农业机械化发展战略规划的过程方法研究

农业机械化发展战略规划是一个复杂系统问题,需要有一个科学可行的过程步骤,才能使战略规划问题顺利地开展.依据系统工程的理论与方法,在充分认识农业机械化系统的边界范围、结构关系、特征要求的基础上,阐明了农业机械化发展战略规划所涉及的基本问题与求解技术,构建了相应的过程方法,提出了每个阶段所需要的技术方法和求解问题.     

灌溉渠系流量最优调配的0-1规划模型

本文根据灌溉配水渠系的运行特点,提出在一定轮期和来水流量限制条件下实现下级配水渠最优组合及其配水时间最优排序的一种0-1规划模型,实例运算表明,这种方法可有效减少无效弃水和闸门调节次数,同时对渠道横断面的设计有参考意义。     

电力系统日有功优化调度的工程化软件与应用

随着电力市场的逐步建立和电力供需形势的变化，电力系统的优化调度具备了可行条件；同时，由于电力部门运作带有的计划性，需要开发出实用有效的、涉及多种人为调控约束的工程化方法用于实际生产。针对上述需要，建立了考虑若干为调控约束的日有功优化调度工程化模型，提出了基于专家规则、优先顺序和动态规划法的工程化算法，并开发了相应的工程化软件。该软件已在陕西电网成功运行一年，取得了显著的效益。     

Conversion to organic arable farming in The Netherlands: A dynamic linear programming analysis

Several studies show that organic farming is more profitable than conventional farming. However, in reality not many farmers convert to organic farming. Policy makers and farmers do not have clear insight into factors which hamper or stimulate the conversion to organic farming. The objective of this paper is to develop a dynamic linear programming model to analyse the effects of different limiting factors on the conversion process of farms over time. The model is developed for a typical arable farm in The Netherlands central clay region, and is based on two static liner programming models (conventional and organic). The objective of the model is to maximise the net present value over a 10-year planning horizon. The results of the analysis of a basic scenario show that conversion to organic farming is more profitable than staying conventional. In order to arrive at the actual profitable phase of organic farming, the farmer has to pass through the economically difficult 2-year conversion period. Sensitivity analysis shows that if depreciation is 25% higher than conventional fixed costs due to machinery made superfluous by conversion, conversion is less profitable than staying conventional. Also the availability of hired labour, which can be constrained in peak periods, has a strong effect on the cropping plan and the amount of area converted. Further analysis shows that a slight drop (2%) in organic prices lowers the labour income of the farmer and makes conversion less profitable than conventional farming. For farmers, a minimum labour income can be required to ‘survive’. The analysis shows that constraint on minimum labour income makes stepwise conversion the best way for farmers to overcome economic difficulties during conversion.     

组合预测方法及其在粮食产量预测中的应用

提出了一种组合预测方法，以“预测偏差绝对值的和最小”为标准，用线性规划确定组合权系数的最优加权算法，对青海省的粮食总产量进行了预测分析。结果表明，此模型具有较高的精度。     

农场作物及机器设备选择系统的研究

将数据库，模拟及线性规划结合在一起，建立了一个同时选择农作物，作业方式及机器设备的软件系统。此系统可求得农场资源的最佳配置，并有较好的可移植性，同时在很大程度上克服了线性规划的非整数解与机器，劳力整数要求的矛盾以及模拟技术在优化系统方面的局限。     

并联泵站群日优化运行方案算法

提出了并联泵站群日优化运行数学模型,该模型以并联泵站群日提水费用最少为目标函数,以各时段机组叶片角或转速为决策变量.考虑站间不同工况调节方式,采用大系统二级分解-动态规划聚合法进行求解.首先以泵站日提水耗电费用最少为目标,泵站提水量的分配为协调变量,将该模型分解成若干个泵站多机组日优化运行一级子模型;再以单机组日提水耗电费用最少为目标,机组提水量的分配为协调变量,将一级子模型分解为若干个单机组日优化运行二级子模型.该二级子模型以机组叶片安放角或机组转速为决策变量,机组提水量的离散值为状态变量,采用动态规划方法求解.构造的聚合模型以各机组提水量为决策变量,并联泵站群提水量的离散值为状态变量,同样采用动态规划方法求解.该方法可以解决不同工况调节方式、不同时段划分及不同日均扬程下并联泵站群的日优化运行问题.以淮阴一、三站并联运行为例,作了日优化运行计算,结果表明：各日均扬程下满负荷、80%负荷、60%负荷优化运行单位费用,较定角恒速运行分别平均节约10.53%,26.54%,34.40%.     

灌溉兼防洪水库的优化调度

我国干旱半干旱地区，天然降水量少且时程及地域相差大，并有很大的随机性。对有水库调节的地区，如何处理天然降水的随机性，合理利用有限的水资源，解决人畜饮水、工业用水、林业及农业灌溉耗水，使其在各种情况下都能达到最佳的社会效益和经济效益，该文选用动态规划法，编制的电算程序，很好地解决了这一问题。     

基于AutoCAD2000的二维图形自动编程软件的开发

介绍了基于AUTOCAD2000、采用Visual LISP作为开发工具的二维图形自动编程系统的开发，并详细介绍了数据结构、实体排序技术及各模块之间数据流的传送．并通过实例讲解了软件的应用。     

Resource allocation tradeoffs in Manila’s peri-urban vegetable production systems: An application of multiple objective programming

This paper designed and developed a multi-objective programming (MOP) model to illustrate the dynamic relationship among technologies, productive activities, constraints and farmers’ objectives in the peri-urban vegetable production system and use the model as an economic tool in analysing probable consequences of a given action or innovation on the farm. The best compromise solution was generated using four analytical steps, as follows: single-objective optimization (to determine the ideal and anti-ideal values of the objective functions); constrained optimization (to generate the set of Pareto non-dominated solutions); cluster analysis (to trim down efficient set into smaller homogeneous groups); and compromise programming (to determine where the best compromise solution lies).