政府促进银行实施绿色信贷的多目标优化策略研究

传统的绿色信贷研究中存在着模型简单、非动态参数以及只能获取纳什均衡点的局限性。为改善这些局限性,研究了一种基于数据驱动多目标优化算法的政府促进银行实施绿色信贷的策略计算方法。首先针对绿色信贷的最优策略求解问题建立数据驱动的多目标优化算法框架,再基于历史数据建立算法框架中的最优策略马可夫状态转移模型,最后使用多目标粒子群优化算法对政府和银行的长远总收益进行最优策略求解。与传统的基于近似模型及博弈论的方法不同,本文提出的方法可以获得历史数据中的经验,从而制定出具有更加长远收益的策略,避免了传统方法中的“短视”现象。分析结果表明,绿色信贷的收益不会在短时间内显现,因此政府在做决策时,必须根据绿色信贷收益的回报周期作出长远的判断。     

计及需求响应不确定性的综合能源系统多目标优化调度管理

需求响应作为电力系统的重要调节手段,可显著提升系统灵活性和经济性。利用价格弹性构建了包含价格与激励措施的需求响应模型,并在此基础上考虑需求响应的不确定性,以综合能源系统经济性和环保性为优化目标,构建了综合能源系统多目标优化调度模型。利用E约束法将多目标优化模型转化为单目标优化模型,得到Pareto最优解集,运用模糊决策法从中选取最优方案。基于实际案例进行测算,结果表明价格型与激励型需求响应手段的结合能够实现削峰填谷,有效降低系统的运行成本和碳排放量。     

不确定条件下工程项目的多目标模糊均衡优化

在工程项目多目标优化问题研究基础上,研究不确定环境下工程项目多目标均衡优化问题.利用模糊数表示费用变化率和质量变化率,考虑模糊集的不同可能性水平,建立工程项目多目标模糊均衡优化模型,给出模型的求解方法和步骤,得到不同可能性水平下多目标优化问题的最优折衷解变化范围.优化方法使决策者能够根据决策风险的大小进行最优目标值的确定.     

电热耦合综合能源系统双阶段多目标规划研究

多能耦合系统是未来分布式能源供给方式的重要发展方向。为了实现电热耦合能源供给系统的合理规划、促进能源供给与消费的经济与环保的协调发展,提出一种面向电热耦合能源系统的综合能源系统双阶段规划优化方法。模型的第一阶段是在投资和环境最优的目标下实现电热耦合综合能源系统的合理规划,第二阶段是在考虑设备运行特性的基础上对规划的结果进行运行优化,以获得能源系统的最优运行方案,并从多个指标验证规划方案的合理性。利用NSGA-II算法求得模型进的帕累托解集,使用多准则妥协优化法从帕累托解集中决策出最优配置方案。仿真结果表明,提出的双阶段多目标综合能源规划方法能够实现能源供给系统的经济与环保双优。     

基于神经网络的多目标演化优化方法

本文考虑在决策者偏好不明确的条件下，使系统获得最优的思想，提出了多目标决策系统最优解的概念。把前馈神经网络与演化算法相结合，用于多目标决策系统最优解的选取。给出了有关定理的证明和示例。     

基于不同优先级的油田增产多目标规划

为了衡量目标优先级不同对油田增产决策的影响,建立了基于不同优先级的多目标规划模型,并对增油量和措施费用两个目标不同优先级下的模型进行了求解,结果表明,目标优先级不同,油田增产措施的配置结果不同,但主要集中在对有效增产措施的配置上,对于效果较差的增产措施并无差异;同时,目标优先级不同导致目标的最优解相差较大,反映了决策者偏好对油田中后期增产决策的重要性.     

基于前景理论的区域绿色经济发展水平多指标评价

针对绿色经济发展水平评价指标存在模糊性、异质性等问题,提出一种规范化信息,并运用前景理论进行多指标评价的方法。首先,构建区域绿色经济发展水平评价指标体系。其次,规范化异质信息,并计算待比较方案的前景函数。然后,通过定义前景值排序与专家决策偏好的一致性和不一致性程度,建立决策优化模型,计算各方案的前景值,进行方案排序和优选。最后,以三明市县域绿色经济发展水平评价为例,说明该方法的有效性、合理性和实用性。     

随机需求下考虑半成品库存的多周期生产决策优化

为了更好地应对需求的不确定性,在需求实现之前,企业既可以生产成品直接满足需求,亦可生产部分半成品,在观察到实际需求之后短时间内迅速完成剩余生产环节以满足需求。未加工的半成品和未售出的成品可用于满足后续周期的需求。作为一种提高生产灵活性的手段,分阶段生产的方式会产生更高的成本。企业需要在成本和灵活性之间作出权衡,优化生产决策。模型通过动态规划的方法,研究需求不确定情况下考虑半成品库存的多周期生产决策问题,通过分析目标函数以及最优值函数的结构性质,推导出最优的多周期生产策略为修正的目标库存策略,并且分析了不同参数对最优策略的影响。     

