Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products

This paper analyzes the model of the recycled products which are considered with the minimum quality level in manufacturing/remanufacturing system. In this model, a constant demand is satisfied by manufacturing raw materials and remanufacturing recycled products which are up to the quality level. It is assumed that functions of recycling rate, buyback cost and remanufacturing cost are depend on the minimum quality level. The quality level of recycled products is set to be exponential distribution and then the model is established. The results show that when the buyback cost is low (the quality of the recycled products is low), the average total cost is low, though the remanufacturing cost is high. Namely, the companies are willing to recycle the used products with low quality level. Meanwhile, the optimal strategy of recycling, manufacturing and remanufacturing is investigated here. Through construction of a solution process on Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and numerical examples with sensitive analysis, the validity of the model has been proved.     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

Generating synthetic benchmark circuits for accelerated life testing of field programmable gate arrays using genetic algorithm and particle swarm optimization

Accelerated life testing (ALT) of a field programmable gate array (FPGA) requires it to be configured with a circuit that satisfies multiple criteria. Hand-crafting such a circuit is a herculean task as many components of the criteria are orthogonal to each other demanding a complex multivariate optimization. This paper presents an evolutionary algorithm aided by particle swarm optimization methodology to generate synthetic benchmark circuits (SBC) that can be used for ALT of FPGAs. The proposed algorithm was used to generate a SBC for ALT of a commercial FPGA. The generated SBC when compared with a hand-crafted one, demonstrated to be more suitable for ALT, measured in terms of meeting the multiple criteria. The SBC generated by the proposed technique utilizes 8.37% more resources; operates at a maximum frequency which is 40% higher; and has 7.75% higher switching activity than the hand-crafted one reported in the literature. The hand-crafted circuit is very specific to the particular device of that family of FPGAs, whereas the proposed algorithm is device-independent. In addition, it took several man months to hand-craft the SBC, whereas the proposed algorithm took less than half-a-day.     

部分传输序列的遗传模拟退火搜索方法

在正交频分复用(OFDM)系统中,基于遗传算法的部分传输序列(GA-PTS)技术有效地降低了 PTS 的计算复杂度,但在 改进峰值-平均功率比(PAPR)性能方面却并不理想。 为此,提出在遗传算法中嵌入模拟退火(SA)算子从而构造一种混合的遗 传模拟退火(GSA)算法,并把它应用于对 PTS 的最优相位因子进行搜索。 首先,通过对 PTS 相位因子编码形成染色体,采用随 机元素组成的染色体作为遗传算法的初始群体,并评估每个染色体的适应度值。 然后,根据适应度值选择染色体,建立染色体 的变异规则和交叉规则,对群体进行迭代进化。 最后,群体中的染色体利用退火温度进行更新,从而产生出新的下一代种群。 仿真结果说明,与 GA-PTS 方案相比,该方法不仅能降低计算负担,而且能够有效地降低 OFDM 系统 PAPR 值。     

电梯智能群控系统研究概况

电梯是高楼大厦中不可缺少的垂直运输工具。为了提高电梯服务的性能,采用优质的电梯群控系统统一管理电梯运行是一个值得研究的问题。本文介绍了电梯群控系统的起源和现状,着重对智能群控技术发展进行了概括和分析。对部分电梯公司的群控技术应用情况进行了论述。最后对群控技术的发展进行了展望。     

Performance studies of R433A in a direct expansion solar-assisted heat pump

ABSTRACT

Energy assessment of a simple direct expansion solar-assisted heat pump system has been experimentally assessed with R433A as a possible alternative to R22. An artificial neural network integrated genetic algorithm model was developed to assess the performance system. The data obtained from the experimentation at different ambient conditions are used as the training data for the ANN network. The back propagation learning mechanism with variants Lavenberg-Maguardt with 20 neurons in the hidden layer were used in modelling of ANN. The values obtained from the analysis using ANN are optimised further by integrating the ANN procedure with GA. The results indicated that R433A has 6.4% and 1% lower instantaneous compressor power consumption and heating capacity compared to R22. Energy performance ratio of R433A was found to be about 5.67% higher compared to R22. The results confirmed that R433A can be used as a possible alternative to R22 DX-SAHP systems.     

Optimization of indoor climate conditioning with passive and active methods using GA and CFD

This study aims at the development of an optimal design tool using a genetic algorithm (GA) and computational fluid dynamics (CFD). Random variables (fluctuating outdoor conditions), passive design elements (model variables) and active design elements (HVAC system) were set up to represent a realistic building environment. A combination of designs is determined based on the relationship between fluctuating outdoor conditions and the HVAC system in the optimal design search. Building environment designs should consider both active and passive design elements because the HVAC system keeps adjusting the supply air flow rate until the indoor climate reaches target conditions when outdoor conditions are changing.     

A New Chaotic Genetic Hybrid Algorithm and Its Applications in Mechanical Optimization Design

A new chaotic genetic hybrid algorithm(CGHA) based on float point coding was put forward in this paper.Firstly,it used chaos optimization to search coarsely and produced a better initial population.Then,a power function carrier was adopted to improve the ergodicity and the sufficiency of the chaos optimization.Secondly,the genetic algorithm(GA) was used to search finely and guaranteed the population's evolution.To avoid the search being trapped in local minimum,a chaos degenerate mutation operator was desig...