Article
Computer Science, Artificial Intelligence
Pablo Garcia Gomez, Ines Gonzalez-Rodriguez, Camino R. Vela
Summary: This article discusses the flexible job shop scheduling problem and its variant where uncertainty in operation processing times is modeled using triangular fuzzy numbers. The objective is to minimize total energy consumption, considering the energy required by resources during operation and the energy consumed when resources are switched on. To solve this NP-Hard problem, a memetic algorithm is proposed, combining global search and local search. The focus is on obtaining an efficient method that can achieve similar solutions to existing state-of-the-art approaches in less time. An extensive experimental analysis compares the algorithm with previous proposals and evaluates the effect of different components on the search.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Istvan A. Harmati, Miklos F. Hatwagner, Laszlo T. Koczy
Summary: Fuzzy cognitive maps are an effective modeling tool for complex systems. Global stability is not always essential, as multiple fixed points are preferred in many applications.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
William Hoyos, Jose Aguilar, Mauricio Toro
Summary: In this paper, a methodology called PRV-FCM is presented, which combines fuzzy cognitive maps (FCMs) and metaheuristic algorithms to generate prescriptive models. The proposed approach uses a genetic algorithm to optimize the prescriptive concepts based on system concepts and the stability of the FCM. Experimental results in four scenarios demonstrate the capability of PRV-FCM to find solutions that lead to desired values for the variables of interest.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Kai Wu, Jing Liu
Summary: This paper investigates the problem of learning large-scale fuzzy cognitive maps with a limited computational budget. The authors propose two strategies to address this problem and demonstrate the effectiveness of the proposed methods through experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Green & Sustainable Science & Technology
Oguz Emir, Sule Onsel Ekici
Summary: In recent years, waste management has gained attention due to sustainability concerns and the depletion of natural resources. Food waste management is particularly important given the growing population and hunger crisis. Integrated assessment models (IAMs) have been commonly used to study food waste and provide insights to policymakers, while the Fuzzy Cognitive Map (FCM) extended with intuitionistic fuzzy sets offers a framework for analyzing interactions between factors and prioritizing policies.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Abdollah Amirkhani, Masoud Shirzadeh, Tufan Kumbasar, Behrooz Mashadi
Summary: This paper presents a framework for the design of a genetically evolved cognitive tracking controller based on IT2-FCM, utilizing a systematic approach based on genetic algorithms to optimize parameters and learn fuzzy rules. Experimental results demonstrate the superiority of the proposed cognitive control system over conventional and fuzzy controllers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Lilan Liu, Kai Guo, Zenggui Gao, Jiaying Li, Jiachen Sun
Summary: This paper proposes a digital twin-driven shop floor adaptive scheduling method to address the traditional static environment problem. By establishing a digital twin model and using a reinforcement learning enhanced genetic algorithm, the method achieves real-time monitoring and adaptive optimization of shop floor scheduling, effectively solving a key problem in smart manufacturing.
Article
Computer Science, Artificial Intelligence
Piotr Szwed
Summary: Fuzzy Cognitive Maps (FCMs) are a soft computing technique used in various fields like system behavior modeling, time series prediction, decision making, and process control. This study introduces an FCM based classifier with a fully connected map structure, learning weights with a gradient algorithm. The aim is to create a descent general purpose classifier with performance comparable to classical methods, and preliminary results show promising outcomes.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics
Mehran Amini, Miklos F. Hatwagner, Laszlo T. Koczy
Summary: Due to the increasing demand on freeway networks, infrastructure improvements alone cannot effectively resolve the issue of congestion. Therefore, the implementation of specific control methods is often the only viable solution. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system were proposed to analyze traffic data and model traffic flow at a macroscopic level, with the goal of addressing congestion-related issues. The results demonstrated that fuzzy inference systems and fuzzy cognitive maps can predict congestion levels, simulate traffic flow, and conduct scenario analysis, thereby improving the performance of traffic control strategies.
Article
Computer Science, Information Systems
Yuzhen Li, Yabin Shao
Summary: In real life, uncertainty problems arise due to the vagueness of concepts, making it difficult to determine if an object conforms to a particular concept. Fuzzy methods are suitable for dealing with such uncertain events. The D-number cognitive maps and D-number fuzzy cognitive maps are intelligent framework models that can handle multiple sources of information with uncertainty and incomplete information, addressing the challenge of knowledge combination.
