GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition

The problem of QoS-aware Web service composition (QWSC), i.e., how to select from a pool of candidate services to construct a composite service with the best overall QoS performance, is an NP-hard problem. To address a large-scale QWSC problem, a novel method is proposed based on information theory, multi-attribute decision making (MADM) and genetic algorithm. To capture complex judgments, the QWSC problem is formulated into a MADM representation which aims to find acceptable solutions assessed by multiple QoS attributes with varying distributions. To solve the MADM problem for QWSC, each QoS attribute is weighted in both a priori, subjective perspective and a posteriori, information-based perspective based on the discriminative capability of QoS attributes for a dynamic pool of candidate services. Furthermore, to solve the large-scale QWSC problem that conventional MADM methods cannot navigate, we develop a GACRM algorithm by integrating genetic algorithm (GA) with Compromise Ratio Method (CRM). Experiments demonstrate that GACRM obtains nearly the same solution ranking by the CRM but scales much better in terms of computation time for large-scale QWSC problems.     

Evaluation of multi-attribute decision making systems applied during the concept design of new microplasma devices

Various multi-attribute decision making (MADM) systems can be implemented to narrow a field of new concept designs down to those with high likelihoods of surpassing state-of-the-art technologies. This research investigated the conceptual design phase of new microplasma devices in order to create metrics that evaluate the efficiency, effectiveness, and overall utility of representative MADM systems studied in previous engineering design applications. Device attributes and concept alternatives for the microplasma devices were identified from open-ended expert surveys. Efficiency metrics were defined based on the number of manual user inputs. Published device literature and testing were used to gauge how closely device concepts satisfied multi-attribute criteria, forming the basis of an effectiveness metric. A weighted average of the efficiency and effectiveness defined a MADM system’s overall utility. Varying the effectiveness weight provided further insight into the conditions under which particular MADM approaches exhibited higher utility values. The MADM systems found to possess the highest overall quantified utilities were based on Pugh’s controlled convergence, Utility Based Axiomatic Framework, and Grey Relational Analysis. The MADM method with the lowest overall utility was the analytical hierarchy process. These findings indicate that consensus building and utility-based MADM systems are especially helpful to engineering design teams during the early design phases of novel technologies when resources are constrained or historical data is limited.     

A two phase multi-attribute decision-making approach for new product introduction

This study aims at improving the quality and effectiveness of decision-making in new product introduction. New product development has long been recognized as one of the corporate core functions to be competitive on an increasingly competitive global market. However, developing new products is a process involving risk and uncertainty. In order to solve this stochastic problem, companies need to evaluate their new product initiatives carefully and make accurate decisions. For this reason, a systematic decision process for selecting more rational new product ideas is proposed. Basically, two stages of decision-making are described: the identification of nondominated new product candidates and the selection of the best new product idea. These stages are composed of an integrated approach based on a fuzzy heuristic multi-attribute utility method and a hierarchical fuzzy TOPSIS method. Finally, an application is given to demonstrate the potential of the methodology.     

Multi-attribute group decision making model under the condition of uncertain information

Owing to the increasing complexity in the construction management, integrating experts' knowledge and experiences to make appropriate decisions is a commonly used method. TOPSIS (technique for order performance by similarity to ideal solution) is a practical and useful technique in dealing with multi-attribute decision making problems, and has been widely employed in the construction management and other fields. The modification and extension of TOPSIS to a group decision environment is investigated in this study. In the proposed group decision making model, we both adopt the Minkowski distance function to solve the over-weighted problem in the original TOPSIS technique, the grey number operations to deal with the problem of uncertain information, and the aggregation approach to integrate experts' evaluations. Finally, an illustrative example of subcontractor selection is used to demonstrate the feasibility and practicability of the proposed model.     

