Combining different prioritization methods in the analytic hierarchy process synthesis

A multicriteria approach for combining prioritization methods within the analytic hierarchy process (AHP) is proposed. The leading assumption is that for each particular decision problem and related hierarchy, AHP must not necessarily employ only one prioritization method (e.g. eigenvector method). If more available methods are used to identify the best estimates of local priorities for each comparison matrix in the hierarchy, then the estimate of final alternatives’ priorities should also be the best possible, which is in natural concordance with an additive compensatory structure of the AHP synthesis. The most popular methods for deriving priorities from comparison matrices are identified as candidates (alternatives) to participate in AHP synthesis: additive normalization, eigenvector, weighted least-squares, logarithmic least-squares, logarithmic goal programming and fuzzy preference programming. Which method will be used depends on the result of multicriteria evaluation of their priority vectors’ performance with regard to suggested deviation and rank reversal measures. Two hierarchies with matrices of size 3–6 are used to illustrate an approach.     

Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: A cubic fuzzy-priority matrix design

The aim of this paper is to develop a regime switching design of the fuzzy analytic hierarchy process (FAHP) and to improve its functionality under the choice-varying priority (CVP) problem. In the conventional AHP decision process, priority matrices are identical and their values are invariant for a specific objective. However, in many Multi-Criteria Decision Making (MCDM) problems, the relative importance of criteria may differ according to the choices. A regime switching process is proposed for improving the CVP problem. Under the fuzzy-AHP (FAHP) framework, choice-varying priorities are presented in a cubic matrix form. Another novel contribution is suggested in the prioritization of the level of expert consistency. During the decision-making practice, experts may have different attitudes and their individual matrix consistencies might be superior or inferior in their overall practices. Individual consistency is one of the objective indicators of the quality of judgment. An expert consistency prioritization approach is proposed to deal with the classification of response stability. For the financial risk assessment part of the study, the loss probability of the intended projects is calculated by the fuzzy Monte-Carlo simulation framework.     

利用佳点集遗传算法的白盒测试用例优先排序

在软件演化过程中,测试用例优先排序作为一种高效实用的回归测试技术,对于提高缺陷的早期检测速率和降低测试成本有重要意义。针对传统遗传算法在白盒测试用例优先排序中收敛速度慢和稳定性差的问题,采用佳点集遗传算法求解白盒测试用例优先排序问题。算法根据程序实体覆盖矩阵对个体进行编码,以程序实体覆盖平均百分比作为适应度函数,采用随机抽样选择算子和佳点集交叉算子产生新一代种群。实验选择6个典型的基准开源项目,以语句、分支和方法作为程序实体,实验结果表明佳点集遗传算法收敛速度快、稳定性好,为回归测试提供了一个有效的测试用例优先排序方法,有助于尽早发现软件缺陷,降低测试成本。     

On the location selection problem using analytic hierarchy process and multi-choice goal programming

Location selection is a crucial decision in cost/benefit analysis of restaurants, coffee shops and others. However, it is difficult to be solved because there are many conflicting multiple goals in the problem of location selection. In order to solve the problem, this study integrates analytic hierarchy process (AHP) and multi-choice goal programming (MCGP) as a decision aid to obtain an appropriate house from many alternative locations that better suit the preferences of renters under their needs. This study obtains weights from AHP and implements it upon each goal using MCGP for the location selection problem. According to the function of multi-aspiration provided by MCGP, decision makers can set multi-aspiration for each location goal to rank the candidate locations. Compared to the unaided selection processes, the integrated approach of AHP and MCGP is a better scientific and efficient method than traditional methods in finding a suitable location for buying or renting a house for business, especially under multiple qualitative and quantitative criteria within a shorter evaluation time. In addition, a real case is provided to demonstrate the usefulness of the proposed method. The results show that the proposed method is able to provide better quality decision than normal manual methods.     

RePizer: a framework for prioritization of software requirements

The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Due to time and resource constraints, it is imperative to identify the high-priority requirements that need to be considered first during the software development process. Moreover, existing prioritization frameworks lack a store of historical data useful for selecting the most suitable prioritization technique of any similar project domain. In this paper, we propose a framework for prioritization of software requirements, called RePizer, to be used in conjunction with a selected prioritization technique to rank software requirements based on defined criteria such as implementation cost. RePizer assists requirements engineers in a decision-making process by retrieving historical data from a requirements repository. RePizer also provides a panoramic view of the entire project to ensure the judicious use of software development resources. We compared the performance of RePizer in terms of expected accuracy and ease of use while separately adopting two different prioritization techniques, planning game (PG) and analytical hierarchy process (AHP). The results showed that RePizer performed better when used in conjunction with the PG technique.     