Article
Computer Science, Artificial Intelligence
Tianming Yu, Qunfeng Gan, Guoliang Feng, Guangxin Han
Summary: In recent years, classification based on fuzzy cognitive maps (FCM) has attracted extensive attention and has been successfully applied in many engineering problems. However, existing methods lack universality and low classification accuracy. In this study, a new model integrating capsule network into inference rules is proposed to enhance the interpretability, universality, and classification performance. Experimental results demonstrate the superiority of the proposed method over low-level cognitive maps.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Alya Al Farsi, Dobrila Petrovic, Faiyaz Doctor
Summary: A Fuzzy Cognitive Map (FCM) is an effective approach for reasoning and decision making, but its capability for handling uncertain data is limited. In this work, a new reasoning algorithm is introduced, which uses Type 2 Fuzzy Sets based on z slices for modelling uncertain weights connecting FCM's concepts. The algorithm preserves uncertainty in values as long as possible and shows better correlation to experts' subjective knowledge compared to traditional methods and statistical approaches.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Gonzalo Napoles, Nevena Rankovic, Yamisleydi Salgueiro
Summary: This paper proposes a post-hoc explanation method for computing concept attribution in Fuzzy Cognitive Map (FCM) models, which combines SHapley Additive exPlanations (SHAP) values and the dynamic properties of the FCM model. The relevance of neural concepts is computed by considering the initial activation values and hidden states of the model. Experimental results demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Omid Orang, Petronio Candido de Lima e Silva, Frederico Gadelha Guimaraes
Summary: Among various soft computing approaches, fuzzy cognitive maps (FCMs) have shown remarkable results in time series forecasting. FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, making them a powerful tool for simulating and studying complex systems. This survey paper presents an overview of the most relevant FCM-based time series forecasting models and provides ideas for future research to enhance their capabilities.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics
Alina Vladimirovna Petukhova, Anna Vladimirovna Kovalenko, Anna Vyacheslavovna Ovsyannikova
Summary: This paper proposes a scenario analysis algorithm based on fuzzy cognitive maps for decision support in complex systems. The algorithm effectively obtains the initial state of the system using the theory of neutrosophic fuzzy equations, reducing the time required for problem-solving.
Article
Operations Research & Management Science
Adil Baykasoglu, Burcu Kubur Ozbel
Summary: The paper introduces a new approach based on the framework of interval analysis to solve maximum flow problems in uncertain networks. By employing risk explicit interval linear programming model and collaborative game theoretic approach in multiple-owners network, the proposed method aims to maintain stable network flow under interval uncertainty.
OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Adil Baykasoglu, Nurhan Dudakli, Mumin Emre Senol, Feyzi Komurcu
Summary: This paper aims to optimize the efficiency of spar cap production in wind turbine blade production by introducing a new optimization problem and developing two mathematical programming models. Solving the roll allocation problem optimally can significantly reduce workers' walking time/distance, improve manufacturing practices, indirectly reduce total operation time, and increase productivity in wind turbine blade production.
ENGINEERING WITH COMPUTERS
(2021)
Article
Engineering, Industrial
Adil Baykasoglu
Summary: This study optimised cutting conditions in multi-pass milling using a weighted superposition attraction algorithm, satisfying all cutting constraints and providing the best solutions for all test cases within a reasonable computational time.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Adil Baykasoglu, Sener Akpinar
Article
Computer Science, Interdisciplinary Applications
Zeynep Didem Unutmaz Durmusoglu, Alptekin Durmusoglu
Summary: Authors consider various factors when selecting journals, such as prospective impact, acceptance probability, and publication time. While journal-related information can be found on journal websites, further research is needed to understand how to incorporate these factors into modeling. TOPSIS method can be useful in ranking journal alternatives, but finding the correct weights for factors is crucial in establishing a decision-making model.