供应商参与下的产品方案多目标优化与多属性决策规划方法

针对现有供应商参与下的产品方案规划方法在零部件优化和供应商评价等两方面所存在的局限性,提出零部件多目标优化和供应商模糊动态多属性决策评价相集成的二阶段规划方法。建立以产品零部件组合方案的质量、成本和交货期等为目标的数学优化模型,以Epsilon策略和主导性约束处理机制对强度Pareto多目标进化算法进行适应性改进后用于对该优化模型进行求解并获得有限数量的Pareto最优解。引入傅里叶级数对伯努利预测模型的误差进行修正来提高预测精度,将修正的伯努利预测模型和模糊动态多属性决策模型相结合用于对Pareto最优解中包含的各零部件供应商进行评价,进而确定出各零部件的最佳供应商和最终的产品规划方案。以供应商参与下的空分设备研发过程作为实例,进行数值仿真计算,结果表明所提出方法具有可行性与有效性。     

基于多属性决策的气动隐身多目标优化

针对多目标优化结果排序与选择的多属性决策(Multi-attribute decision making,MADM)问题,将多目标优化与MADM相结合,提出基于MADM的多目标优化方法,并将该方法应用于跨声速前掠翼(Forward-swept wing,FSW)气动隐身多目标优化中,优化结果提高了跨声速FSW的气动和隐身性能。采用类别形状函数变换法(Class-shape function transformation,CST)方法对翼型几何外形进行描述,实现FSW气动和隐身多学科优化设计模型的参数化描述。建立基于N-S方程的计算流体力学方法的FSW气动分析模型和基于矩量法的计算电磁学方法的FSW隐身分析模型。将Pareto多目标遗传算法得到的Pareto非劣解集构成MADM矩阵,采用基于模糊熵权的改进的逼近理想解的排序法(Modified technique for order preference by similarity to ideal solution,M-TOPSIS)方案评价方法进行Pareto非劣解排序,最终确定最佳的Pareto非劣解。研究结果验证了所提出方法的有效性,为多目标优化问题提供了一种新的解决途径。     

基于直觉模糊多属性群决策的压裂风险排序

针对目前低渗透油气藏压裂技术风险评估的不确定性及方法的单一性。应用直觉三角模糊数理论和多属性群决策理论,建立一种新的压裂风险排序模型,提高了风险排序的准确度。首先,确定压裂风险影响指标体系和风险因素的属性。其次,收集专家对压裂风险影响指标在各风险属性下的判断信息,并提出一种新的确定直觉模糊数中隶属度和非隶属度的方法,且应用该方法将判断信息转换为相应的三角模糊数和直觉三角模糊数。然后,确定各风险属性的熵权并确定不同方案的风险综合值;最后,利用逼近理想解法和直觉模糊数距离排序法对不同方案的风险综合值进行排序,并给出两种方法的比较。结果表明模型是有效的,并且有一定的实际应用价值。     

矿产资源综合开发利用评价的一种新的多属性决策法

矿产资源综合开发利用评价是一个典型的多目标决策问题。本文在分析有关方法在矿产资源综合利用评价中的不足的同时，采用了一种新的多属性决策法，从而使结论更加科学和可靠。     

多属性选择专家系统的启发式策略

在许多场合,诸如设计、测试等领域都会遇到与多个属性相关的选择问题.我们研制了一个用于解决多属性选择问题的专家系统,其作法是以多属性效益量度的评优函数为基础,采用了启发式的选择策略将属性要求与评优分值有机地结合起来帮助用户决策.在选择过程中,利用了用户反馈来自动调整评优函数的评价机制.     

密度加权平均中间算子及其在多属性决策中的应用

针对已有的信息集结算子没有考虑属性信息分布疏密程度的情况,研究了一维数据聚类的问题,并给出一种一维聚类的方法.在此基础上,开发了密度加权平均（DWA）中间算子,并将该算子与已有的5种信息集结算子进行合成,得到的合成算子兼顾了多种算子的特点.最后通过一个算例验证了DWA算子及合成算子的一些新颖的特征.