软件开发团队的多目标优化构建研究

分析了客户需求与候选成员能力的关系,使用模糊排序聚类算法得到专业领域分工的集群;同时依据迈尔斯—布里格斯性格类型指标得到候选成员协作关系的量化评估。建立了以成员综合能力和性格匹配度最大化为目标的团队构建模型。最后,结合一个具体案例,采用带有判断与修复算子的微粒群算法对模型进行求解,得到表示团队构建候选方案集合的帕累托解,从而验证了该优化模型及算法的有效性和实用性。     

基于熵权Vague集的多目标决策方法

针对目前基于Vague集的多目标决策中目标权重的主观任意性以及评价函数的单一性问题,提出一种基于熵权Vague集的多目标决策方法。首先,将决策矩阵转化为目标优属度矩阵,再利用熵权系数法计算各个目标的客观权重,综合考虑客观权重和主观权重得到各个目标的权重向量区间;然后,通过计算方案的支持目标集、反对目标集和中立目标集得出方案的Vague估计值;最后,定义新的评价函数对方案进行排序并选出最优方案。通过算例验证了所提方法的合理性和有效性。     

欧氏范数的Vague多目标决策

在Vague多目标决策的研究中,引入欧氏范数,建立加权向量概念,通过计算和比较各候选方案与理想方案间的几何偏差来确定最优方案。算例验证了该方法的有效性和可行性。     

Cognitive biases and time stress in team decision making

This experiment investigates the impart of time stress on the decision making performance of command and control teams. Two person teams were trained to execute a set of simple decision procedures. Some of these procedures required subjects to make judgments that were inconsistent with normal heuristic decision processing. The principal hypothesis was that these decision procedures would be vulnerable-to-bias, and would therefore be more vulnerable to the effects of time stress than other decision procedures. The results support this hypothesis. In addition, the results suggest that the subjects adapted inappropriately to time stress. As time stress increased, they began to use a decision processing strategy that was less effective than the strategy they were trained to use     

基于灰色关联分析的Vague多目标决策

在Vague多目标决策的研究中,引入灰色关联分析法,通过计算和比较各候选方案与理想方案的关联度来确定最优方案,该方法具有全面性和简便性的特点。所举的例子验证了该方法的有效性和简便性。     

Simulation-based evaluation of defuzzification-based approaches to fuzzy multiattribute decision making

This paper presents a simulation-based study to evaluate the performance of 12 defuzzification-based approaches for solving the general fuzzy multiattribute decision-making (MADM) problem requiring cardinal ranking of decision alternatives. These approaches are generated based on six defuzzification methods in conjunction with the simple additive weighting (SAW) method and the technique for order preference by similarity to the ideal solution method. The consistency and effectiveness of these approaches are examined in terms of four new objective performance measures, which are based on five evaluation indexes. The simulation result shows that the approaches, which are capable of using all the available information on fuzzy numbers effectively in the defuzzification process, produce more consistent ranking outcomes. In particular, the SAW method with the degree of dominance defuzzification is proved to be the overall best performed approach, which is followed by the SAW method with the area center defuzzification. These findings are of practical significance in real-world settings where the selection of the defuzzification-based approaches is required in solving the general fuzzy MADM problems under specific decision contexts.     

基于环境选择和配对选择的多目标粒子群算法

为了使多目标粒子群算法中种群粒子能够快速地收敛于怕累托最优边界,针对标准多目标粒子群算法中缺乏粒子评价标准以及种群个体历史最优值位置和全局最优值位置选择问题,提出了一种基于环境选择和配对选择策略的多目标粒子群算法.该算法在每次迭代时,采用SPEA2中的环境选择和配对选择策略及适应度值计算方法,以此来提高种群粒子之间的信息交换力度,减少标准多目标粒子群算法中大量的随机性,使种群粒子能够更快速地收敛于怕累托最优边界.经典测试函数的仿真实验结果表明,在标准多目标粒子群算法中运用SPEA2的环境选择、配对选择策略和适应度值计算方法,能够使种群粒子更快速地收敛于帕累托最优边界,验证了算法改进的可行性和有效性.     

Multi-objective decision making model under fuzzy random environment and its application to inventory problems

In this paper, we concentrate on developing a fuzzy random multi-objective model about inventory problems. By giving some definitions and discussing some properties of fuzzy random variable, we design a method of solving solution sets of fuzzy random multi-objective programming problems. These are applied to numerical inventory problems in which all inventory costs, purchasing and selling prices in the objectives and constraints are assumed to be fuzzy random variables in nature, and then the impreciseness of fuzzy random variables in the above objectives and constraints are transformed into fuzzy variables which are similar trapezoidal fuzzy numbers. The exact parameters of fuzzy membership function and probability density function can be obtained through fuzzy random simulating the past dates. By comparing the results with those from the fuzzy multi-objective models, we believe that the proposed fuzzy random multi-objective model and hybrid intelligent algorithm provide significant solutions to construct other inventory models with fuzzy random variables in real life.     

