控制点优化磨光算法在长江径流量预测中的应用

盈亏修正磨光法所得到的逼近效果仍然很差,通过控制点的参数优化和目标函数的最小,提出一种控制点优化磨光算法,利用这个算法得到参数后代入模型,使预测的精度得到提高.通过实例,该算法简单易行,并通过相对误差进行了分析,控制点优化磨光算法所得到的预测值好于神经网络模型、PPAR和小波网络模型的预测值,这为研究磨光法提供了较好的分析方法.     

基于得分信息的双边匹配决策方法

针对基于得分信息的双边匹配问题,提出了一种决策方法。首先,给出了基于得分信息的双边匹配问题的描述;接着,引入了满意度计算公式;以每个主体满意度最大为目标,构建了求解该双边匹配决策问题的多目标优化模型;考虑到每方每个主体的重要程度,将多目标优化模型转化为双目标优化模型;运用线性加权法将双目标优化模型转化为单目标优化模型,在此基础上,运用匈牙利法进行求解获得匹配结果。最后,毕业生与实习岗位的双边匹配实例分析说明了所提方法的有效性。     

基于改进微粒群算法的建设项目多目标动态优化

针对建设项目的复杂性和动态性,建立基于改进微粒群算法的多目标动态优化模型.首先,为提高算法性能,引入外部归档集和阈值并构建基于理想点法的适应度函数;其次,分别建立工期模型、加入系统可靠度的质量模型以及加入费用现值的成本模型,由其得到综合优化模型;最后结合工程实例对算法进行验证并与非劣分类遗传算法(NSGA-Ⅱ算法)对比.结果表明:方法比NSGA-Ⅱ算法的优化结果更科学、收敛速度更快.     

求解物流运输网络SUM-MIN双目标路径问题的扩展标号法

研究了物流运输网络SUM-MIN双目标路径问题. 基于模糊规划方法提出了一种求解SUM-MIN双目标路径问题的目标函数集成方法,以及集成后目标函数的扩展标号法. 在将双目标转化为单目标时,综合考虑了每个目标的边缘评价和两个目标的整体评价因素,通过对每个目标分配的权重将决策者的偏好充分体现到决策过程中,采用广义的模糊目标集成算子形成了相应的折衷规划模型. 最后,通过实例对所提方法进行了说明.     

一种基于模糊多目标决策的炮兵火力优化方法

适应现代炮兵作战的战术思想和要求，深入分析影响炮兵目标威胁度和炮兵火力单位射击有利度的诸因素，构建了基于模糊多目标决策算法的目标威胁度评估和火力优化模型，通过实例仿真，表明所建立的优化模型能有效提高炮兵辅助指挥决策的可信度。     

基于极大模理想点法的投资组合决策模型分析

基于马克维茨投资组合模型的均值一方差理论,构建一种投资组合收益和风险在一定范围的双目标线性模糊优化模型,并尝试采用极大模理想点法来求解该模型．最后,给出一实际算例,对一具体投资组合模型进行研究,结果表明：本文所采用的极大模理想点法是可行的、有效的;本文所采用的算法比已有文献给出的模糊线性规划法具有更加广泛意义的优化结果．     

基于较优方案最大区别度的组合权重赋值法

现有多方案决策中的指标权重计算方法可能产生评价结果中较优方案不突出的难题,进而提出了一种新的方法——基于较优方案最大区别度的组合权重赋值法.方法可根据方案间区别度最大的原则将主观赋权法和客观赋权法中所确定的指标权重进行集结,综合考量待评方案,并建立多方案决策结果最大化和决策结果间方差最大化非线性优化模型,采用理想点法对具体的多目标规划问题进行求解.方法的运用有利于扩大备选方案之间的差距,进而突出较优方案,最后通过实例说明了该方法的优越性.     

基于遗传算法和改进的Dijkstra算法的电缆敷设优化研究

首先结合电缆敷设相关标准建立了基于多种条件限制的电缆敷设优化的多目标规划模型,将分层序列法的思想运用于模型的求解中.将总敷设路线最短作为第一目标,转弯数最少作为第二目标,错层数最少作为第三目标.求解时首先将遗传算法和改进的Dijkstra算法相结合,共同进行第一目标和第二目标的求解;对于第三目标错层数最少,在运用改进的Dijkstra算法得出待敷设路线后,设计了基于贪心准则的贪婪敷设算法来满足错层数最少的要求.最终通过MATLAB编程实现以上思想并分别对30条和100条电缆的敷设进行实例验证.     

