becomes then that of retrieving combinations of objects that have high scores, whose feature vectors are close to one another and possibly to a given feature vector (the query). Traditional rank join algorithms may read more input than needed when solving proximity rank join. Such weakness can be overcome by designing new algorithms for which, as in classical rank join, bounding scheme (and a tight version thereof) and pulling strategy play a crucial role to efficiently compute the solution.

关键词： Vectors

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