考虑再利用需求的垃圾回收处理优化模型

垃圾回收处理是建设资源节约和环境友好形社会(即两型社会)的基本要求.本文利用逆向物流管理优化方法研究带确定性日回收垃圾处理问题,对经过垃圾处理中心处理过后的垃圾分全部或部分可被再利用两种情形,建立了垃圾回收相关企业成本最小的约束优化模型.该模型是0-1整数规划模型,能够直接在LINGO软件平台上求解.应用实例验证了模型的效用.     

整数规划的一类填充函数算法

填充函数算法是求解连续总体优化问题的一类有效算法。本文改造[1]的填充函数算法使之适于直接求解整数规划问题。首先,给出整数规划问题的离散局部极小解的定义,并设计找离散局部极小解的领域搜索算法。其次,构造整数规划问题的填充函数算法。该方法通过寻找填充函数的离散局部极小解以期找到整数规划问题的比当前离散局部极小解好的解。本文的算法是直接法,数值试验表明算法是有效的。     

一类系数为梯形模糊数的整数规划

介绍了模糊数学和整数规划的背景、现状、以及发展趋势,并以模糊结构元理论定义了梯形模糊加权序,进一步证明了模糊整数规划模型的最优解等价于整数规划模型的最优解,再利用整数规划模型的最优解的求解方法求解模糊整数规划模型的最优解,最后,通过算例验证方法的可行性.     

混合启发式算法求解多配送人员车辆路径问题

为解决带时间窗和多配送人员的车辆路径问题,本文采用混合启发式算法对其进行求解。该算法主要由整数规划重组、局部搜索算法和模拟退火算法三部分组成。在算法中,整数规划重组有效提高了解的质量,局部搜索算法和模拟退火算法保证了算法搜索的深入性和广泛性。通过与CPLEX和禁忌搜索算法进行对比,证实了混合启发式算法实用价值更高,求解效果更好。     

整数规划的布谷鸟算法

布谷鸟搜索算法是一种新型的智能优化算法．本文采用截断取整的方法将基本布谷鸟搜索算法用于求解整数规划问题．通过对标准测试函数进行仿真实验并与粒子群算法进行比较,结果表明本文所提算法比粒子群算法拥有更好的性能和更强的全局寻优能力,可以作为一种实用方法用于求解整数规划问题．     

基于DNA遗传算法的随机整数规划期望值模型求解

为了求解随机整数规划问题,提出了随机整数规划期望值模型的概念,分析了利用DNA遗传算法求解此类问题的优点,并设计了求解算法,最后通过报童问题,验证了算法的可行性和有效性.     

离散回溯搜索算法求解多柔性作业车间调度

针对柔性作业车间生产中机器和工序柔性与多能工的存在建立模型,并提出一种整数编码方案和设一种基于Pareto解集的离散回溯搜索算法进行求解。首先,采用精英化历史种群的方法提升历史种群引导当前种群进化的能力;其次,在交叉变异步骤用遗传交叉算子替代回溯搜索算法原有结构;再次,为保留更多较优解到当前种群,结合快速非支配排序方法更新当前种群;最后,求解数值实例,与多种智能算法进行对比,验证算法的可行性和有效性。     

多气源天然气管网运行优化问题的混合整数规划模型及其算法

对于多气源天然气管网运行优化问题,文章首先引入了刻画压缩机开关的0-1整数变量,并对非线性的管道压降方程进行了合理的松弛化处理,建立了更符合实际的非线性混合整数规划模型.其次,基于序列线性化的思想,设计了一种求解该模型的序列混合整数线性化算法.最后,在不同规模的天然气管网系统中进行了多方面的对比实验.实验结果表明,新模型及求解算法能够有效降低成本、减少压缩机能耗,并且所需求解时间大大减少.     

最小正基直接搜索共轭梯度法

1引言直接搜索算法是求解最优化问题的一类重要算法,它特别适用于目标函数的梯度信息无法得到或很难计算的情形.直接搜索算法大致可以分为两大类,一类是包含单纯形搜索[19]和模式搜索[12,22]等的直接搜索算法,这类算法不使用泰勒展开等方法对目标函数进行建模;另一类算法则依赖于对目标函数的建模,常被称为以模型为基础的方法,有     

储油罐的变位识别与罐容表标定

研究了储油罐的变位识别与罐容表重新标定的问题,利用三重积分的方法和Maple软件对平底椭圆柱储油罐油量的表达式进行了精确求解,并利用实验数据对求解结果进行了误差补偿;利用近似积分算法对主体为圆柱,两端为球冠的储油罐的油量表达式进行了近似求解,通过建立优化模型,搜索算法和Matlab软件求解出了问题二中的变位角度,并利用实验数据对求解结果进行了检验;最后利用得到的油量表达式给出了两个储油罐的罐容表.     