A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection

Farmers in regions experiencing water stress or drought conditions can struggle to balance their crop portfolios. Periods of low precipitation often lead to increased, unsustainable reliance on groundwater-supplied irrigation. As a result, regional water management agencies place limits on the amount of water which can be obtained from groundwater, requiring farmers to reduce acreage for more water-intensive crops or remove them from the portfolio entirely. Real-time decisions must be made by the farmer to ensure viability of their farming operation and reduce the impacts associated with limited water resources. Evolutionary algorithms, coupled with accurate, flexible, realistic simulation tools, are ideal mechanisms to allow farmers to assess scenarios with regard to multiple, competing objectives. In order to effective, however, one must be able to select among a variety of simulation tools and optimization algorithms. Many simulation tools allow no access to the source code, and many optimization algorithms are now packaged as part of a suite of tools available to a user. In this work, we describe a framework for integrating these different software components using only their associated input and output streams. We analyze our strategy by coupling a multi-objective genetic algorithm available in the DAKOTA optimization suite (developed and distributed by Sandia National Laboratory) with the MODFLOW-FMP2 simulation tool (developed and distributed by the United States Geological Survey). MODFLOW-FMP2 has been used extensively to model hydrological and farming processes in agriculture-dominated regions, allowing us to represent both farming and conservation interests. We evaluate our integration by considering a case study related to planting decisions facing farmers experiencing water stress. We present numerical results for three competing objectives associated with stakeholders in a given region (i.e., profitability, meeting demand targets, and water conservation). The data obtained from the optimization are robust with respect to algorithmic parameter choices, validating the ability of the associated evolutionary algorithm to perform well without expert guidance. This is integral to our approach, as a motivation for this work is providing decision-making tools. In addition, the results from this study demonstrate that output from the chosen evolutionary algorithm provides a suite of feasible planting scenarios, giving farmers and policy makers the ability to compromise solutions based on realistic simulation data.     

Advanced machine learning approaches to personalise learning: learning analytics and decision making

The aim of the paper is to present methodology to personalise learning using learning analytics and to make further decisions on suitability, acceptance and use of personalised learning units. In the paper, first of all, related research review is presented. Further, an original methodology to personalise learning applying learning analytics in virtual learning environments and empirical research results are presented. Using this learning personalisation methodology, decision-making model and method are proposed to evaluate suitability, acceptance and use of personalised learning units. Personalised learning units evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify learning components’ suitability to particular students’ needs according to their learning styles. In the paper, there are also examples of implementing the methodology using different weights of evaluation criteria. This methodology is applicable in real life situations where teachers have to help students to create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.     

An interactive evolutionary multi-objective optimization and decision making procedure

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade of research and application, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study uses many year’s of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as a aggregate task of optimization and decision-making.     

实时格斗游戏的智能决策方法

格斗游戏作为实时双人零和对抗博弈的代表性问题,具有实时对抗和快速响应的重要研究特性.相应针对性方法的提出有效反映了游戏人工智能领域的重要研究进展及发展方向.本文以格斗游戏人工智能竞赛作为研究背景,将智能决策方法分为启发式规则型、统计前向规划型与深度强化学习型三大类型,介绍相应的智能决策方法在实时格斗游戏中的研究进展.为分析格斗游戏智能决策方法的表现性能,本文提出了胜率、剩余血量、执行速率、优势性和伤害性的5个性能因子,系统分析智能决策方法的性能优势及不足.最后,对未来的在格斗游戏中研究发展趋势进行展望.     

Group decision making with linguistic preference relations with application to supplier selection

Linguistic preference relation is a useful tool for expressing preferences of decision makers in group decision making according to linguistic scales. But in the real decision problems, there usually exist interactive phenomena among the preference of decision makers, which makes it difficult to aggregate preference information by conventional additive aggregation operators. Thus, to approximate the human subjective preference evaluation process, it would be more suitable to apply non-additive measures tool without assuming additivity and independence. In this paper, based on λ-fuzzy measure, we consider dependence among subjective preference of decision makers to develop some new linguistic aggregation operators such as linguistic ordered geometric averaging operator and extended linguistic Choquet integral operator to aggregate the multiplicative linguistic preference relations and additive linguistic preference relations, respectively. Further, the procedure and algorithm of group decision making based on these new linguistic aggregation operators and linguistic preference relations are given. Finally, a supplier selection example is provided to illustrate the developed approaches.     

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast.The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.     

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.     

An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.     

Visual optimization and decision making system for android robot soccer

Microsystem Technologies - The paper developed a visual optimization algorithm to ameliorate the problems encountered by androids on the soccer field involving a visual prediction path that can...