The concepts associated with sustainability have been gradually incorporated into business models in recent years. Particularly in the field of Operations Management (OM), the adoption of new performance indicators an...
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The concepts associated with sustainability have been gradually incorporated into business models in recent years. Particularly in the field of Operations Management (OM), the adoption of new performance indicators and the revision of operations strategy content and process frameworks are changing the way companies manage their operations regarding sustainability, creating the right conditions for new operations management model development. Such models were created in order to assess the impact and to characterize the progress of new initiatives in sustainability in the economic, environmental and social spheres (Triple Bottom Line-TBL). This paper, based on academic literature and professional models, proposes a framework for verifying the level of maturity of sustainable operations. Its distinguishing aspect contemplates the integrated approach over TBL along five proposed maturity levels, followed by descriptions of context, structure, processes, value chain and functional recommendations on implementation. The main objective of the Sustainable Operations Maturity Model (SOMM) is to be applied as a managerial tool to support organizations when evaluating the integration of sustainability into their strategies, and especially to provide guidelines for the evolution of sustainable operation processes.
In the field of content analysis, there are several methods that aim to compare texts and analyze them to determine whether they are equivalent. Within the content analysis, we found techniques used to compare and cor...
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In the field of content analysis, there are several methods that aim to compare texts and analyze them to determine whether they are equivalent. Within the content analysis, we found techniques used to compare and correlate texts. Some of them are present in computational tools such as the Sphinx and Atlati. In General, these tools are closed solutions of difficult integration with other systems. In this context, it is not possible to set up an automated process including these tools. This difficulty became evident during operation activities of a sustainability indicators formulation model proposed by Machado et al. 2012. In this paper, the aim is to develop a content correlation methodology and make it operational in the form of an automated process of similar correlation texts and or equivalents. The research was driven by a methodological organization process. This process is the combination of methodological tools: action research, BPM Cycle-Business Process Management, Project Management and Process Approach. The correlation process developed by this research makes it possible to correlate texts that are grouped so that assignments are made that determine a degree of correlation between texts. It is known that with this model it is not possible to determine which texts are equivalent between themselves exactly, however, there is a qualifying list of correlation between the group texts.
Differential evolution (DE) is an evolutionary algorithm (EA) that uses a rather greedy and less stochastic approach to solve optimization problems than other evolutionary methods [1]. Like other EAs, DE is a populati...
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ISBN:
(纸本)9781479904532
Differential evolution (DE) is an evolutionary algorithm (EA) that uses a rather greedy and less stochastic approach to solve optimization problems than other evolutionary methods [1]. Like other EAs, DE is a population-based, stochastic global optimizer, capable of working reliably in nonlinear and multimodal environments. Due to several features such as simplicity, efficiency and global search capabilities, DE rapidly became a successful paradigm of evolutionary computation. However, to achieve adequate performance with DE, the process of tuning the control parameters is essential as its performance is sensitive to the choice of both mutation and crossover settings. This paper proposes a DE algorithm with adaptive tuning of scaling factor (F), crossover rate (CR) and quasi-oppositional probability based on population's variance information - Adaptive Differential Evolution (ADE). Furthermore, ADE adopts a vector called Fm in each dimension of the optimization problem instead of single variable for F as presented in the classical DE approach. The proposed optimization method is validated on the test-bed proposed for the IEEE CEC'13 (IEEE Congress on Evolutionary Computation 2013) contest for real parameter single objective optimization with 28 benchmark functions. Simulation results over the benchmark functions demonstrate the effectiveness and usefulness of the proposed ADE method. This version of paper includes the ADE's performance on the 10, 30 and 50-dimensional benchmark functions.
作者：
John Paul FarmerRyan PanchadsaramDirector of technology and civic innovation at Microsoft and founder/CEO of The Innovation Project
a 501(c)3 dedicated to spreading best practices in institutional innovation. He served as senior advisor for innovation in the White House where he spearheaded government innovation initiatives that infused lean startup and agile methods as well as design thinking into the public sector. In this role Farmer co‐founded and directed the Presidential Innovation Fellows program. Earlier in his career he worked in healthcare and finance creating and building new business units from the ground up. Farmer also played professional baseball as a shortstop in the Los Angeles Dodgers and Atlanta Braves minor league systems. He holds an MBA with honors from the Graduate School of Business at Columbia University and an AB with honors from Harvard University. Currently an entrepreneur in residence at venture capital firm Kleiner Perkins Caufield Byers. Formerly
he was the deputy chief technology officer for the United States at the White House. Panchadsaram helped shape how an $80 billion budget can be used by federal agencies to deliver on their missions in a more effective design‐centric and data‐driven way. In 2014 Time magazine featured him on its cover as part of the crisis response team tasked with rescuing the rollout of Healthcare.gov. In response to that experience he helped found the US Digital Service—a “startup” within the White House that recruits the country's top technical talent to work on the government's most critical citizen‐facing services. Panchadsaram is an active designer and coder and held various product roles at Microsoft and ***. He holds a degree from the University of California Berkeley in industrial engineering and operations research.
Design is making inroads into the workings of government agencies within the DC Beltway and beyond. Civic‐minded designers take note. An overview of the design/government ecosystem in Washington, DC.
