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37 Papers

Hybrid Manufacturing / Remanufacturing Inventory Model with Two Markets and Price Sensitive Demands with Competition

Topics: Optimization; Decision Analysis; OR in Logistics, Maintenance, and Supply

Authors: Matthieu Godichaud and Lionel Amodeo

Affiliation: LIST3N (Computer and Digital Society) Laboratory, University of Technology of Troyes, Troyes, France

Digital Object Identifier: 10.5220/0012429900003639

Proceedings of ICORES 2024, ISBN: 978-989-758-681-1

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A hybrid system that produces new and remanufactured products on a common line for two distinct markets is studied in this article. We consider price sensitive demands with competition between the two types of products. The problem is to maximize a profit as a function of economic production quantities and demands or prices decisions. The resulting model is a mixed nonlinear model with linear and nonlinear constraints. A mathematical analysis is proposed to develop an efficient resolution approaches. Numerical study shows that sequential decisions, determining demands first and reorder intervals after, provides solutions closed to optimum. Sensitivity analysis highlights the importance of demand parameters compared to those related to inventory and the importance of considering all costs, not just setup and holding costs, when evaluating order or production quantities.

Importance of Context Awareness in NLP

Topics: Tools and Technologies for Knowledge Management; AI for Knowledge Management

Authors: Nour Matta , Nada Matta and Philippe Herr

Affiliation: LIST3N, University of Technology of Troyes, 12 Rue Marie Curie, 42060 10004 Troyes Cedex, France

Digital Object Identifier: 10.5220/0012994700003838

Proceedings of KMIS 2024, ISBN: 978-989-758-716-0

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Context is a complex notion, that enables the understanding of happenings and concepts in an environment and the analysis of their influence (Adomavicius et al, 2011) As previously mentioned, context plays a major role in assigning meanings to words, sentences, and texts when dealing with text analysis. Multiple natural language processing approaches aim to consider “context” in analyzing the information extracted and applying a sort of word sense disambiguation (Adhikari et al, 2019). Numerous intelligence systems require knowledge of happening and are context dependent, but the definition of context and context elements used varies from one application to another based on needs. Context plays several roles in text analysis especially to reduce ambiguity and semantic extraction. In this paper, main influence of context on TextMining and NLP are shown.

Unsupervised Feature Selection Using Extreme Learning Machine

Topics: Deep Learning; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Neural Approaches and Neural Architectures; Neural based Implementation, Applications and Solutions

Authors: Mamadou Kanouté , Edith Grall-Maës and Pierre Beauseroy

Affiliation: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, Troyes, France

Digital Object Identifier: 10.5220/0013067500003837

Proceedings of NCTA 2024, ISBN: 978-989-758-721-4

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In machine learning, feature selection is an important step in building an inference model with good generalization capacity when the number of variables is large. It can be supervised when the goal is to select features with respect to one or several target variables or unsupervised where no target variable is considered and the goal is to reduce the number of variables by removing redundant variables or noise. In this paper, we propose an unsupervised feature selection approach based on a model that uses a neural network with a single hidden layer in which a regularization term is incorporated to deal with nonlinear feature selection for multi-target regression problems. Experiments on synthetic and real-world data and comparisons with some methods in the literature show the effectiveness of this approach in the unsupervised framework.

An Approach to Developing Ontology-Based Tools for Event Series Analysis

Topic: Applications and Case-Studies

Authors: Anton Platunov 1 , Lyudmila Lyadova 1 , Nada Matta 2 , Viacheslav Lanin 3 and Elena Zamyatina 1

Affiliations: 1 Department of Information Technologies in Business, HSE University, Perm, Russian Federation , 2 University of Technology of Troyes, Troyes, France , 3 Perm State University, Perm, Russian Federation

Digital Object Identifier: 10.5220/0012259100003598

Proceedings of KEOD 2023, ISBN: 978-989-758-671-2

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Existing process mining methods allow to investigate processes in different domains. Besides mandatory event attributes like as identifier, activity, and timestamp, additional event attributes can be present in data sources. The analysing dynamics of changing the values of additional attributes allows to get important information on the system. The applications must be developed by programmers with programming languages to implement new methods of analysis. An approach to develop tools based on the use of algorithm designers and expression builders like those included in MS Office applications is proposed in the paper. Their use does not require programming skills. The implementation of the approach is based on a multifaceted ontology, including descriptions of the rules for developing functions, as well as a description of functions for generating and analysing event logs in accordance with these rules. The user interface for developing rules and the algorithm for their interpreting are implemented in the research prototype of the application.

