Article
Computer Science, Artificial Intelligence
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, Tinne Tuytelaars
Summary: This article introduces the application of artificial neural networks in continual learning, focusing on task incremental classification. It proposes a new framework for continually evaluating the stability-plasticity trade-off of the network and performs experimental comparisons of 11 state-of-the-art continual learning methods, evaluating their strengths and weaknesses by considering different benchmark datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Psychology, Multidisciplinary
Oana C. Fodor, Petru L. Curseu, Nicoleta Meslec
Summary: MTM is a form of work organization that flexibly deploys human resources across multiple simultaneous projects, where individual members bring in their cognitive resources and use the resources developed while working together. The study supports the group-to-individual transfer of learning and suggests that individual estimates improve only in groups with low or average collective estimation errors.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Management
Patricia Heuser, Bjorn Tauer
Summary: In today's dynamic work environment, employee skills and the effects of learning and forgetting have a significant impact on production efficiency. This study presents a new learning and forgetting effect model for single-machine scheduling, considering different product categories and optimizing processing times.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Software Engineering
Antonio Mastropaolo, Nathan Cooper, David Nader Palacio, Simone Scalabrino, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota
Summary: This paper evaluates the performance of the T5 model in supporting four different code-related tasks and studies the impact of pre-training and multi-task fine-tuning. The results show that the T5 model outperforms state-of-the-art baselines and that not all tasks benefit from multi-task fine-tuning despite the advantages of pre-training.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Neurosciences
Robert T. Moore, Tyler Cluff
Summary: Individual differences in sensorimotor adaptation were found to be reliable across different stages of exposure to the same force field, with individuals who adapted more initially also adapting more upon re-exposure. However, correlations were weaker between adaptation to different force fields and visuomotor rotations, suggesting unique mechanisms at play during these different adaptation processes.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
C. Goelz, K. Mora, J. Rudisch, R. Gaidai, E. Reuter, B. Godde, C. Reinsberger, C. Voelcker-Rehage, S. Vieluf
Summary: The study reveals changes in brain network function in older adults, resulting in lower classification performance in body side but better performance in task characteristics, suggesting a higher sensitivity of brain networks to task difficulty in the elderly. These findings contribute to understanding age-specific characteristics of brain activity patterns, which have relevance in applications such as brain-computer interfaces.
Article
Psychology, Multidisciplinary
Weiwei Zhang, Meijuan Zhao, Ye Zhu
Summary: This study investigates individual differences in the use of metacognitive strategies among Chinese EFL learners and examines its relationship with task demand and learner performance. The results show that Chinese EFL learners display variance in metacognitive strategy use, with problem-solving being the most frequently used strategy and monitoring being the least frequent. Additionally, metacognitive strategies interactively respond to task demands, but their use in individual and interactive modes has no relationship with speaking performance.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Harvinder Singh, Sanjay Tyagi, Pardeep Kumar, Sukhpal Singh Gill, Rajkumar Buyya
Summary: This paper discusses various nature-inspired metaheuristic algorithms for scheduling tasks in cloud computing environments and identifies Crow Search Algorithm as the most optimal technique in terms of efficiency and cost through comparative analysis of six algorithms.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Computer Science, Information Systems
Hao Yuan, Guoming Tang, Xinyi Li, Deke Guo, Lailong Luo, Xueshan Luo
Summary: The emergence of edge computing addresses transmission delays, but challenges remain in task dispatching and scheduling. This article proposes an online method OTDS that dynamically assigns tasks to optimal edge servers using OL and DRL techniques, achieving efficient and fair task scheduling by allocating appropriate resources to tasks based on their time sensitivity.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Neurosciences
Shachar Gal, Niv Tik, Michal Bernstein-Eliav, Ido Tavor
Summary: Relating individual differences in cognitive traits to brain functional organization is a challenge for neuroscience. A study found that task-induced brain activation maps better predict individual intelligence than resting-state connectivity patterns, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. The study used data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI and further predict individual differences in various traits.
Article
Engineering, Industrial
Cesar Augusto Henao, Yessica Andrea Mercado, Virginia I. Gonzalez, Armin Luer-Villagra
Summary: Multiskilling is a workforce flexibility strategy that involves training workers to perform multiple tasks effectively. This study evaluates the potential benefits of multiskilled workers using a k-chaining policy with k >= 2, considering the learning/forgetting phenomena and workforce heterogeneity. The results show that modeling the workforce as homogeneous underestimates the level of multiskilling required to minimize understaffing, while incorporating heterogeneous workforce modeling suggests the need for more multiskilling to compensate for lower workers' productivity.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Behavioral Sciences
Pamela M. Prentice, Chloe Mnatzaganian, Thomas M. Houslay, Alex Thornton, Alastair J. Wilson
Summary: Cognition is crucial for survival and reproduction, and it varies between species and individuals. This study on male Poecilia reticulata found differences in spatial learning ability and stress response behavior among individuals. However, the cumulative effects of experience and chronic stress may impact cognitive performance.
Article
Psychology, Mathematical
Lauren L. Richmond, Lois K. Burnett, Alexandra B. Morrison, B. Hunter Ball
Summary: Individual differences in working memory capacity (WMC) are related to performance in domains outside of WM. Researchers found that processing performance is associated with WMC in complex span WM tasks. Including processing task performance measures may provide a better understanding of the relationships between complex span task performance and performance in disparate domains of cognition.
