Article
Engineering, Industrial
Hugo Hissashi Miyata, Marcelo Seido Nagano
Summary: Nowadays, distributed scheduling problem is a reality in many companies. Over the last years, an increasingly attention has been given to the distributed flow shop scheduling problem and the addition of constraints to the problem. This article introduces a new distributed no-wait flow shop scheduling problem using a mix of mixed-integer linear programming and heuristic algorithms. Studies show that the proposed algorithm performs well in the trade-off between efficiency and effectiveness.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Levi R. Abreu, Marcelo S. Nagano
Summary: This paper presents a new hybrid approach combining adaptive large neighborhood search (ALNS) and constraint programming (CP) for solving scheduling problems with setup times and costs. The proposed method outperforms other exact methods and shows promise for solving large-sized instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Hugo Hissashi Miyata, Marcelo Seido Nagano
Summary: This article introduces a distributed blocking flow shop scheduling problem with sequence-dependent setup times and maintenance operations, and proposes an iterative greedy method to solve this problem. Computational experiments demonstrate that the proposed method achieves a good balance between effectiveness and efficiency.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Automation & Control Systems
Bin Qian, Zi-Qi Zhang, Rong Hu, Huai-Ping Jin, Jian-Bo Yang
Summary: In this article, a matrix-cube-based estimation of distribution algorithm is proposed to solve the no-wait flow-shop scheduling problem with sequence-dependent setup times and release times. The algorithm demonstrates efficient exploration and exploitation in the solution space, leading to improved solutions compared to state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Imma Ribas, Ramon Companys, Xavier Tort-Martorell
Summary: This paper addresses the scheduling problem in a parallel flow shop configuration with sequence-dependent setup times. The analysis of various iterated greedy algorithms led to the identification of an efficient algorithm to minimize maximum job completion time. Computational evaluation highlighted the efficiency of searching in different neighborhood structures and the significant impact of the initial solution on performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Operations Research & Management Science
Fernando Siqueira de Almeida, Marcelo Seido Nagano
Summary: In this article, the m-machine no-wait flow shop scheduling problem with sequence dependent setup times is addressed. A new heuristic called I G(A) is developed to solve the problem by repeatedly performing a process of destruction and construction of an existing solution. Computational experiments show that I G(A) outperforms the best literature method for similar applications in overall solution quality by about 35%. Therefore, IG(A) is recommended to solve the problem.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Francisco Angel-Bello, Jobish Vallikavungal, Ada Alvarez
Summary: This paper investigates the makespan minimization in a dynamic single-machine scheduling problem with sequence-dependent setup times, and proposes two rescheduling strategies with corresponding algorithms. Experimental results show that these algorithms are fast and provide high-quality solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Raka Jovanovic, Stefan Voss
Summary: This paper discusses the problem of minimizing makespan on unrelated parallel machines with sequence-dependent setup times and introduces a novel population-based metaheuristic method called FSS. Experimental results show that FSS outperforms GRASP, ACO and WO significantly on standard benchmark instances.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Industrial
Amina Haned, Abida Kerdali, Mourad Boudhar
Summary: This paper addresses the problem of scheduling jobs on identical machines to minimize the maximum completion time. The authors introduce a dynamic programming approach to solve the case with two machines and prove that it has a fully polynomial time approximation scheme. For the case of m machines, heuristics and an adapted genetic algorithm are proposed and evaluated through numerical experiments.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Karam Allali, Said Aqil, Jabrane Belabid
Summary: This paper investigates a multi-objective optimization distributed no-wait permutation flow shop scheduling problem under the constraint of sequence dependent setup time. The study proposes mixed integer linear programming and several efficient metaheuristics to solve this industrial problem. The combination of the genetic algorithm and Nawaz-Enscore-Ham algorithm yields the best results.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Multidisciplinary Sciences
Jingcao Cai, Shejie Lu, Jun Cheng, Lei Wang, Yin Gao, Tielong Tan
Summary: This study investigates the distributed scheduling problem in hybrid flow shops and proposes a collaborative variable neighborhood search algorithm (CVNS) to simultaneously minimize total tardiness and makespan. The algorithm simplifies the problem and defines various neighborhood structures and global search operators. Experimental results validate the advantages of CVNS over the considered problem.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics
Ana Rita Antunes, Marina A. Matos, Ana Maria A. C. Rocha, Lino A. Costa, Leonilde R. Varela
Summary: This paper addresses the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times and solves it using a genetic algorithm. The results show that the genetic algorithm performs better in large instances of the problem.