一种具有区间数信息的多目标指派方法

针对具有区间数信息的多目标指派问题,给出了一种指派方法。首先,将不同类型目标的区间数损益矩阵规范化为区间数成本矩阵,并应用区间数运算法则构建区间数多目标指派问题的总成本矩阵。然后,通过事先定义的任意两个区间数的序关系,将区间数指派问题优化模型转化为一个双目标优化模型,并采用线性加权法将其转化为单目标优化模型来进行求解,同时还考虑了如何处理人员数量与任务数量不相等的情形的指派问题;最后,通过一个实例分析说明了本文给出方法的可行性和有效性。本文的方法丰富了已有的求解方法,具有实际应用价值。     

满意度最大条件下的战时保障补给站选址问题研究

设施选址是装备保障系统构建过程中的重要决策步骤。文章首先分析了现代战争中装备保障设施尤其是终端补给站选址问题对形成装备保障快速反应能力的重要性,提出了以作战区域内所有作战单位的保障满意度最大为目标的有限补给站选址优化模型,作战单位对选址方案的保障满意度是从所需的资源数量和保障反应及时性两个方面进行考虑的。之后基于拉格朗日松弛算法设计了模型的启发式求解算法,实验结果表明该算法有效。     

制造单元布局问题的双目标模型与算法研究

单元制造系统的布局对于提高系统的效率起着十分重要的作用。以最小化物料周转量和设施面积为目标,建立了一个单元制造系统布局的双目标优化模型,在该模型中不同制造单元的布局、单元内部不同设施的位置与方向这几个问题可以同时进行优化。基于模拟退火邻域解的变尺度生成机制和双目标抽样准则设计了模型的求解算法。算例表明本文算法所得Pareto解集优于经典的NSGA-Ⅱ算法。     

基于两粒度语言评价信息的双边匹配决策

针对基于两粒度语言评价信息的双边匹配问题,提出了一种了基于二元语义信息处理的决策方法。在该方法中,首先将两粒度语言评价信息转化为两粒度二元语义信息;考虑以每个主体满意度最大为目标,运用广义二元语义加权平均算子构建了多目标优化模型;进一步地,运用二元语义算术平均算子将多目标优化模型转化为双目标优化模型;根据二元语义的自身特点将双目标优化模型转化为单目标优化模型,进而进行求解来得到匹配方案。最后,给出一个算例说明所提供方法的有效性。     

The inventory replenishment planning and staggering problem: a bi-objective approach

To the best of our knowledge, this paper is the first one to suggest formulating the inventory replenishment problem as a bi-objective decision problem where, in addition to minimizing the sum of order and inventory holding costs, we should minimize the required storage space. Also, it develops two solution methods, called the exploratory method (EM) and the two-population evolutionary algorithm (TPEA), to solve the problem. The proposed methods generate a near-Pareto front of solutions with respect to the considered objectives. As the inventory replenishment problem have never been formulated as a bi-objective problem and as the literature does not provide any method to solve the considered bi-objective problem, we compared the results of the EM to three versions of the TPEA. The results obtained suggest that although the TPEA produces good near-Pareto solutions, the decision maker can apply a combination of both methods and choose among all the obtained solutions.     

A bi-objective mathematical model for two-dimensional loading time-dependent vehicle routing problem

This paper introduces two-dimensional loading time-dependent vehicle routing problem and proposes a bi-objective mathematical model. This problem assesses the process of distributing the rectangular-shaped demanded items over an urban environment; it does not, however, allow items to be loaded on top of each other. In addition to the above assumptions, the presented model also satisfies the first-in-first-out property in the time-dependent vehicle routing problem. Given the NP-hard nature of the problem, a method called elitist non-dominated sorting local search is developed to obtain its solutions. To evaluate the performance of the proposed algorithm, the solutions of this algorithm for small-scale problem instances are compared with the results of an exact method. For the medium-scale problem instances, results of NSGA-II and SPEA2 are used as the basis of comparison. The computational results demonstrate the good performance of the proposed method.     