带交易费用的离散多因素投资组合最优化(英文)

研究带有凹的交易费函数的离散多因素投资组合模型.与传统的投资组合模型不同的是,该模型中投资组合的决策变量是交易手数(整数),其最优化模型是一个非线性整数规划问题.为此本文提出了一个基于拉格朗日松弛和连续松弛的混合分枝定界算法,为测试算法的有效性,我们分别采用美国股票市场真实数据和随机产生的数据,数值结果表明该算法是有效的.     

带交易费用的离散多因素投资组合最优化

研究带有凹的交易费函数的离散多因素投资组合模型．与传统的投资组合模型不同的是,该模型中投资组合的决策变量是交易手数（整数）,其最优化模型是一个非线性整数规划问题．为此本文提出了一个基于拉格朗日松弛和连续松弛的混合分枝定界算法,为测试算法的有效性,我们分别采用美国股票市场真实数据和随机产生的数据,数值结果表明该算法是有效的．     

A cutting plane algorithm for MV portfolio selection model

This paper deals with a portfolio selection problem with fuzzy return rates. A possibilistic mean variance (FMVC) portfolio selection model was proposed. The possibilistic programming problem can be transformed into a linear optimal problem with an additional quadratic constraint by possibilistic theory. For such problems there are no special standard algorithms. We propose a cutting plane algorithm to solve (FMVC). The nonlinear programming problem can be solved by sequence linear programming problem. A numerical example is given to illustrate the behavior of the proposed model and algorithm.     

Selecting Portfolios with Fixed Costs and Minimum Transaction Lots

The original Markowitz model of portfolio selection has received a widespread theoretical acceptance and it has been the basis for various portfolio selection techniques. Nevertheless, this normative model has found relatively little application in practice when some additional features, such as fixed costs and minimum transaction lots, are relevant in the portfolio selection problem. In this paper different mixed-integer linear programming models dealing with fixed costs and possibly minimum lots are introduced. Due to the high computational complexity of the models, heuristic procedures, based on the construction and optimal solution of mixed integer subproblems, are proposed. Computational results obtained using data from the Milan Stock Exchange show how the proposed heuristics yield very good solutions in a short computational time and make possible some interesting financial conclusions on the impact of fixed costs and minimum lots on portfolio composition.     

非负约束条件下组合证券投资决策的分枝定界法

研究非负约束条件下 ,实现预期收益率的组合证券投资决策问题 ,将整数线性规划的分枝定界法用于该问题的求解 ,并应用于一个四元证券投资决策问题     

Expanded model of the project portfolio selection problem with divisibility,time profile factors and cardinality constraints

This paper develops a new model for project portfolio selection over a planning horizon with multiple time periods. The model considers the divisibility of projects and combines reinvestment, set-up cost, cardinality constraints and precedence relationship in the scheduling, simultaneously. For efficient computation, an equivalent mixed integer linear programming representation is provided. One numerical example with three scenarios is given to highlight the capability and characteristics of the proposed model.     

Comments on “A mixed integer linear programming formulation of the optimal mean/Value-at-Risk portfolio problem”

Benati and Rizzi [S. Benati, R. Rizzi, A mixed integer linear programming formulation of the optimal mean/Value-at-Risk portfolio problem, European Journal of Operational Research 176 (2007) 423–434], in a recent proposal of two linear integer programming models for portfolio optimization using Value-at-Risk as the measure of risk, claimed that the two counterpart models are equivalent. This note shows that this claim is only partly true. The second model attempts to minimize the probability of the portfolio return falling below a certain threshold instead of minimizing the Value-at-Risk. However, the discontinuity of real-world probability values makes the second model impractical. An alternative model with Value-at-Risk as the objective is thus proposed.     

手数约束和凹交易费下的离散投资组合模型及算法

本文建立带手数约束和凹交易费的离散投资组合模型,给出求解该模型的一种精确算法。该算法是一个基于拉格朗日松弛和次梯度对偶搜索的分枝定界算法。为测试算法的有效性,用随机产生的数据对模型进行数值实验。作为其应用,用沪深300指数的真实数据实证检验该模型,并与不含交易费用的离散投资组合模型进行数值比较分析。数值分析表明算法能在合理的时间内给出模型的投资组合策略, 对解决中小规模的离散投资组合问题是有效的。     

A zero-one model for project portfolio selection and scheduling

A zero-one integer linear programming model is proposed for selecting and scheduling an optimal project portfolio, based on the organisation's objectives and constraints such as resource limitations and interdependence among projects. The model handles some of the issues that frequently arise in real world applications but are not addressed by previously suggested models, such as situations in which the amount of available and consumed resources varies in different periods. It also allows for interactive adjustment following the optimisation process, to provide decision makers a method for controlling portfolio selection, based on criteria that may be difficult to elicit directly. It is critical for such a system to provide fast evaluation of alternatives the decision makers may want to examine, and this requirement is addressed. The proposed model not only suggests projects that should be incorporated in the optimal portfolio, but it also determines the starting period for each project. Scheduling considerations can have a major impact on the combination of projects that can be incorporated in the portfolio, and may allow the addition of certain projects to the portfolio that could not have been selected otherwise. An example problem is described and solved with the proposed model, and some areas for future research are discussed.