Consistency control and expert consistency prioritization for FFTA by using extent analysis method of trapezoidal FAHP

The purpose of this study is to enable consistency control along with expert consistency prioritization for direct fuzzy inputs as basic events (BEs) assigned to the fault tree analysis (FTA) method. In the recent literature, fuzzy fault tree analysis (FFTA) applications have no consistency check for the expert judgments. In this study, as a multi criteria decision making method, fuzzy analytic hierarchy process (FAHP) is applied for checking the consistency by centric consistency index (CCI). Expert consistency prioritization is also implemented for FFTA by using extent analysis method of trapezoidal FAHP. An analytic comparison between with and without consistency control is obtained. The numerical results for collapse of an offshore platform are presented to illustrate the applicability of the approach.     

Tool-supported requirements prioritization: Comparing the AHP and CBRank methods

Requirements prioritization aims at identifying the most important requirements for a software system, a crucial step when planning for system releases and deciding which requirements to implement in each release. Several prioritization methods and supporting tools have been proposed so far. How to evaluate their properties, with the aim of supporting the selection of the most appropriate method for a specific project, is considered a relevant question.In this paper, we present an empirical study aiming at evaluating two state-of-the art tool-supported requirements prioritization methods, AHP and CBRank. We focus on three measures: the ease of use, the time-consumption and the accuracy. The experiment has been conducted with 23 experienced subjects on a set of 20 requirements from a real project. Results indicate that for the first two characteristics CBRank overcomes AHP, while for the accuracy AHP performs better than CBRank, even if the resulting ranks from the two methods are very similar. The majority of the users found CBRank the “overall best” method.     

A neural network approach to decision alternative prioritization

A common decision problem faced by managers in organizations is that of decision alternative prioritization. There have been many proposed approaches to the problem where the decision maker constructs a pairwise comparison matrix of the alternatives under study. All existing ranking methods possess major shortcomings for the general problem. This paper illustrates the usefulness of a neural network model in such prioritization problems, which considers these shortcomings of previous methods. Use of the model is shown through the use of example ranking scenarios.     

A systematic literature review on requirement prioritization techniques and their empirical evaluation

[Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete. [Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019. [Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches. [Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well.     

基于层次分析法的牙齿隐形正畸方案评价与优选

随着科学技术的发展,牙齿正畸软件系统可以自动生成若干正畸方案。针对医患如何从中选择 最满意方案的问题,提出基于层次分析法的牙齿隐形正畸方案综合评价与优选方法。结合牙齿正畸医师常用的 正畸标准与患者的基本需求,从咬合、美观和正畸代价 3 个维度考虑,建立了咬合接触情况、牙列拥挤度、牙 弓对称度、牙齿移动难度、正畸周期及费用等 11 个指标的牙齿隐形正畸方案优选层次模型。利用层次分析法 构建成对比较矩阵,通过一致性检验,确定各指标及各方案权重,计算综合权重并排序得出最优方案。结合实 际案例,对不同案例进行实验,同时引入专家评分法进行实验对比,结果表明,基于层次分析法的综合评价可 为医患选择牙齿隐形正畸方案提供有效参考。     

基于粒子群算法的模糊层次分析模型

针对模糊层次分析法中存在的模糊判断矩阵一致性检验和修正困难、元素权重计算繁琐的问题,从模糊判断矩阵的定义角度出发,构建了基于粒子群算法的模糊层次分析模型(PSO-FAHP),提出了包含模糊判断矩阵一致性修正及各元素排序过程的非线性带约束优化问题,引入粒子群算法实现了问题的求解,并分析了模型的合理性。最后通过数值算例对比了模型的计算结果,验证了模型的正确性。对模糊层次分析法的实践应用具有一定的指导意义。     

FAHP在软件项目风险优先级排序中应用

风险优先级排序是软件项目风险管理的重要环节。针对当前风险排序方法的不足,提出一种基于模糊层次分析法的风险排序方法。该方法引入层次分析法对风险事件进行分层,通过三角模糊数来表征专家判断信息,同时借助模糊风险判断矩阵实现了风险事件按风险发生概率、风险损失、风险不可控制性以及综合考虑三方面准则的优先级排序。最后,通过案例分析验证了该方法的可操作性和合理性。     

Test-case prioritization: achievements and challenges

Test-case prioritization, proposed at the end of last century, aims to schedule the execution order of test cases so as to improve test effectiveness. In the past years, test-case prioritization has gained much attention, and has significant achievements in five aspects: prioritization algorithms, coverage criteria, measurement, practical concerns involved, and application scenarios. In this article, we will first review the achievements of test-case prioritization from these five aspects and then give our perspectives on its challenges.     

LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing

Providing required level of service quality in cloud computing is one of the most significant cloud computing challenges because of software and hardware complexities, different features of tasks and computing resources and also, lack of appropriate distribution of tasks in cloud computing environments. The recent research in this field show that lack of smart prioritization and ordering of tasks in scheduling (as an NP-hard problem) has been very effective and resulted in lack of load balancing, response time increase, total execution time increase and also, average resource use decrease. In line with this, the proposed method of this research called LATOC considered first the key criteria of an input task like required processing unit, data length of task and execution time. Then, it addressed task prioritization in separate queues using the technique for order preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) in figure of a hybrid intelligent algorithm (AHP-TOPSIS). Each ordered task in separate priority queues was placed based on its priority level, and then, to assign each task from each priority queue to virtual machines, optimized particle swarm optimization was used. Many simulations based on various scenarios in Cloudsim simulator show that smart assignment of prioritized tasks by LATOC resulted in improvement of important cloud computing parameters such as total execution time and average resource use comparing similar methods.

     

两阶段设备维护策略选择的灰靶决策模型

针对设备的最佳维护策略选择问题,首先提出6种设备运维目标,同时给出两阶段设备运维策略选择的加权智能灰靶决策模型的架构图以及建模算法流程;然后采用德尔菲调查法与层次分析法相结合确定不同目标的权重.结合某半导体面板制造企业的设备运行现场实际数据,对于成本型目标和适中型目标,分别采用相应的效果测度函数计算出设备在不同运行状态下的一致效果测度矩阵以及综合效果测度矩阵.通过运用两阶段设备维护的灰靶决策模型,最终得到设备不同状态下的最佳维护策略.所提出方法对正确选择半导体面板设备维护策略、提高设备运维效率、降低维护成本具有实际指导意义.     

Supplier selection using AHP methodology extended by D numbers

Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts’ assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being’s subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method.     

直觉模糊层次分析法处理医院用药风险评价

基于直觉模糊集的模糊逼近理论,给出了将直觉模糊互补判断矩阵转换为模糊逼近矩阵的方法,提出了直觉模糊环境下的AHP方法,简称为直觉模糊层次分析法,将其应用于对医疗机构的用药风险的评价问题,给出了各子风险类别的权重。经检验,是一种实用性较强的医疗机构用药风险评价方法。     

基于离散粒子群算法的测试用例优先排序

测试用例优先排序技术能够有效提高回归测试效率,是软件测试的热点研究课题之一。针对基于需求的测试用例优先排序方法可操作性差的问题,提出了一种改进的基于测试点覆盖和离散粒子群优化算法的求解方法（TCP-DPSO）。首先,把影响排序的各种因素分为测试收益型因素和测试成本型因素两大类,通过加权平均的方式进行归一化,得到基于需求的通用测试平均收益率评价指标;然后,利用交换子和基本交换序列定义粒子的位置和速度,借鉴遗传算法（GA）变异策略引入变异算子,采用时变惯性权重调整粒子的探索能力和开发能力,促进可持续进化和逼近优化目标。实验结果表明,TCP-DPSO在最优解质量上与遗传算法相当,大幅优于随机测试,在最优解成功率和平均求解时间上优于遗传算法,具有更好的算法稳定性。     

An intelligent approach to supplier evaluation in automotive sector

During the process of supplier evaluation, selecting the best desirable supplier is one of the most critical problems of companies since improperly selected suppliers may cause losing time, cost and market share of a company. For this multiple-criteria decision making selection problem, in this paper, a fuzzy extension of analytic network process (ANP), which uses uncertain human preferences as input information in the decision-making process, is applied since conventional methods such as analytic hierarchy process cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. The resulting fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgments. In short, in this paper, an intelligent approach to supplier selection problem through fuzzy ANP is proposed by taking into consideration quantitative and qualitative elements to evaluate supplier alternatives, and a case study in automotive sector is presented.     

基于FAHP的深水半潜式平台钻井设备选型

钻井设备选型的合理性关系着平台的总体性能。将模糊层次分析法(FAHP)应用于深水半潜式平台钻井设备的选型,以钻井泵为例,综合考虑技术性和经济性两方面建立钻井泵选型的评价模型。分别利用 FAHP 和AHP计算出各因素的权重,对备选方案进行综合评价。结果表明,模糊层次分析法能有效地解决钻井设备选型问题。通过对比分析两种方法的结果,总结出二者的适用范围,为决策者综合运用两种分析方法奠定了基础。