Design is making inroads into the workings of government agencies within the DC Beltway and beyond. Civic‐minded designers take note. An overview of the design/government ecosystem in Washington, DC.
In this paper we propose a methodology to classify Power Quality for feeders, based on sags and by the use of KDD technique, establishing a quality level printed in labels. To support the methodology, it was applied t...
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ISBN:
(纸本)9789898565778
In this paper we propose a methodology to classify Power Quality for feeders, based on sags and by the use of KDD technique, establishing a quality level printed in labels. To support the methodology, it was applied to feeders on a substation located in Curitiba, Paraná, Brazil, based on attributes such as sag length, duration and frequency (number of occurrences on a given period of time). In the search for feeders quality classification, on the Data Mining stage, the main stage on KDD process, three different techniques were used in a comparatively way for pattern recognition: Artificial Neural Networks, Support Vector Machines an Genetic Algorithms. Those techniques presented acceptable results in classification feeders with no possible classification using a simplified method based on maximum number of sags. Thus, by printing the label with information and Quality level, utilities companies can get better organized for mitigation procedures, by establishing clear targets.
The combination of wavelet theory and feedforward artificial neural networks has resulted in wavelet neural networks or wavenets (WNNs). In these networks, the activation functions are described by discrete wavelet fu...
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ISBN:
(纸本)9781467322331
The combination of wavelet theory and feedforward artificial neural networks has resulted in wavelet neural networks or wavenets (WNNs). In these networks, the activation functions are described by discrete wavelet functions. Due to the promising properties of time-frequency localization and multi-resolution signal processing of the wavelet transform combined with the approximation capability of artificial neural networks, WNNs have found applications in dynamic system identification field during the past years. The paper aims at the development of the WNN based on traditional firefly algorithm (FA). The proposed FA is based on Tinkerbell map to tune the spread of wavelets and number of selected wavelet bases. The FA is a stochastic metaheuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. The efficacy of WNN with FA tuning is tested on the identification of a multivariable thermal process.
High level sports require a steady intensification of training in order to raise the athletes' performance. With the purpose of support swimmers and coaches new biomechanical analysis are been performed, becoming ...
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ISBN:
(纸本)9781479900466
High level sports require a steady intensification of training in order to raise the athletes' performance. With the purpose of support swimmers and coaches new biomechanical analysis are been performed, becoming one of the most studied areas in swimming. By using technology resources, significant results related to performance improvements are being achieved. Specific analysis of the movements, strength, velocity and projection allow identifying relevant points that directly impact athletes' results. In this context, this work uses a Radial Basis Function Neural Network (RBF-NN) with training combining the Gustafson-Kessel clustering method and the proposed Modified Differential Evolution (MDE) in order to perform the swimmer velocity profile identification. The main idea is to obtain the dynamic of the velocity profile and to use it to improve the athletes' swim style. Differential Evolution (DE) is an evolutionary algorithm that uses a rather greedy and less stochastic approach to solve problems when compared to other evolutionary methods [1]. However, to achieve good performance with DE, the tuning of control parameters is essential as its performance is sensitive to the choice of the mutation and crossover settings. On the other hand, the RBF-NN is a powerful approach for nonlinear identification. This paper combines the two strategies described above proposing a modified DE algorithm based on the association of a sinusoidal signal and chaotic sequences generated by logistic map for the mutation factor tuning. By using data collected from breaststroke and crawl swim style of an elite female swimmer, the validity and the accuracy of the RBF-NN model have been tested by simulations. Results reveal that it is feasible to establish a good model to represent data experimental related to swimming field. Identification results show that MDE outperforms both other tested classical DE approaches for training RBF-NNs in terms of solution quality.
An artificial neural network (ANN) consists of a number of interconnecting artificial neurons and employs mathematical or computational models for information processing. ANNs are suitable for handling large amounts o...
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An artificial neural network (ANN) consists of a number of interconnecting artificial neurons and employs mathematical or computational models for information processing. ANNs are suitable for handling large amounts of dynamic, noisy and nonlinear data. On the other hand, the wavelet theory provides a multi-resolution approximation for discriminate functions. The combination of the wavelet transforms theory with the basic concept of ANNs leads to new mapping networks called wavelet neural networks (WNNs) or wavenets, which are proposed as an alternative to feedforward ANNs for approximating arbitrary nonlinear functions. Generalized from radial basis function ANNs, WNNs are in fact feed-forward neural networks with one hidden layer, radial wavelets as activation functions in the hidden nodes and a linear output layer. The contribution of this paper is to evaluate the WNNs to model a parathlete swimmer behavior. The parathlete swimmer swims the breaststroke style using biomechanics data generated by the software tool called SWUMSUIT, which was developed in Tokyo Technological Institute in Japan. The forecasted results clearly show that WNN has good prediction properties. The proposed WNN modeling approach can benefit disabled swimmers (parathletes) to gain competitive advantage by studying the biomechanics involved in the sport and considering the help of simulations systems.
The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times s...
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ISBN:
(纸本)9781467363822
The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications.
In this paper we address a minimum latency problem and propose a multi-start strategy for solving it. For constructing initial solutions two procedures are designed, which are based on metaheuristic GRASP. We also dev...
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