区间粗糙数多属性决策方法

针对属性值为区间粗糙数,属性权重部分已知和属性权重未知两种情形的多属性决策问题,本文利用灰色关联分析的思想方法,构建了一种区间粗糙数多属性决策方法。本文首先利用区间粗糙数的运算法则和期望值比较,确定最优理想方案和最劣理想方案,并基于灰色关联度分析方法构建了属性权重部分已知、属性权重未知两种情形的多目标优化模型,从而确定属性权重和属性权重表达式,进而获得各方案的综合评价值和方案排序。最后以一个实例验证模型的有效性与适用性。     

框架结构多目标优化方法

提出了一种框架结构多目标优化方法。基于力法给出了框架结构的等效刚度、最大弯矩随结构材料、几何等设计参数的变化,利用动能等效给出了框架结构的等效质量,进而得到框架结构一阶固有频率随结构材料、几何等参数的变化,用有限元方法验证了所得频率、弯矩公式的正确性。以固有频率、最大弯矩为优化目标函数构造框架结构的多目标优化模型,通过单目标优化得到频率、弯矩最优值,以单目标优化值为基础构造全局目标优化函数,将多目标优化转化为单目标优化。     

A green train scheduling model and fuzzy multi-objective optimization algorithm

Train scheduling model is traditionally formulated to minimize the energy consumption for reducing the operation cost. As the European Union formulates the first carbon emission trading scheme in the world, it is necessary to extend the operation cost to include the expenses for buying/selling the carbon emission allowances. In this paper, we propose a multi-objective train scheduling model by minimizing the energy and carbon emission cost as well as the total passenger-time, and named it as green train scheduling model. For obtaining a non-dominated timetable which has equal satisfactory degree on both objectives, we apply a fuzzy multi-objective optimization algorithm to solve the model. Finally, we perform two numerical examples to illustrate the efficiency of the proposed model and solution methodology.     

绿色建筑项目管理综合均衡优化研究

绿色建筑凭借其诸多优点,已然成为我国未来建筑发展的新方向,而对绿色建筑的工期、成本和功能进行全面系统研究并进行综合均衡优化,对于促进绿色建筑的发展具有深远影响.在描述各工程活动的成本和持续时间之间、功能和持续时间之间的非线性关系基础上,运用多属性效用函数理论构建了绿色建筑项目的功能一工期一成本综合均衡优化模型,并用遗传算法进行求解,可以得到最佳均衡解.最后通过一个实例验证了优化模型具有良好的科学性和实用性.     

能耗强度与能源结构优化对内生经济增长影响研究

本文在垂直创新模型框架下,将能耗强度和碳减排比例引入生产函数,将人们对气候变化的警惕意识引入效用函数,建立了一个动态的内生低碳经济增长模型。通过求解所建动态优化模型,找到了经济长期均衡增长的最优路径,讨论了能源强度和能源消费结构变化与经济均衡增长的相互关系,分析了各参数对经济最优增长路径的影响,比较了化石能源内部消费结构不变和逐年清洁两种情景下的经济最优增长路径的异同。长期来看,能源强度、二氧化碳减排速率与经济增长率有相互的负向线性关系,缩小反映化石能源内部消费结构的综合碳排放系数对经济增长有促进作用。非化石能源比例越高,低碳技术应用越广泛,研发成果对技术积累的贡献率越大,消费者对当前消费的偏好程度越小,人们对气候变化的警惕意识越强,则越有利于经济的低碳发展。     

A NEW DERIVATIVE FREE OPTIMIZATION METHOD BASED ON CONIC INTERPOLATION MODEL

In this paper, a new derivative free trust region method is developed basedon the conic interpolation model for the unconstrained optimization. The conic inter-polation model is built by means of the quadratic model function, the collinear scalingformula, quadratic approximation and interpolation. All the parameters in this model axedetermined by objective function interpolation condition. A new derivative free method isdeveloped based upon this model and the global convergence of this new method is provedwithout any information on gradient.     

CFD-based optimization of fuel injection strategies in a diesel engine using an adaptive gradient method

A computationally efficient computational fluid dynamics (CFD)-based optimization method with the capability of finding optimal engine operating conditions with respect to emissions and fuel consumption has been developed. The approach taken uses a steepest descent method for an adaptive cost function, where the line search is performed with a backtracking algorithm. The backtracking algorithm utilizes quadratic and cubic polynomials to accelerate the convergence, and the initial backtracking step employs an adaptive step size mechanism which depends on the steepness of the search direction. The adaptive cost function is based on the penalty method such that the penalty term is stiffened after every line search. The engine simulations are performed with a KIVA-3-based CFD code which is equipped with well-established spray, combustion and emission models. The application of this optimization tool is demonstrated for a non-road, medium-speed DI diesel engine which, for these simulations, utilizes a multi-orifice, asynchronous injection system. It has been demonstrated that this new injection method has a large potential for reducing emissions while maintaining a low fuel consumption. In addition, this optimization approach is computationally very efficient when good enough initial values are available.     