Article
Computer Science, Artificial Intelligence
Victor M. Valenzuela-Alcaraz, M. A. Cosio-Leon, A. Danisa Romero-Ocano, Carlos A. Brizuela
Summary: The study proposes a cooperative coevolutionary algorithm for solving the no-wait job shop scheduling problem. The algorithm evolves permutations and binary chains simultaneously to optimize sequencing and timetabling decisions, and includes one-step perturbation mechanisms to improve solution quality. Experimental results show that the proposed algorithm produces competitive results and obtains new best values for some instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Victor Fernandez-Viagas, Antonio Costa
Summary: This study introduces two approximate algorithms for solving the single machine scheduling problem, evaluating their effectiveness against both anticipatory and non-anticipatory setup strategies. The computational experience confirms the proposed approaches' effectiveness in comparison to other promising algorithms in related scheduling problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Engineering, Industrial
Jun Kim, Hyun-Jung Kim
Summary: This paper explores a parallel machine scheduling problem where jobs can be processed in multiple parts or in complete form, with various job splitting alternatives. By proposing a mixed integer programming model and a hybrid genetic algorithm, the study shows significant improvement in processing jobs with multiple alternatives compared to a CPLEX solution or a lower bound.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: This paper investigates the mixed no-idle flowshop scheduling problem with sequence-dependent setup times and makespan minimisation criterion. A mathematical formulation and a constructive heuristic are proposed for this new problem, and extensive experiments show that the new heuristic outperforms methods from the literature.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Eliseu J. Araujo, Antonio A. Chaves, Luiz A. N. Lorena
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Article
Engineering, Multidisciplinary
Marcelo Seido Nagano, Fernando Siqueira de Almeida, Hugo Hissashi Miyata
Summary: This article proposes an iterated greedy-with-local-search algorithm for the no-wait flowshop scheduling problem, which outperforms both the mathematical model and the best existing algorithm in terms of effectiveness and efficiency according to computational experiments and statistical analysis.
ENGINEERING OPTIMIZATION
(2021)
Article
Management
Geraldo Regis Mauri, Fabricio Lacerda Biajoli, Romulo Louzada Rabello, Antonio Augusto Chaves, Glaydston Mattos Ribeiro, Luiz Antonio Nogueira Lorena
Summary: This paper proposes two hybrid metaheuristics to solve a multiproduct two-stage capacitated facility location problem, adapting and implementing methods that have been successfully applied to a single-product problem. Experimental results show that these algorithms achieve good performance and are compared to a commercial solver.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: The distributed permutation flowshop scheduling problem (DPFSP) has been widely studied due to the complex production systems with mixed no-idle flowshops. Although the issue of identical factories with mixed no-idle flowshop environments has not been explored in literature, new solutions including MILP formulation, constructive heuristic, and iterated greedy algorithms have been proposed. Extensive experiments showed that the proposed methods outperformed existing approaches.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Levi R. Abreu, Roberto F. Tavares-Neto, Marcelo S. Nagano
Summary: In this paper, a new biased random key genetic algorithm with an iterated greedy local search procedure (BRKGA-IG) is proposed for solving open shop scheduling with routing by capacitated vehicles. The algorithm combines approximation and exact algorithms to achieve high-quality solutions in acceptable computational times. The extensive computational experiments demonstrate that the proposed metaheuristic BRKGA-IG outperforms all other tested methods, showing promise in solving large-sized instances for the new proposed problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Multidisciplinary
Levi Ribeiro de Abreu, Kennedy Anderson Guimaraes Araujo, Bruno de Athayde Prata, Marcelo Seido Nagano, Joao Vitor Moccellin
Summary: This article introduces a new variant of the open shop scheduling problem, known as the open shop scheduling problem with repetitions (OSSPR), which has many applications in automotive and maintenance activities. By presenting a mixed-integer linear programming model and a new constraint programming model, along with a new efficient variable neighbourhood search method, the NP-hard problem is effectively solved with excellent performance shown in computational results.
