In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a par...
Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDEs). This...
Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rhe...
Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepO...
Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and...
Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identificatio...
High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environ...
This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of po...
Scientific machine learning (SciML) offers an emerging alternative to the traditional modeling approaches for wave propagation. These physics-based models rely on computationally demanding numerical techniques. However, SciML extends artificial neura...
Mean-field games (MFGs) are developed to model the decision-making processes of a large number of interacting agents in multi-agent systems. This paper studies mean-field games on graphs (
Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potent...
The accurate simulation of the dynamics of the anaerobic–anoxic–oxic (A2O) process in the biochemical reactions in wastewater treatment plants (WWTPs) is important for system prediction and optimization. Previous studies have used real-ti...
During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to...
A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize the physical phenomenon of rime-ice growth. It simulates the hard-rime and soft-rime processes, constructing the mechanisms of hard-rime pun...
Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two bas...
This paper presents a voltage controller for an alternating current microgrid, where the nonlinear optimization problem of voltage regulation is transformed into a linear one by employing a linear predictor based on an online extended dynamic mode de...
As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the...
This research-to-practice article delves into novel learning experiences for operations management education, involving the circular economy and experiential learning. Higher Education academics are required to develop effective learning that activel...
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Exi...
The conditions for the safe and stable operation of a large power grid are highly interdependent and difficult to predict. In order to accurately understand the operational state of a large power grid, an efficient assessment model for its safe opera...
The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, th...
We present a novel approach to reinforcement learning (RL) specifically designed for fail-operational systems in critical safety applications. Our technique incorporates disentangled skill variables, significantly enhancing the resilience of conventi...
Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the o...
Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industri...
Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies de...
Improving the operating income of farmers and herdsmen is an important starting point for achieving common prosperity. As a common form of learning and an important source of skills training, informal learning has a certain impact on the economy and...
To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. The full-process...
The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a no...
One of the promises of AI in the military domain that seems to guarantee its adoption is its broad applicability. In a military context, the potential for AI is present in all operational domains (i.e., land, sea, air, space, and cyber-space) and all...
Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where mo...
Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication...
Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluat...
The operating room is a challenging learning environment for many students. Preparedness for practice is important as perceived stress and the fear of making mistakes are known to hamper learning. The aim was to evaluate students’ perspectives of an...
At present, substation operation ticket system is developed based on an expert system, which has some problems such as knowledge base redundancy, intelligence deficiency and automatic learning ability. To solve this problem, this paper proposes an op...
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis whe...
In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for s...
Background: E-learning is an important adjunct used for teaching clinical skills in medicine dentistry. This study evaluated and compared the effectiveness of e-learning resources as an additional teaching aid to traditional teaching methods in male...
The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiologi...
This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating...
Constraints imposed by the shrinking resources and the climate change dynamics necessitate a behavioral change to increase knowledge exchange and optimize resource utilization. Existing entrepreneurship and innovation practices are therefore undergoi...
Stochastic dynamic programming (SDP) is a widely-used method for reservoir operations optimization under uncertainty but suffers from the dual curses of dimensionality and modeling. Reinforcement learning (RL), a simulation-based stochastic optimizat...
Reinforcement learning (RL) has achieved remarkable success in complex decision-making tasks; however, its application to structured combinatorial optimization problems in operations research (OR) and smart manufacturing remains challenging due to hi...
The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of class...
The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with r...
With the continuous expansion of power grids and the gradual increase in operational uncertainty, it is progressively challenging to meet the capacity requirements for power grid development based on manual experience. In order to further improve the...
This study aims to develop a controller to operate an energy system-consisting of a photovoltaic thermal (PVT) system combined with a heat pump, using the reinforcement learning approach to minimize the operating costs of the system. For this, the fl...
Automation in the industry can improve production efficiency and human safety when performing complex and hazardous tasks. This paper presented an intelligent cyber-physical system framework incorporating image processing and deep-learning techniques...
The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on ste...
Establishing accurate models for central air-conditioning systems is an indispensable part of energy-saving optimization research. This paper focuses on large commercial buildings and conducts research on improving the energy efficiency model of chil...
Large lifting equipment is used regularly in the maintenance operations of chemical plant installations, where safety controls must be carried out to ensure the safety of lifting operations. This paper presents a convolutional neural network (CNN) me...
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