Kanban optimization by simulation and evolution

The problem of the optimal design of multistage systems with Kanban control mechanism is investigated. The optimization problem generalizes those from literature by considering a general criterion function and including the lot sizes as decision variables. Since no analytical solutions can be expected simulation combined with a genetic algorithm is used. The simulator KaSimIR as well as the optimization tool LEO are briefly described. Some examples demonstrate the usability of the approach.     

植物遗传资源获取与惠益分享的经济学分析

植物遗传资源是人类生存和发展的战略性资源。相关利益主体在争夺植物遗传资源时，利益冲突与分享博弈可以达成纳什均衡，这是一种社会成本最低的帕雷托最优状态。要维持这种均衡，并且使博弈双方的合作最终成为可能，就必须存在有效的威胁——完善、公正、有强制执行力的制度体系：确立完善的国际公约及国内立法，独立的植物遗传资源管理机构，严格的监督、处罚机制等等，这样才能提高社会经济活动的效率，增加社会总福利。     

Acting ethically,responding culturally: framing the new reproductive and genetic technologies in Sri Lanka

The spread of new reproductive and genetic technologies to the developing world stimulates reflection on the ethical issues they generate. The ‘built-in-ness’ of assumptions about personhood and relationality within these technologies means that local professionals must, perforce, engage with attempts to make sense of new technology with reference to local meanings and traditions or take positions which reject them. This paper explores such attempts to indigenise bioethics in contemporary Sri Lanka with reference to ethical/ cultural conflicts surrounding two practices: donor insemination and consanguinity counselling. The former is discussed in relation to the now defunct practice of polyandry and the latter in relation to matrilateral cross-cousin marriage. The final discussion takes up the consequences of this detailed parsing of local relationality for the larger project of rendering bioethics in some sense comparative.     

A Novel Estimation Approach for Mixture Transition Distribution Model in High-Order Markov Chains

A transformation is proposed to convert the nonlinear constraints of the parameters in the mixture transition distribution (MTD) model into box-constraints. The proposed transformation removes the difficulties associated with the maximum likelihood estimation (MLE) process in the MTD modeling so that the MLEs of the parameters can be easily obtained via a hybrid algorithm from the evolutionary algorithms and/or quasi-Newton algorithms for global optimization. Simulation studies are conducted to demonstrate MTD modeling by the proposed novel approach through a global search algorithm in R environment. Finally, the proposed approach is used for the MTD modelings of three real data sets.     

A Parameter-Tuned Genetic Algorithm for Economic-Statistical Design of Variable Sampling Interval X-Bar Control Charts for Non-Normal Correlated Samples

Among innovations and improvements that occurred in the past two decades on the techniques and tools used for statistical process control (SPC), adaptive control charts have shown to substantially improve the statistical and/or economical performances. Variable sampling intervals (VSI) control charts are one of the most applied types of the adaptive control charts and have shown to be faster than traditional Shewhart control charts in identifying small changes of concerned quality characteristics. While in the designing procedure of the VSI control charts the data or measurements are assumed independent normal observations, in real situations the validity of these assumptions is under question in many processes. This article develops an economic-statistical design of a VSI X-bar control chart under non-normality and correlation. Since the proposed design consists of a complex nonlinear cost model that cannot be solved using a classical optimization method, a genetic algorithm (GA) is employed to solve it. Moreover, to improve the performances, response surface methodology (RSM) is employed to calibrate GA parameters. The solution procedure, efficiency, and sensitivity analysis of the proposed design are demonstrated through a numerical illustration at the end.     

Comparing backpropagation with a genetic algorithm for neural network training

This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.     

Single process plan scheduling with genetic algorithms

AIn this paper, a genetic algorithm model for scheduling manufacturing resources is developed for the case when there is only one process plan available per job, hence there is no routeing flexibility. The scheduling objectives considered are minimizing the makespan and mean flow time. Genetic algorithms design issues are discussed and the working of the employed genetic operators is explained in detail. Parameters for the genetic algorithms used for single process plan scheduling SPPS problems are set through extensive experimentation. Finally, the genetic algorithms approach is compared with several other approaches in terms of optimality of solution and computAuthors: ing time. It was observed that in most cases the genetic algorithms approach performed better than other approaches both in terms of finding an optimal or near optimal solution as well as computing time.     

遗传算法在投资组合模型中的应用

均值-VaR模型是比较复杂的非线性规划问题，传统的算法不能保证得到全局最优值。鉴于此，引入遗传算法求解资产配置比例。对基于均值-VaR的单目标优化问题，设计了限定搜索空间和惩罚函数的遗传算法；而对多目标优化问题，应用并行选择遗传算法，并以沪深300行业分类指数构建投资组合，分析了行业资产配置的投资组合问题。结果表明，算法取得了良好的效果，解的结果既满足了投资的目标和约束条件，又反映了投资者之间不同的收益风险需求，且具有较好的实践性。     

基于可视化交互式遗传算法中映射方法的分析与研究

提出了一种对基于可视化交互式遗传算法中的非线性映射方法,结合遗传算法在n维空间上快速搜索的优点和人类在二维空间上把握数据整体分布的能力,采用VISOR算法既降低了计算的复杂度,又使数据空间和输出空间上数据点与聚类中心间的距离信息保持相似.仿真结果验证了该方法的可靠性、稳定性和快速性.     

遗传算法在逆向物流网络选址问题中的应用研究

随着公众环保意识的增强、环保立法的出台和可观的经济利益的显现,企业开始关注逆向物流。而在逆向物流中,集中退货中心管理是企业逆向物流系统高品质运作的基础和前提。因此,本文构建了一个逆向物流网络中的选址模型,旨在研究从备选地址中选择建立配送中心或回收中心最佳策略,然后应用遗传算法讨论了该模型的具体解法。最后针对模拟算例,通过对比遗传算法和部分枚举法的效率及结果,说明遗传算法对于求解这类问题是一种有效的方法。