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A Model of Mass Customization for Engineering Production System Development in Textile and Apparel Industries in Thailand

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Systems Engineering Procedia 2011年 2卷 382-397页

作者： Kornthip Watcharapanyawong Sompong Sirisoponsilp Peraphon Sophatsathit Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok 10330 Thailand Department of Civil Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

This paper presents a framework, aiming to develop the mass customization for textile and apparel industries in Thailand. The developed model as presented is the relationship of the connection pertaining to customers’ needs at industrial levels, most of which are Original Design Manufacturer (OEM), a type of textile and apparel business. The outstanding of OEM is the presentation of newly created designs to customers with a variety of products. Importantly, in order to gain such effective production management, an appropriate administrative system with high flexibility, efficient production (Small Lot Size), and punctual delivery is crucially required. In relation to the framework for mass customization development, it concerns three main related involvements: customers, OEM industries, and suppliers, all of which will be assessed so as to be promptly employed in such firms manufacturing products in accordance with the productive systems of mass customization *** conceptual from mass customization systems will facilitate the management in terms of the production designing in factories, aiming at clustering which is complex to the creation of platform from common parts, leading to the development of a variety of goods by the process of product family.

关键词： Mass Customization Clustering Common platform Product family

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Hardware Particle Swarm Optimization with passive congregation for embedded applications

Hardware Particle Swarm Optimization with passive congregati...

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Southern Conference on programmable Logic

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brazil

Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power consumption and portability must be accomplished. This paper proposes a hardware implementation of the Particle Swarm Optimization algorithm with passive congregation (HPPSOpc), which was developed using several floating-point arithmetic libraries. The passive congregation is a biological behavior which allows the swarm to preserve its integrity, balancing between global and local search. The HPPSOpc architecture was implemented on a Virtex5 FPGA device and validated using two multimodal benchmark problems. Synthesis, simulation and execution time results demonstrates that the passive congregation approach is a low cost solution for solving embedded optimization problems with a high performance.

关键词： Hardware computer architecture Software Field programmable gate arrays Benchmark testing Optimization Software algorithms

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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Congress on Evolutionary Computation

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia Brazil

Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfaces and a process of trial and error for creating musical sounds similar to a target sound. This drawback can be overcome by adjusting automatically the synthesizer parameters using optimization algorithms. In this paper a hybrid particle swarm optimization (PSO) algorithm is proposed to solve the frequency modulation (FM) matching synthesis problem. The proposed algorithm takes advantage of a shuffle process for exchanging information between particles and applies the selective passive congregation and the opposition-based learning approaches to preserve swarm diversity. Both approaches for injecting diversity are based on simple operators, preserving the easy implementation philosophy of the particle swarm optimization. The proposed hybrid particle swarm optimization algorithm was validated for a three-nested FM synthesizer, which represents a 6-dimensional multimodal optimization problem with strong epistasis. Simulation results revealed that the proposed algorithm presented promising results in terms of quality of solutions.

关键词： Frequency modulation Optimization Equations Convergence Particle swarm optimization Genetic algorithms

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Collective Intelligence for the design of emergency response

Collective Intelligence for the design of emergency response

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International Conference on computer Supported Cooperative Work in Design

作者： Vivacqua, Adriana S. Borges, Marcos R.S. PPGI/DCC-IM - Graduate Program in Informatics Department of Computer Science Institute of Mathematics UFRJ - Federal University of Rio de Janeiro Rio de Janeiro Brazil PPGI/DCC-IM - Graduate Program in Informatics Department of Computer Science Institute of Mathematics NCE - Electronic Computing Institute UFRJ - Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781424467631

Collective Intelligence has received much attention in recent years, as organizations and businesses discover the power of crowds. With new technologies, such as blogs, twitter, wikis, photo sharing, collaborative tagging and social networking sites, people may create and disseminate content as never before. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Additionally, many open-source initiatives have shown that it is possible to obtain high quality results from collectively produced work. In this paper, we consider the domain of emergency response. We have been working in this domain for a few years now, mostly focusing on small disaster relief groups and field command. One of the command problems is lack of information about the location where the disaster occurred. We believe some of this information could be crowdsourced, changing current practices and enabling more effective response to the situation. In this paper, we discuss the possibilities for crowdsourcing and present an initial proposal for harnessing collective intelligence for the fire department. © 2010 IEEE.