Article
Operations Research & Management Science
Adil Baykasoglu, Nurhan Dudakli, Kemal Subulan, A. Serdar Tasan
Summary: This paper explores the importance of fleet planning in intermodal transportation and proposes a holistic approach to address the complexity of fleet planning in comparison with unimodal systems. Through a comprehensive mixed-integer linear programming model, the optimization of fleet planning is achieved.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Fehmi Burcin Ozsoydan, Adil Baykasoglu
Summary: The Flower Pollination Algorithm (FPA) simulates the pollination behavior of flowers to solve global optimization problems, and has been extended and modified to improve performance. Modifications include adjustments to the pollination mechanism and convergence control, as well as the extension to a species-based algorithm for independent search of promising regions. These modifications show significant improvements in solving various optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Adil Baykasoglu, Fatma S. Madenoglu
Summary: This study proposed a GRASP algorithm for simultaneous dynamic scheduling of operations and preventive maintenance activities in flexible job shops, considering various dynamic events and maintenance strategies. The results demonstrated that this approach is effective in improving performance in dynamic flexible job shop scheduling environments.
Article
Computer Science, Artificial Intelligence
Adil Baykasoglu, Elif Ercan
Summary: The paper examines the rank reversal problem in MADM methods, focusing on the WASPAS method. Computational experiments show that traditional normalization techniques lead to rank reversal problems in WASPAS, but using modified normalization techniques can mitigate this issue.
Article
Computer Science, Cybernetics
Adil Baykasoglu, Burcu Felekoglu, Ceylin Unal
Summary: The usage of learning management systems (LMSs) has become widespread due to the disruption caused by the COVID-19 pandemic. Selecting a suitable LMS is a complex decision-making problem that involves considering multiple criteria and inputs from different parties. Usability evaluation of LMS is a critical step in the decision-making process. This study proposes an axiomatic design procedure (ADP) based approach for the perceived usability evaluation of SAKAI-LMS. The ADP method allows easy data fusion and setting performance targets. A questionnaire is developed to collect data on usability criteria and their importance from system users. The proposed approach provides an easy and practical evaluation of perceived usability of LMSs for decision makers. It has been verified through a real-life case study at an academic department.
Article
Computer Science, Artificial Intelligence
Kemal Subulan, Bilge Varol, Adil Baykasoglu
Summary: This paper introduces a new unequal-area capability-based facility layout design problem and proposes a MINLP model and a heuristic decomposition-based iterative solution approach to solve it. Computational results demonstrate the effectiveness and applicability of the proposed approach through illustrative examples and a real-life application.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Manufacturing
Cengiz Baykasoglu, Adil Baykasoglu, Erhan Cetin
Summary: The objective of this study is to find the optimum design configurations for functionally graded lattice structure filled aluminum tubes under multiple impact loading conditions. The optimal design is sought for maximizing specific energy absorption and minimizing peak crush force by considering base strut diameter, draft angle, and aspect ratio as filler material design parameters. Finite element simulations, regression meta-models, and an attraction-repulsion algorithm were employed to establish the design space, estimate objective function values, create design alternatives, and seek their optimum combinations. The results showed that the crashworthiness performance of hybrid structures can be effectively enhanced by selecting appropriate lattice filler parameters, with a potential improvement of up to 76% in specific energy absorption for square tubes. This study provides a guideline for the optimum design of functionally graded lattice structure filled thin-walled structures under multiple impact loading conditions.
INTERNATIONAL JOURNAL OF CRASHWORTHINESS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Adil Baykasoglu, Mumin Emre Senol
Summary: This study demonstrates the improvement of metaheuristic algorithms performance through the application of Levy flight to the WSAR algorithm. The experimental results show that the Levy flight WSAR algorithm performs better than other algorithms in constrained design optimization problems.
PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS (ICHSA 2022)
(2022)
Article
Sociology
Nurhan Dudakli, Burcu Felekoglu, Adil Baykasoglu
Summary: This paper explores the reverse innovation process of multinational enterprises in emerging markets and identifies the key factors for successful reverse innovation, including the quality of innovation ideas, collaboration between MNEs and local enterprises, and unique diffusion strategies.
INNOVATION-THE EUROPEAN JOURNAL OF SOCIAL SCIENCE RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Alptekin Durmusoglu, Zeynep Didem Unutmaz Durmusoglu
Summary: This article analyzes all TCS-related patents issued during the period of 2009-2018 to provide an overview of TCS technological developments, including trends and growth in national distribution, subfields, and applicants. Various countries' areas of TCS technology specialization are also reported.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2021)
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)
