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Swarm intelligence-based framework for accelerated and optimized assembly line design in the automotive industry
This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines...
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A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures
We study large-scale portfolio optimization problems in which the aim is to maximize a multi-moment performance measure extending the Sharpe ratio....
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Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning
In the era of Industry 4.0, production scheduling as a critical part of manufacturing system should be smarter. Smart scheduling agent is required to...
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Machine learning for coverage optimization in wireless sensor networks: a comprehensive review
In the context of wireless sensor networks (WSNs), the utilization of artificial intelligence (AI)-based solutions and systems is on the ascent....
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Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm
Computer resources provision over the internet resulted in the wide spread usage of cloud computing paradigm. With the use of such resources come...
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Designing an adaptive and deep learning based control framework for modular production systems
In today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s...
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Learning Optimal Solutions via an LSTM-Optimization Framework
In this study, we present a deep learning-optimization framework to tackle dynamic mixed-integer programs. Specifically, we develop a bidirectional...
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Real Estate Market Prediction Using Deep Learning Models
Real estate significantly contributes to the broader stock market and garners substantial attention from individual households to the overall...
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Collaborative optimization of manufacturing service allocation via multi-task transfer learning evolutionary approach
Industrial internet platforms are regarded as an emerging fashion for the flexible integration of production resources located in multiple sites to...
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A blending ensemble learning model for crude oil price forecasting
To efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for...
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Incremental Machine Learning-Based Approach for Credit Scoring in the Age of Big Data
The determination of the financial credibility of a loan applicant by financial institutions is quantified using a credit score. Sources of credit,...
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Synthetic reality map** of real estate using deep learning-based object recognition algorithms
Artificial intelligence (AI), encompassing machine learning and deep learning (DL), has penetrated the real estate domain. This research investigated...
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Applying machine learning to wire arc additive manufacturing: a systematic data-driven literature review
Due to its unique benefits over standard conventional “subtractive” manufacturing, additive manufacturing is attracting growing interest in academic...
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Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review
While electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) have the potential solution from an environmental perspective, they face an...
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A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach
Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The...
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization...
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Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time
In this paper, a novel stacked encoded cascade error feedback deep extreme learning machine (SEC-E-DELM) network is proposed to predict order...
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A digital twin-assisted deep transfer learning method towards intelligent thermal error modeling of electric spindles
Thermal error modeling (TEM) is essential for preserving machining accuracy and enhancing the reliability of electric spindle systems. However, the...
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Strategic bidding in freight transport using deep reinforcement learning
This paper presents a multi-agent reinforcement learning algorithm to represent strategic bidding behavior by carriers and shippers in freight...
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A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups
As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints...
