Results 71 to 80 of about 51,613 (264)
Heuristic Algorithms for Assigning Hispanic Ethnicity
PLoS ONE, 2013 We compared several techniques for assigning Hispanic ethnicity to records in data systems where this information may be missing, variously making use of country of origin, surname, race, and county of residence. We considered an algorithm in use by the North American Association of Central Cancer Registries (NAACCR), a variation of this developed by ...
Francis P Boscoe +3 more
openaire +4 more sources
Advanced Electronic Materials, EarlyView.Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley +1 more source
A multiscale Bayesian optimization framework for process and material codesign
AIChE Journal, EarlyView.Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley +1 more source
Study on Near-Optimal Path Finding Strategies in a Road Network
Journal of Algorithms & Computational Technology, 2008 In this paper we analyze the existing defects of hierarchical and nonhierarchical path finding algorithm respectively in detail and present a hierarchical path finding algorithm based on the heuristic directing search approach which is used for promoting
Shi Jun +3 more
doaj +1 more source
Ain Shams Engineering Journal, 2018 This paper presents a comparison between four modern heuristic algorithms for optimal loss reduction of power distribution network equipped with renewable energy resources.
Amr M. Ibrahim, Rania A. Swief
doaj +1 more source
Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization
AIChE Journal, EarlyView.Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed +4 more
wiley +1 more source
A trust‐region funnel algorithm for gray‐box optimization
AIChE Journal, EarlyView.Abstract Gray‐box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black‐box models lacking analytic derivatives, remains a challenge. Trust‐region (TR) methods provide a robust framework for gray‐box problems through local reduced models (RMs) for black‐box components, but they are ...
Gul Hameed +4 more
wiley +1 more source
AIChE Journal, EarlyView.Abstract This article demonstrates the integration of in‐line mass spectrometry as a process analytical technology (PAT) tool with model‐based soft sensors in a continuous filtration‐drying carousel system for solid–liquid separation (SLS) of crystal slurries.
Inyoung Hur +3 more
wiley +1 more source
A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems
Journal of Entrepreneurship, Management and Innovation, 2012 The m-machine, n-job, permutation flowshop problem with the total tardiness objective is a common scheduling problem, known to be NP-hard. Branch and bound, the usual approach to finding an optimal solution, experiences difficulty when n exceeds 20. Here,
Chia-Shin Chung +3 more
doaj +1 more source
Graph‐based imitation and reinforcement learning for efficient Benders decomposition
AIChE Journal, EarlyView.Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman +3 more
wiley +1 more source