Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming

In this paper, we first refine a recently proposed metaheuristic called “Marriage in Honey-Bees Optimization” (MBO) for solving combinatorial optimization problems with some modifications to formally show that MBO converges to the global optimum value. We then adapt MBO into an algorithm called “Honey-Bees Policy Iteration” (HBPI) for solving infinite horizon-discounted cost stochastic dynamic programming problems and show that HBPI also converges to the optimal value.     

SPAR: stochastic programming with adversarial recourse

We consider a general adversarial stochastic optimization model. Our model involves the design of a system that an adversary may subsequently attempt to destroy or degrade. We introduce SPAR, which utilizes mixed-integer programming for the design decision and a Markov decision process (MDP) for the modeling of our adversarial phase.     

模糊蕴涵与Smooth紧

1985年,Sostak[8]由逻辑的观点出发定义了一种基于Lukasiew icz蕴涵算子的Sm ooth紧,基于同样的思想A.H aydar E s[12]又定义了另外几种Sm ooth(fuzzy)紧并讨论了它们之间的关系。王国俊教授[10]提出了一种比Lukasiew icz算子具有更好性质的R0-算子,基于R0-算子我们定义了一种模糊蕴涵,通过这种模糊蕴涵定义几种Sm ooth紧并讨论了它们的一些性质。     

Solving multiple-objective problems in the objective space

Projection and relaxation techniques are employed to decompose a multiobjective problem into a two-level structure. The basic manipulation consists in projecting the decision variables onto the space of the implicit tradeoffs, allowing the definition of a relaxed multiobjective master problem directly in the objective space. An additional subproblem tests the feasibility of the solution encountered by the relaxed problem. Some properties of the relaxed problem (linearity, small number of variables, etc.) render its solution efficient by a number of methods. Representatives of two different classes of multiobjective methods [the Geoffrion, Dyer, Feinberg (GDF) method and the fuzzy method of Baptistella and Ollero] are implemented and applied within this context to a water resources allocation problem. The results attest the computational viability of the overall procedure and its usefulness for the solution of multiobjective problems.This work was partially sponsored by grants from CNP_q and FAPESP, Brazil. The authors are indebted to the anonymous reviewers for their valuable comments.     

用模糊综合评价值作为单纯形优化指标

本文介绍了一个模糊综合评价方法,讨论了模糊运算方法对运算结果即综合评价值的影响,并将模糊综合评价值用作单纯形优化指标。     

模糊正交法用于石墨炉探针原子化测定铋条件的研究

模糊正交法是通过把正交试验结果模糊化,然后用模糊数学的理论和方法来处理实验数据。与常规正交设计相比,能在同样实验工作量的情况下获得更多的信息。本文采用模糊正交法对元素Bi的测定条件进行优化,并将其同常规正交设计方差分析的结果进行了比较,并提出一个综合评价参数,对建立的分析方法进行了综合评价。     

基于累积前景理论和VIKOR的毕达哥拉斯犹豫模糊风险型多属性决策方法

针对属性权重未知,且属性值为毕达哥拉斯犹豫模糊数(PHFN)的风险型多属性决策问题,考虑到决策者的有限理性行为,提出基于累积前景理论(CPT)和多准则妥协优化解(VIKOR)的决策方法。首先,定义PHFN的分散率,并构建优化模型确定属性权重。其次,将CPT融入PHFN环境,定义PHFN的价值函数,并结合决策权重函数计算方案在各属性下的综合前景值。进一步,构建综合前景值矩阵,在此基础上运用VIKOR法确定方案排序。最后,通过风险投资项目选择的应用案例说明所提方法是可行、有效的。     

改进的蝗虫优化算法在双目标应急物资中心选址问题中的应用

近年来世界各地频发灾情疫情等紧急事件,严重影响人民的生活物资保障。在这种情况下,急需建立应急物资中心来缓解燃眉之急。该类问题通常面临资源稀缺并且时间相对紧迫的处境,因此需要在短时间内获得合理的应急设施选址方案来提升服务的质量和效率。本文对应急物资中心选址问题展开研究,提出一种考虑后续运输成本以及有概率发生紧急事件而导致无法正常运送物资的双目标离散选址模型,并为此设计一种二进制多目标蝗虫优化算法。该算法采用模糊关联熵系数来引导迭代更新,同时为其添加外部档案,最优解选择机制和竞争决策机制来提升算法性能。多次数值实验表明该算法的计算效率和求解质量较高,可作为应急物资中心选址问题的一种可行且有效的算法。     

Learning to align organizational design and data

The relationship between organizational learning and organizational design is explored. In particular, we examine the information processing aspects of organizational learning as they apply to a two-valued decision making task and the relation of such aspects to organizational structure. Our primary contribution is to extend Carley's (1992) model of this process. The original model assumes that all data input into the decision making processes are of equal importance or weight in determining the correct overall organizational decision. The extension described here allows for the more natural situation of non-uniform weights of evidence. Further extensions to the model are also discussed. Such organizational learning performance measures provide an interesting framework for analyzing the recent trend towards flatter organizational structures. This research suggests that flatter structures are not always better, but rather that data environment, ultimate performance goals, and relative need for speed in learning can be used to form a contingency model for choosing organizational structure.