Neural Network-Based Approach for Supervised Nonlinear Feature Selection

Topics: Deep Learning; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Neural Approaches and Neural Architectures; Neural based Implementation, Applications and Solutions

Authors: Mamadou Kanouté , Edith Grall-Maës and Pierre Beauseroy

Affiliation: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, Troyes, France

Digital Object Identifier: 10.5220/0012185700003595

Proceedings of NCTA 2023, ISBN: 978-989-758-674-3

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In machine learning, the complexity of training a model increases with the size of the considered feature space. To overcome this issue, feature or variable selection methods can be used for selecting a subset of relevant variables. In this paper we start from an approach initially proposed for classification problems based on a neural network with one hidden layer in which a regularization term is incorporated for variable selection and then show its effectiveness for regression problems. As a contribution, we propose an extension of this approach in the multi-output regression framework. Experiments on synthetic data and real data show the effectiveness of this approach in the supervised framework and compared to some methods of the literature.

Simulation Model Validation based on Ontological Engineering Methods

Topics: Applications and Case-Studies; Ontology Matching and Alignment; Domain Analysis and Modeling

Authors: Elena Zamyatina 1 , Denis Churin 1 , Viacheslav Lanin 1 , Lyudmila Lyadova 1 and Nada Matta 2

Affiliations: 1 Department of Information Technologies in Business, HSE University, Perm, Russian Federation , 2 University of Technology of Troyes, Troyes, France

Digital Object Identifier: 10.5220/0011589000003335

Proceedings of KEOD 2022/IC3K, ISBN: 978-989-758-614-9

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A task of the simulation models examination (verification and validation, V&V) is considered. At the V&V process the correspondence degree of the simulation model created by developers to the simulated object, that description is presented in the form of a conceptual model built by customers, is determined. An ontological approach is proposed to determine the semantic proximity of the simulation model and the conceptual model, whose descriptions are presented in the form of ontologies. Matching rules can also be defined with ontology based on the metrics chosen by the customer. The approach has been tested using the simulation system Triads. The results of the matching algorithm execution are illustrated by an example. The article provides description of the simulation model ontology created in TriadNS and conceptual model ontology, developed with MASK method. The metrics used for proximity assessment are described.

Participatory Design in Knowledge Management: An Opportunity to Small and Medium Business

Topics: KM Strategies and Implementations ; Tools and Technologies for Knowledge Management

Authors: Amanda Carolina Teixeira 1 , Nelson Tenório 2 ; 1 , Danieli Pinto 3 , Nada Matta 4 and Arthur Gualberto Bacelar da Cruz Urpia 2 ; 1

Affiliations: 1 Stricto Sensu Departament, Cesumar University, Maringá, Paraná, Brazil , 2 Cesumar Institute of Science Technology and Innovation (ICETI), Maringá, Paraná, Brazil , 3 Business Faculty of Cianorte, Cianorte, Paraná, Brazil , 4 Technological University of Troyes (UTT), France

Digital Object Identifier: 10.5220/0010655400003064

Proceedings of KMIS 2021/IC3K, ISBN: 978-989-758-533-3

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Knowledge Management (KM) has become an essential driver to develop dynamic capabilities for businesses, organizational learning, and boost knowledge assets on behalf of competitiveness. Nonetheless, some critical success factors are hampering KM implementation, such as a lack of a KM strategy, cultural aspects, leadership, and technology. In this paper, we focused on KM implementation within Small and Medium Enterprises (SMEs). Our study explores if Participatory Design might be considered an opportunity for KM implementation in SMEs, by focusing on information technology as a critical success factor. To achieve this goal, we conducted an ethnographic study in the real environment of a consulting firm that is starting its own KM. Our results show that Participatory Design might be recommended to SMEs to implement KM, by taking advantage of already available, but underused technological tools.