BEHAVIOR RESEARCH METHODS
(2022)
Article
Computer Science, Theory & Methods
Zhenqian Chen, Xinkui Zhao, Chen Zhi, Jianwei Yin
Summary: DeepBoot solves the challenges by utilizing idle GPUs in the inference cluster for the training DLTs. Specifically, it designs an adaptive task scaling (ATS) algorithm to allocate GPUs in the training and inference clusters for training DLTs and minimize the performance loss when reclaiming inference GPUs. Results show that DeepBoot achieves significant average JCT reduction compared with the scheduler without utilizing idle GPUs in the inference cluster.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Huirong Ma, Rui Li, Xiaoxi Zhang, Zhi Zhou, Xu Chen
Summary: Mobile-edge computing is a promising technique for IoT devices with limited resources to access AI capabilities. We propose a reliability-aware online scheduling scheme that utilizes online feedback and offline data to learn the uncertain availability of edge servers, maximizing both inference accuracy and service reliability of DNN inference tasks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Industrial
Hiroshi Matsuhisa, Nobuo Matsubayashi
Summary: This study investigates the formation of an alliance between competing manufacturers and a monopolistic platform retailer, and analyzes the impact of the degree of differentiation among manufacturers on the formation of the alliance and the profitability of the retailer.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Lingxuan Kong, Ge Zheng, Alexandra Brintrup
Summary: Supply Chain Financing is used to optimize cash flows in supply networks, but recent scandals have shown inefficiencies in risk evaluation. This paper proposes a Federated Learning framework to address order-level risk evaluation.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Jing Gu, Xinyu Shi, Junyao Wang, Xun Xu
Summary: The asymmetric market power between a firm and its partners negatively affects the firm's financial performance. Building relationships with suppliers or customers that have matched market power is the best approach. The strength of the buyer-supplier relationship amplifies the negative impact of asymmetric market power, while the level of relationship embeddedness reduces its negative effect. Moreover, firm-specific institutional, industry, and regional economic heterogeneities also influence the financial impact of asymmetric market power.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yu Du, Jun-qing Li
Summary: This study investigates the group scheduling of a distributed flexible job shop problem using the concrete precast process. The proposed solution utilizes three coordinated double deep Q-networks (DQN) as a learn-to-improve reinforcement learning approach. The algorithm shows superiority in minimizing costs and energy consumption.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Xiaoyu Yan, Weihua Liu, Ou Tang, Jiahe Hou
Summary: This study analyzes the market amplification effect and the impact of entrant's overconfidence on a two-sided platform. The results show that overconfident entrants can lead to price increases and benefit both the existing firms and themselves to a certain extent.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Illya Kaynov, Marijn van Knippenberg, Vlado Menkovski, Albert van Breemen, Willem van Jaarsveld
Summary: The One-Warehouse Multi-Retailer (OWMR) system is a typical distribution and inventory system. Previous research has focused on heuristic reordering and allocation strategies, which are time-consuming and problem-specific. This paper proposes a Deep Reinforcement Learning (DRL) algorithm for OWMR problems, which infers a multi-discrete action distribution and improves performance with a random rationing policy.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yimeng Sun, Ruozhen Qiu, Minghe Sun
Summary: This study considers a multi-period inventory management problem for a retailer offering limited-time discounts and having a joint service-level requirement under demand uncertainty. It proposes a double-layer iterative approach to solve the problem and maximize total profit while balancing the service level using a posteriori method and an affinely adjustable robust chance-constrained model.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Anas Neumann, Adnene Hajji, Monia Rekik, Robert Pellerin
Summary: This paper presents a new mathematical formulation for planning and scheduling activities of Engineer-To-Order (ETO) projects, along with a new ETO strategy to reduce the impacts of design uncertainty. The study proposes a hybrid Layered Genetic Algorithm combined with an adaptive Lamarckian learning process (LLGA) and compares it with the branch-and-cut procedure of CPLEX. The results show good performance of the proposed mathematical model for small and medium-sized instances.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Thilini Ranasinghe, Chanaka D. Senanayake, Eric H. Grosse
Summary: Production systems are undergoing transformative changes, necessitating adaptability from human workers. This study developed an analytical model to account for stochastic processing times and learning heterogeneity, revealing insights into system performance.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Sunil Tiwari, Pankaj Sharma, Ashish Kumar Jha
Summary: Black Swan events such as the COVID-19 pandemic and the Suez Canal blockage have a significant impact on firms' technology adoption decisions, especially in terms of disruptions and digitalization in the supply chains. This study investigates the influence of institutional forces and environmental contingencies on supply chain digitalization from an institutional and contingency theory perspective. The findings emphasize the importance of organizational readiness and people readiness, including top management involvement and employee training, in facilitating digitalization.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Fabio Neves-Moreira, Pedro Amorim
Summary: Omnichannel retailers are using stores as distribution centers to provide faster online order fulfillment services. However, in-store picking operations can impact the offline customer experience. To address this, we propose a Dynamic In-store Picker Routing Problem (diPRP) that minimizes customer encounters while fulfilling online orders. Our solution approach combines mathematical programming and reinforcement learning to find efficient picking policies that reduce customer encounters.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Richard Kraude, Ram Narasimhan
Summary: In this study, the relationship between Vertical Integration (VI) and Environmental Performance (EP) is examined, revealing that highly integrated firms produce less waste but engage in fewer environmental initiatives. These findings are crucial for understanding the impact of stakeholder exposure on organizational behavior.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Review
Engineering, Industrial
Korina Katsaliaki, Sameer Kumar, Vasilis Loulos
Summary: This research conducts a systematic literature review (SLR) and content analysis on Supply Chain Coopetition (SCC) through the PRISMA framework. It examines the theory of coopetition and organizational relationships in intra-firm and inter-firm supply chains, focusing on collaboration between rival manufacturers. The study identifies structures and mechanisms of coopetition, such as buyer-supplier coopetition, supply networks coopetition, and production and distribution/logistics coopetition. It provides a holistic approach to SCC management practices and serves as a guide for future research.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)