ENGINEERING OPTIMIZATION
(2022)
Article
Information Science & Library Science
Suzana Xavier Ribeiro, Marcelo Seido Nagano
Summary: This study investigates the relation between knowledge management and university-industry-government collaboration in influencing organizations' performance, focusing on the Brazilian context. An analytical model is proposed, considering structural, relational, cognitive, and contextual dimensions. The findings show that organizational structure, relationships, and cognition play important roles in knowledge flow and sharing, while the context also has an impact. Cultural differences, bureaucracy, and socio-economic reality are identified as main obstacles, while the presence of technology parks, incubators, government incentives, and geographical proximity are facilitators.
VINE JOURNAL OF INFORMATION AND KNOWLEDGE MANAGEMENT SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Guilherme Oliveira Chagas, Luiz Antonio Nogueira Lorena, Rafael Duarte Coelho dos Santos
Summary: This study proposed a hybrid heuristic for detecting overlapping clusters in networks, using a heterogeneous set of good-quality clusters generated by two state-of-the-art overlapping community detection algorithms and local search methods to improve clustering quality.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Luiz H. N. Lorena, Antonio A. Chaves, Geraldo R. Mauri, Luiz A. N. Lorena
Summary: This paper discusses a variant of the Rank Aggregation problem and proposes an Adaptive Biased Random-key Genetic Algorithm (A-BRKGA) to solve it. By introducing a local search component and partial fitness evaluation, the proposed method achieves good results in terms of both quality and computational time.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Geraldo R. Mauri, Luiz H. N. Lorena, Luiz A. N. Lorena, Antonio A. Chaves
Summary: This paper presents a new approach to solve the Point Feature Cartographic Label Placement (PFCLP) problem, taking into account the intensity of label overlap for improved legibility. The proposed strategy, using the Jaccard index, outperformed traditional mathematical models and showed good results for large-sized instances through the Clustering Search algorithm.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Luiz Henrique Nogueira Lorena, Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena
Summary: Aggregating ranks into a consensus is an important task in various fields of science. This paper introduces a specific variation that considers ties between elements, providing a more flexible and meaningful approach for modeling certain circumstances. The proposed Adaptive Biased Random-key Genetic Algorithm with Variable Neighborhood Descent outperformed the state-of-the-art Evolution Strategy in terms of fitness improvement and average solutions in the conducted instances.
2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)
(2021)
Article
Economics
Tiago Fernando Musetti, Alceu Gomes Alves Filho, Marcelo Seido Nagano, Ana Lucia Vitale Torkomian
Summary: This research contributes to the literature on strategic management in micro and small technology-based companies by identifying the main characteristics of strategic behavior. The qualitative research method used case studies to indicate that strategic behavior in these companies includes defining competitive and innovation strategies, allocating organizational resources to innovate and develop dynamic capabilities, and adapting to market demands to gain competitive advantages.
DIMENSION EMPRESARIAL
(2021)
Article
Engineering, Industrial
Tuane Tonani Yamada, Marcelo Seido Nagano, Hugo Hissashi Miyata
Summary: The study proposes constructive methods to minimize total tardiness in production scheduling, with the HENLL algorithm using insertion logic showing the best performance. Additionally, a metaheuristic based on the iterated greedy search method is presented to significantly improve results obtained by the heuristics alone.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2021)
Article
Management
Juliana Keiko Sagawa, Marcelo Seido Nagano
Summary: This paper investigates the relationships among integration, uncertainty, IQ and performance in the context of the production planning and control function, showing that integration positively affects planning performance, mediated by IQ and moderated by uncertainty.
REGE-REVISTA DE GESTAO
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