A Hybrid Genetic and Simulation Annealing Approach for a Multi-period Bid Generation Problem in Carrier Collaboration

Topics: Optimization; Management Sciences; Industrial Engineering; Scheduling; Routing; Logistics; OR in Transportation

Authors: Elham Jelodari Mamaghani , Haoxun Chen and Christian Prins

Affiliation: Industrial Systems Optimization Laboratory, Charles Delaunay Institute and UMR CNRS 6281, University of Technology of Troyes, Troyes 10004 and France

Digital Object Identifier: 10.5220/0007369203070314

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

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In this article, a new vehicle routing problem appeared in carrier collaboration via a combinatorial auction (CA) is studied. A carrier with reserved requests wants to determine within a time horizon of multi periods (days) which requests to serve among a set of selective requests open for bid of the auction to maximize its profit. In each period, the carrier has a set of reserved requests that must be served by the carrier itself. Each request is specified by a pair of pickup and delivery locations, a quantity, and two time windows for pickup and delivery respectively. The objective of the carrier is to determine which selective requests may be served in each period in addition of its reserved requests and determine optimal routes to serve the reserved and selective requests to maximize its total profit. For this NP-hard problem, a mixed-integer linear programming model is formulated and a genetic algorithm combined with simulated annealing is proposed. The algorithm is evaluated on instances with 6 to 100 requests. The computational results show this algorithm significantly outperform CPLEX solver, not only in computation time but also in solution quality.
Collapse Button Related Ontology Subjects/Areas/Topics (18)
Applications

Knowledge-Based Systems

OR in Transportation

Artificial Intelligence

Logistics

Pattern Recognition

e-Business

Management Sciences

Routing

Enterprise Information Systems

Methodologies and Technologies

Scheduling

Industrial Engineering

Operational Research

Software Engineering

Knowledge Discovery and Information Retrieval

Optimization

Symbolic Systems

Assist the Sustainable Development within Industries through the Territorial Knowledge Ontology

Topics: Knowledge Management Projects; KM Strategies and Implementations ; Management and Organisational Issues in Information Systems

Authors: Amer Ezoji and Nada Matta

Affiliation: ICD/ TECH-CICO, University of Technology of Troyes, 12 Rue Marie Curie, Troyes and France

Digital Object Identifier: 10.5220/0008165201020112

Proceedings of KMIS 2019/IC3K, ISBN: 978-989-758-382-7

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Studying of territory as the main dimension of sustainability impact in the industrial activities and decision makers’ information when considering the sustainability in their activities. Therefore, exploring of territorial knowledge in order to integrate into industries activates is needed. So, this research is proposed a descriptive ontology for territorial knowledge (DOTK) which make explicit the knowledge of actors within industries about the sustainable development goal. Also, implementation of this ontology to a real case is proved that it can identify the intangible and tangible resources of territory for sustainability. Moreover, a semantic graph is proposed which shows the relationships between entities of DOTK ontology. Final validation of DOTK ontology is performed by the interview with the organizations of sustainable development implementation.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Artificial Intelligence

Knowledge Management and Information Sharing

Knowledge-Based Systems

KM Strategies and Implementations

Knowledge Management Projects

Symbolic Systems

Rethinking Strategies of Hackathons to Increase Team’s Creativity: Findings of a Qualitative Research

Topics: Communication, Collaboration and Information Sharing; Knowledge Management Projects

Authors: Nelson Tenório 1 , Gisele Caroline Urbano Lourenço 2 , Mariana Oliveira 2 , Steffi Aline Stark Becker 2 , Fabrício Tomaz Bernardelli 2 , Hassan Atifi 3 and Nada Matta 3

Affiliations: 1 Cesumar Institute of Science, Technology and Innovation (ICETI), UniCesumar, Maringá, Paraná and Brazil , 2 Masters’Program in Knowledge Management, UniCesumar, Maringá, Paraná and Brazil , 3 Department of Computer Science, University of Technology of Troyes, Troyes and France

Digital Object Identifier: 10.5220/0008164300920101

Proceedings of KMIS 2019/IC3K, ISBN: 978-989-758-382-7

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Hackathons are events that have become increasingly common around the world. This kind of event, described as a programming marathon, is based on problem-solving that can go beyond the technological boundary. This paper presents the findings of an international hackathon to aid its organizers to rethink their strategies to improve the development of the team’s creativity to solve the challenge proposed. The paper summarizes qualitative research based on interviews and observations which point out that the organizers should consider strategies to improve knowledge application, resolving conflicts, individual learning, and experienced emotions, during pre-hackathon as well as post-hackathon events. Our findings could leverage the innovation, creativity, and knowledge sharing and creation within hackathons.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Artificial Intelligence

Knowledge Management and Information Sharing

Knowledge-Based Systems

Communication, Collaboration and Information Sharing

Knowledge Management Projects

Symbolic Systems

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