Microextraction of volatile compounds from wine samples and their determination by GC-FID. The effect of the salts and extraction solvents used

Summary This study describes the optimization of microextraction as a method for extracting volatile compounds from wine. The study has been applied to twelve compounds present in wine: 3-methylbutyl acetate, 3-methyl-1-butanol, ethyl hexanoate, hexanol, ethyl octanoate, butanoic acid, ethyl decanoate, diethyl succinate, hexanoic acid, phenylethanol, octanoic acid, and decanoic acid. These compounds were selected from those identified by GC-MS analysis of a real wine sample. By means of a synthetic wine, the study investigated the influence on extraction yield of the solvents and salts used, the proportion of salts, and the agitation time. The determination was performed by GC with flame ionization detection and an internal standard was used for quantification. The method was applied to samples of white wine from La Rioja.     

Decision making under uncertainty with fuzzy targets

This paper discusses the issue of how to use fuzzy targets in the target-based model for decision making under uncertainty. After introducing a target-based interpretation of the expected value on which it is shown that this model implicitly assumes a neutral behavior on attitude about the target, we examine the issue of using fuzzy targets considering different attitudes about the target selection of the decision maker. We also discuss the problem for situations on which the decision maker’s attitude about target may change according to different states of nature. Especially, it is shown that the target-based approach can provide an unified way for solving the problem of fuzzy decision making with uncertainty about the state of nature and imprecision about payoffs. Several numerical examples are given for illustration of the discussed issues.     

Multicriteria tour planning for mobile healthcare facilities in a developing country

A multiobjective combinatorial optimization (MOCO) formulation for the following location-routing problem in healthcare management is given: For a mobile healthcare facility, a closed tour with stops selected from a given set of population nodes has to be found. Tours are evaluated according to three criteria: (i) An economic efficiency criterion related to the tour length, (ii) the criterion of average distances to the nearest tour stops corresponding to p-median location problem formulations, and (iii) a coverage criterion measuring the percentage of the population unable to reach a tour stop within a predefined maximum distance. Three algorithms to compute approximations to the set of Pareto-efficient solutions of the described MOCO problem are developed. The first uses the P-ACO technique, and the second and the third use the VEGA and the MOGA variant of multiobjective genetic algorithms, respectively. Computational experiments for the Thiès region in Senegal were carried out to evaluate the three approaches on real-world problem instances.     

Handling selection bias when choosing actions in retail credit applications

In many situations one needs to know which action one should take with a customer to yield the greatest response. Typically, estimates of the response functions of different actions will be based on the responses of customers previously assigned to each action. Often, however, the previous assignments will not have been random, so that estimates of the response functions will be biased. We examine the case of two possible actions. We look at the error arising from using the simple OLS estimate ignoring the selection bias, and also explore the possibility of using the Heckman model to allow for the sample selectivity. The performance of Heckman’s model is then compared with the simple OLS through simulation.     

A chi-square method for obtaining a priority vector from multiplicative and fuzzy preference relations

Decision makers (DMs)’ preferences on decision alternatives are often characterized by multiplicative or fuzzy preference relations. This paper proposes a chi-square method (CSM) for obtaining a priority vector from multiplicative and fuzzy preference relations. The proposed CSM can be used to obtain a priority vector from either a multiplicative preference relation (i.e. a pairwise comparison matrix) or a fuzzy preference relation or a group of multiplicative preference relations or a group of fuzzy preference relations or their mixtures. Theorems and algorithm about the CSM are developed. Three numerical examples are examined to illustrate the applications of the CSM and its advantages.     

Sensitivity analysis for product design selection with an implicit value function

In product design selection the decision maker (DM) often does not have enough information about the end users’ needs to state the “preferences” with precision, as is required by many of the existing selection methods. We present, for the case where the DM gives estimates of the preferences, a concept for calculating a “robustness index.” The concept can be used with any iterative selection method that chooses a trial design for each iteration, and uses the DM’s preference parameters at that trial design to eliminate some design options which have lower value than the trial design. Such methods, like our previously published method, are applicable to cases where the DM’s value function is implicit. Our robustness index is a metric of the allowed variation between the actual and estimated preferences for which the set of non-eliminated trial designs (which could be singleton) will not change. The DM, through experience, can use the robustness index and other information generated in calculating the index to determine what action to take: make a final selection from the present set of non-eliminated designs; improve the precision of the preference estimates; or otherwise cope with the imprecision. We present an algorithm for finding the robustness index, and demonstrate and verify the algorithm with an engineering example and a numerical example.     

关于群体决策的偏比映射

本文引进群体决策的决策个体和决策群体关于供选方案对的偏比度,以及偏比泛函概念.由此,定义了群体在供选方案集上的偏比映射,构造了相应的偏比公理系,并且建立了关于群体决策的偏比度分析的基本理论.最后,给出一个利用偏比映射进行群体排序的方法.特别是,文中表明,满足本文所提出的所有偏比公理的偏比映射是存在的.     

群众决策的ακ-较多规则

对于群体决策问题,本文给出一个新的带参数的ακ-较多规则,并且讨论了在此规则下个体选优解和群体选优解的关系。此外,还论证了相应的ακ-较多规则满足Arrow公理的情况。     

一种基于决策矩阵的DST-AHP多属性决策方法

针对层次分析法决策时存在两两判断、一致性检验次数过多和判断矩阵残缺性等问题,本文提出了一种基于决策矩阵的DST-AHP多属性决策方法。该方法结合决策矩阵的特征值,构建DST-AHP方法层次结构模型和判断矩阵,并根据判断矩阵定义不同属性下各焦元的基本概率分配函数;然后利用Dempster合成法则对基本概率分配函数值进行合成,依据合成后值对方案进行排序。最后对AHP和DST-AHP两种方法进行比较分析,说明该方法的有效性。     

混合互补判断矩阵一致性研究

给出混合互补判断矩阵一致性的定义和判别加性一致性的方法.定义了核算子、核矩阵,对带有精确数、三角模糊数和梯形模糊数的混合互补判断矩阵给出基于核矩阵的一致性调整方法,调整量可以是精确数也可以是模糊数.最后给出一个应用实例.