基于峰谷分时电价机制的节能发电调度优化模型

为减少能源消耗,电力工业在发电侧推行节能发电调度,而在用电侧则施行需求侧管理。事实上,发电侧与用电侧可以通过联合优化实现更好的节能效果。峰谷分时电价将改变原有系统负荷的分布,发电侧机组发电调度的安排将随之改变,发电煤耗水平也将相应地变动。有鉴于此,首先分别构建了需求侧峰谷分时电价响应分析模型以及发电侧节能发电调度的机组组合模型;其次,为了实现分时电价响应向机组煤耗效益的传递,以机组发电煤耗与启停煤耗最少为目标构建了需求侧与发电侧的联合优化模型;最后,通过算例分析发掘模型的节能效益,借助GAMS求解上述模型,优化结果表明分时电价的优化将有助于改进发电侧的节能调度效果,同时将实现一定的环境效益。     

An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels

This paper proposes a new design optimization framework for suspension systems considering the kinematic characteristics, such as the camber angle, caster angle, kingpin inclination angle, and toe angle in the presence of uncertainties. The coordinates of rear inner hardpoints of upper control arm and lower control arm of double wishbone suspension are considered as the design variables, as well as the uncertain parameters. In this way, the actual values of the design variables will vary surrounding their nominal values. The variations result in uncertainties that are described as interval variables with lower and upper bounds. The kinematic model of the suspension is developed in software ADAMS. A high-order response surface model using the zeros of Chebyshev polynomials as sampling points is established, termed as Chebyshev metamodel, to approximate the kinematic model. The Chebyshev meta-model is expected to provide higher approximation accuracy. Interval uncertain optimization problems usually involve a nested computationally expensive double-loop optimization process, in which the inner loop optimization is to calculate the bounds of the interval design functions, while the outer loop is to search the optimum for the deterministic optimization problem. To reduce the computational cost, the interval arithmetic is introduced in the inner loop to improve computational efficiency without compromising numerical accuracy. The numerical results show the effectiveness of the proposed design method.     

An Activity-Based Costing decision model for life cycle assessment in green building projects

Carbon emissions are an increasingly important consideration in sustainable environmental development. In the green building industry, green construction cost controls and low-carbon construction methods are considered to be the key barriers encountered. Based on Corporate Social Responsibility (CSR) policy, management of carbon emissions from green building projects contributes to the acquisition of accurate building cost information and to a reduction in the environmental impact of these projects. This study focuses on the CO₂ emission costs and low-carbon construction methods, and proposes a 0–1 mixed integer programming (0–1 MIP) decision model for integrated green building projects, using an Activity-Based Cost (ABC) and life cycle assessment (LCA) approach. The major contributions of this study are as follows: (1) the integrated model can help construction company managers to more accurately understand how to allocate resources and funding for energy saving activities to each green building through appropriate cost drivers; (2) this model provides a pre-construction decision-making tool which will assist management in bidding on environmentally-friendly construction projects; and (3) this study contributes to the innovation operation research (OR) literature, especially in regard to incorporating the life cycle assessment measurement into construction cost management by utilizing a mixed decision model for green building projects.     

考虑订单合并和货物转运的多式联运路径优化研究

为了获得运输的规模经济效应,本文研究了一种考虑订单合并和货物转运的零担多式联运路径优化问题。首先,以总运输成本为目标函数,以网络中的运输工具容量、可以提供的运输工具最大数量、运输工具服务的关闭时间以及订单时间窗为约束,构建混合整数规划模型,在模型中允许多个订单进行合并运输并考虑运输过程中的转运成本。其次,由于多式联运路径优化问题是典型的NP-hard问题,为了快速求解该模型,开发了一种可以快速为该问题提供近似最优解和下界的列生成启发式算法。最后,生成并测试了大量算例,结果表明所开发的列生成启发式算法可以在较短的时间内提供高质量的近似最优解。文章所构建的模型和开发的列生成启发式算法可以为零担自营多式联运物流企业提供高效的决策支持。     

基于随机规划的建筑集群冷热电联供系统优化研究

面向建筑集群的冷热电联供系统的设计和优化是实现建筑楼宇能源成本节约的重要途径。随机因素对该联供系统的优化决策,具有显著的影响。考虑建筑楼宇的能源需求为随机变量,构建随机混合整数规划模型,解决以最小化建筑楼宇总费用为目标时建筑集群冷热电联供系统的优化问题;其次,提出采用Benders多割平面方法求解多目标规划问题,从而寻找冷热电联供系统的设备配置和系统运行的Pareto最优决策;最后,通过实验验证了模型和算法的有效性。实验结果表明建筑集群在协作模式下,相比于非协作模式,具有更低的总费用。