•Analyses 72 manufacturing and trade companies from Baltic states and Finland.•Data is annual from years 2010–2018.•Proposes staged DEA efficiency models for the role of inventory and assets.•Inventory and assets seem to have causal relationship with shareholder value.•Relationship is either indirect, through financial outputs, or direct.
As a typical emergent swarm intelligence algorithm, Moth-Flame Optimization (MFO) has been created to deal with global optimization problems. Since the introduction, it has been applied to various optimization problems. However, MFO may have the trouble of getting into the local best, and the convergence rate cannot be satisfying when handling the high-dimensional and some multimodal problems. In this work, an enhanced MFO integrated with orthogonal learning (OL) and Broyden-Fletcher-Goldfarb-Shanno (BFGS), which we called BFGSOLMFO, is proposed to alleviate the stagnation shortcomings and accelerate the performance of well-regarded MFO. In the BFGSOLMFO, OL is used to construct a better candidate solution for each moth and then guide the whole population to a reasonable potential area. Meanwhile, in each iteration, after the evolution of population finished and the global optima are obtainable, BFGS is employed to further excavate the potential of the global best moth in the current population. With the aim of evaluating the efficacy of the BFGSOLMFO, first of all, the IEEE CEC2014 benchmark set is utilized to measure the performance in solving function optimizations with high-dimensional and multimodal characteristics. Both sets of the IEEE CEC2011 real-world benchmark problems and the three constrained engineering optimization problems are adopted to estimate the performance of BFGSOLMFO in tackling practical scenarios. In all the experiments, the developed BFGSOLMFO is compared with state-of-the-art advanced algorithms. Experimental results and statistical tests demonstrate that the proposed method outperforms the basic MFO and a comprehensive set of advanced algorithms. (C) 2020 Elsevier Ltd. All rights reserved.
•Reviewing advanced machine learning methods for an important medical application.•Summarizing notable deep learning-based methods for detecting heart arrhythmia.•Categorizing widely accepted datasets and evaluation metrics within the community.•Reviewing mostly considered heart arrhythmias published in the recent years.•Analyzing the advanced methods and comparing them based on their performance.
In the manufacturing industry, the preventive maintenance (PM) is a common practice to reduce random machine failures by replacing/repairing the aged machines or parts. The decision on when and where the preventive maintenance needs to be carried out is nontrivial due to the complex and stochastic nature of a serial production line with intermediate buffers. In order to improve the cost efficiency of the serial production lines, a deep reinforcement learning based approach is proposed to obtain PM policy. A novel modeling method for the serial production line is adopted during the learning process. A reward function is proposed based on the system production loss evaluation. The algorithm based on the Double Deep Q-Network is applied to learn the PM policy. Using the simulation study, the learning algorithm is proved effective in delivering PM policy that leads to an increased throughput and reduced cost. Interestingly, the learned policy is found to frequently conduct "group maintenance" and "opportunistic maintenance", although their concepts and rules are not provided during the learning process. This finding further demonstrates that the problem formulation, the proposed algorithm and the reward function setting in this paper are effective. (c) 2020 Elsevier Ltd. All rights reserved.
In this paper, we suggest new feature extraction models based on the stock market price signal analysis. In particular, we study the behavior observed in signals originating from different sources, such as prices of different Limit Order Book levels and open, close, low, high prices of the preselected time intervals. We apply Fourier transformation to extract new features. Moreover, we evaluate if the performance of the model based on the Gated Recurrent Unit (GRU) architecture is improved when we select features utilizing the proposed methods. Furthermore, we benchmark the performance of new indicators on the GRU model and provide quantified results proving the significant performance improvement obtained by incorporating the suggested features. (C) 2020 The Author(s). Published by Elsevier Ltd.