不确定偏好序信息下考虑主体心理行为的双边匹配决策方法

针对基于不确定偏好序信息的双边匹配问题,本文提出了一种决策方法。给出了双边匹配和不确定偏好序的相关概念,同时给出了不确定偏好序信息下考虑主体心理行为的双边匹配问题描述;以每个主体给出的临界值作为其参照点,计算了每个主体给出的不确定偏好序相对于参照点的收益或损失;考虑到主体损失规避的心理行为特征,依据TODIM思想计算每个主体对另一方主体的益损值的感知价值;在此基础上,构建了求解该双边匹配问题的双目标优化模型,使用线性加权法将双目标优化模型转化为单目标优化模型,通过求解该单目标优化模型获得匹配结果;最后,通过IT服务外包中的供给方与需求方的双边匹配实例分析说明了所提方法的有效性。     

On the calculation of stability radius for multi-objective combinatorial optimization problems by inverse optimization

This paper deals with stability analysis in multi-objective combinatorial optimization problems. The stability radius of an efficient solution is defined as the maximal adjustment of the problem parameters such that this solution remains efficient. An algorithm based on inverse optimization is proposed to compute it. The adjustment is limited to the coefficients of the objective functions and measured by the Chebyshev norm. This approach is applied to randomly generated instances of the bi-objective knapsack problem and computational results are reported. Several illustrative examples are analyzed.     

Competition among parties and power: an empirical analysis

This study develops a bi-objective method for burn-in decision makings with a view to achieving an optimal trade-off between the cost and the performance measures. Under the proposed method, a manufacturer specifies the relative importance between the cost and the performance measures. Then a single-objective optimal solution can be obtained through optimizing the weighted combination of these two measures. Based on this method, we build a specific model when the performance objective is the survival probability given a mission time. We prove that the optimal burn-in duration is decreasing in the weight assigned to the normalized cost. Then, we develop an algorithm to populate the Pareto frontier in case the manufacturer has no idea about the relative weight.     

Multiobjective scatter search for a commercial territory design problem

In this paper, a multiobjective scatter search procedure for a bi-objective territory design problem is proposed. A?territory design problem consists of partitioning a set of basic units into larger groups that are suitable with respect to some specific planning criteria. These groups must be compact, connected, and balanced with respect to the number of customers and sales volume. The bi-objective commercial territory design problem belongs to the class of NP-hard problems. Previous work showed that large instances of the problem addressed in this work are practically intractable even for the single-objective version. Therefore, the use of heuristic methods is the best alternative for obtaining approximate efficient solutions for relatively large instances. The proposed scatter search-based framework contains a diversification generation module based on a greedy randomized adaptive search procedure, an improvement module based on a relinked local search strategy, and a combination module based on a solution to an assignment problem. The proposed metaheuristic is evaluated over a variety of instances taken from literature. This includes a comparison with two of the most successful multiobjective heuristics from literature such as the Scatter Tabu Search Procedure for Multiobjective Optimization (SSPMO) by Molina et al. (INFORMS J. Comput. 19(1):91?C100, 2007), and the Non-dominated Sorting Genetic Algorithm (NSGA-II) by Deb et?al. (Parallel problem solving from nature ?C PPSN VI, Lecture notes in computer science, vol. 1917, Springer, Berlin, pp.?849?C858, 2000). Experimental work reveals that the proposed procedure consistently outperforms both heuristics, SSPMO and NSGA-II, on all instances tested.     

Multisourcing suppliers selection in service outsourcing

Multisourcing suppliers selection in service outsourcing involves selecting a supplier portfolio with a reasonable number of suppliers and better performance to cover aspiration levels of criteria. It is a specific weighted matching problem with new challenges. This paper proposes a decision method for solving this problem. In the proposed method, different formats of preference information, including numerical values, interval numbers and linguistic variables, are used to express alternative ratings. The technique for order preference by similarity to ideal solution is extended to aggregate the three formats of preference information. A bi-objective 0–1 linear programming model using the aggregated information is built to select a desired supplier portfolio, in which the objectives of minimization of suppliers number and maximization of supplier performance are involved. To solve this model, we transform it into an equivalent, and then an exact multi-objective branch-and-bound algorithm is developed to obtain Pareto-optimal solutions. In addition, a real case of an insurance company is used to illustrate the applicability of the proposed method.     

Robust-based interactive portfolio selection problems with an uncertainty set of returns

This paper considers a robust portfolio selection problem with an uncertainty set of future returns and satisfaction levels in terms of the total return and robustness parameter. Since the proposed model is formulated as an ill-defined problem due to uncertainty and is bi-objective, that is, to maximize both the abovementioned satisfaction levels, it is difficult to solve the model directly without introducing some criterion of optimality for the bi-objective functions. Therefore, by introducing fuzzy goals and an interactive fuzzy satisficing method, the proposed model is transformed into a deterministic equivalent problem. Furthermore, to obtain the exact optimal portfolio analytically, a solution method is developed by introducing the auxiliary problem and performing equivalent transformations. In order to compare the proposed model with previous useful models, numerical examples are provided, and the results show that it is important to maximize the robustness parameter and total return using the interactive process for adjusting investor’s satisfaction levels.