企业逆向物流系统优化研究

随着市场竞争的加剧、产品生命周期的日益缩短、自然资源的日渐枯竭、人们环保意识的加强,逆向物流越来越受到企业的重视。因而,构建并优化自己的逆向物流系统成为企业具有战略意义的工作。文章首先分析了企业构建逆向物流系统的指标体系和优化时所应采取的措施．并据此建立数学模型,运用遗传算法来进行求解,并对结果进行了分析。,最后用一个算例来验证模型的可行性,并总结了模型的不足之处和将来的研究方向。     

一种求解装箱问题的混合算法

针对一维装箱问题,在考虑遗传算法早熟收敛问题和禁忌搜索算法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传和禁忌搜索的装箱优化算法,与简单遗传算法相比,该算法具有更好的收敛性能。最后通过实例验证了算法的有效性。     

配送中心车辆调度模型及遗传算法设计

针对物流配送中心车辆调度问题,采用混合整数规划方法进行建模。对实际问题进行研兖分析后,基于所研究问题的特点,提出基于遗传算法的求解方法。通过数值实验对算法不同参数组合进行分析、比较,获得最佳参数组合．建立了有效的求解该问题的遗传算法。并通过对实际问题的数值仿真试验．验证了算法的有效性。     

产销不平衡运输问题的遗传算法研究

根据运输问题的特点和要求,在运输成本最低的基础上,构建了产销不平衡运输问题的数学模型,并将该模型转换为产销平衡的运输问题,然后把遗传算法引入到模型的求解中,通过选择恰当的编码方法和遗传算子,求得模型的最优解。     

铁路煤炭调运问题的遗传算法

根据铁路运输中现有煤炭的运输状况，构造出煤炭调运时，煤种间不能相互代用和可以相互代用的数学模型，并基于遗传算法的C语言程序，对该模型进行了遗传算法设计，最后通过算例，验证了模型和算法的可行性。     

论植物品种权与农民权利的冲突

分析了农民权利问题产生、发展的历史过程,重点考察了农民权利在有关的国际条约中的性质、地位、作用,提出了解决农民权利问题的基本政策思路。研究结果表明:农民权利与育种者权利产生对抗的主要原因在于不合理的基因资源知识产权制度;不合理的基因资源知识产权制度严重损害发展中国家农民利益;CBD中基因资源国家主权原则应当成为构建发展中国家的农民权利和基因资源保护的法律体系的基本准则。     

Hedging derivative securities with genetic programming

One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black–Scholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black–Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20% of the 97 test paths GP has a lower tracking error than the Black–Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP. Copyright © 1999 John Wiley & Sons, Ltd.     

The impacts of CGIAR research: A review of recent evidence

We review evidence on the impacts of CGIAR research published since 2000 in order to provide insight into how successfully the CGIAR Centers have been in pursuing the System’s core missions. Our review suggests that CGIAR research contributions in crop genetic improvement, pest management, natural resources management, and policy research have, in the aggregate, yielded strongly positive impacts relative to investment, and appear likely to continue doing so. Crop genetic improvement research stands out as having had the most profound documented positive impacts. Substantial evidence exists that other research areas within the CGIAR have had large beneficial impacts although often locally and nationally rather than internationally. However, the “right-time, right-place” nature of successful policy research and the relatively limited geographic scale of much natural resource management research often limits the overall scale of impacts of these programmatic thrusts vis-à-vis genetic improvement research. We conclude that given the evidence available, the CGIAR’s portfolio of research allocations has become overly skewed toward natural resource management and policy research over time. Hence, restoring somewhat the share of resources allocated to crop genetic improvement is warranted. In addition, the CGIAR needs to prioritize impact assessment of resource management and policy research to deepen its understanding of the social and environmental impacts of its work.     

考虑供电枢纽传输成本的充电站选址定容模型

电动汽车及其配套设施的发展受制于早期形成的有限布局,不断增长的电动汽车市场规模和充电需求产生矛盾。充电站的建设运营方能够在巨大潜力的充电设施发展中获得巨大利好。通过以最大化充电站建设运营方的利润为优化目标,构建需求覆盖选址模型,权衡供电枢纽、充电站和充电桩的成本和满足充电需求的收入,利用遗传算法求解获得选址定容的决策。研究发现：一方面,充电站的覆盖半径对利润和决策结果影响较大且复杂,充电站投资建设前期需要对影响覆盖范围的诸多因素进行市场调研;另一方面,铺电单位成本对决策结果影响明显,相对高的铺电成本会抑制充电站选址数量和容量大小,从而降低利润。因此充电站投资建设方需要分别对两类成本做好数据调研,避免决策结果出现较大偏差。     

基于改进遗传算法的离场航班时刻优化

为提高机场航班放行正常率,对离场航班时刻进行了智能优化方法研究。考虑进场航班时刻不变、离场容量和航班时刻调整范围受限等约束,构建了以全局时间调整偏差总量最小为目标的离场航班时刻优化模型;为提高优化效率,将遗传算法的交叉概率改进为自适应交叉概率;设计了一种基于改进遗传算法的离场航班时刻优化方法。以兰州中川国际机场全天运行455起降架次为例,对航班时刻进行优化和仿真验证。结果表明,优化的航班时刻相较于原航班时刻,航班平均延误时间降低12.8%;离场航班平均延误时间降低22.3%;离场航班延误架次减少了42.8%;航班放行正常率提高了12%。采用基于自适应交叉概率的遗传算法可有效降低了航班延误和提高航班放行正常率。