关键词： Emergency services

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AN EQUIVALENCY CONDITION OF NONSINGULARITY IN NONLINEAR SEMIDEFINITE programMING

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Journal of Systems science & Complexity 2010年第4期23卷 822-829页

作者： Chengjin LI Wenyu SUN Raimundo J. B. de SAMPAIO School of Mathematical Sciences Nanjing Normal and Computer Science Fujian Normal University Wenyu SUN University Nanjing 210046 China School of Mathematics Fuzhou 350007 China School of Mathematical Sciences Nanjing Normal University Nanjing 210046 China Pontifical Catholic University of Parana ( PUCPR) Graduate Program in Production and Systems Engineering (PPGEPS) CEP 81611-970 Curitiba Parana Brazil

In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,... 详细信息

关键词： Equivalency condition of nonsingularity nonlinear semidefinite programming semidefinite programming.

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Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications

Hardware particle swarm optimization based on the attractive...

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2010 International Conference on Reconfigurable Computing and FPGAs, ReConFig 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9780769543147

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. However, the PSO algorithm suffers from premature convergence and high elapsed time when solving multimodal and large scale engineering problems. This problem becomes an evident drawback for embedded applications in which the microcontrollers often operates at low computational capacity. This paper proposes a hardware implementation of a parallel self-adaptive PSO algorithm based on an attractive-repulsive scheme and using the efficient floating-point arithmetic which performs computations with large dynamic range and high precision. The parallel capabilities of the PSO are exploited by implementing parallel particles directly in hardware in order to decrease the running time. In addition, the attractive-repulsive technique avoids the premature convergence problem by self-adapting the swarm behavior according to its diversity. Synthesis and simulation results for benchmark test problems were performed, demonstrating the correctness of the proposed architectures. Finally, an elapsed time comparison between the hardware and software implementations shows the suitableness of the proposed architecture for embedded applications. © 2010 IEEE.

关键词： Global optimization

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACG University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide sub-optimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems. © 2010 IEEE.

关键词： Global optimization

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Comparison between two FPGA implementations of the particle swarm optimization algorithm for high-performance embedded applications

Comparison between two FPGA implementations of the particle ...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a high performance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set computer (RISC) microprocessors in which the operational frequencies are low in comparison with the high operational frequencies of traditional personal computers (PCs). This paper compares two hardware implementations of the parallel PSO algorithm using an efficient floating-point arithmetic which perform computations with large dynamic range and high precision. The full-parallel and the partially-parallel PSO architectures allow the parallel capabilities of the PSO to be exploited in order to decrease the running time. Three well-known benchmark test functions have been used to validate the hardware architectures and a comparison in terms of cost in logic area, quality of the solution and performance is reported. In addition, a comparison of the execution time between the hardware and two C-code software implementations, based on a Intel Core Duo at 1.6GHz and a embedded Microblaze microprocessor at 50MHz, are presented. © 2010 IEEE.

关键词： Global optimization

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasilia D.F Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F Brazil

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide suboptimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems.

关键词： mathematics Field programmable gate arrays Table lookup

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Hardware Architecture for Particle Swarm Optimization Using Floating-Point Arithmetic

Hardware Architecture for Particle Swarm Optimization Using ...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Daniel M. Muñoz Arboleda Carlos H. Llanos Leandro dos_S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasília Brazil Industrial and Systems Engineering Graduate Program Pontificial Catholic University of Paraná Curitiba Brazil Departments of Computer Science and Mathematics University of Brasilia Brasília Brazil

High computational cost for solving large engineering optimization problems point out the design of parallel optimization algorithms. Population based optimization algorithms provide parallel capabilities that can be explored by their implementations done directly in hardware. This paper presents a hardware implementation of particle swarm optimization algorithms using an efficient floating-point arithmetic which performs the computations with high precision. All the architectures are parameterizable by bit-width, allowing the designer to choose the suitable format according to the requirements of the optimization problem. Synthesis and simulation results demonstrate that the proposed architecture achieves satisfactory results obtaining a better performance in therms of elapsed time than conventional software implementations.

关键词： Hardware Particle swarm optimization Floating-point arithmetic computer architecture Design optimization Computational efficiency Design engineering Algorithm design and analysis High performance computing Computational modeling

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