AHP Is Intended for More Than Expected Value Calculations

In a previous paper, Schoner and Wedley [9] presented a way to automatically compute the importance of criteria from the values of alternatives. The following discussion illustrates that the approach of [9] can only be applied if the alternatives under the criteria are measured with a single absolute scale. However, when this does occur, in part or in all of a decision problem, the approach is extraneous. The measurements can (and should) first be combined with the appropriate arithmetic of that scale which reduces that part of the problem to a single dimension and the result can be taken up with the remaining dimensions for consideration with AHP.     

Two issues in the evaluation of job-shop schedules

Two issues are examined concerning the evaluation of job-shop schedules. First, machine and labor cost/utilization are suggested as alternatives to physical utilization criteria. Secondly, an exploration is made of the use of Kiviat Charts as a means of integrating multidimensional criteria for the overall evaluation of the quality of job shop schedules.     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

信息系统外包决策的AHP/PROMETHEE方法

针对信息系统外包项目优选这一重要问题,在已有方法的基础上,结合层次分析法与偏好顺序结构评估法,提出了一种基于这两种方法相结合的信息系统外包项目选择决策方法。以管理、战略、技术、经济、质量与风险6项因素作为评价准则,用层次分析法确定信息系统外包项目选择问题的层次结构与评价准则的权重,用偏好顺序结构评估法确定信息系统外包项目的排序,并通过算例说明该方法的有效性。     

Best-worst multi-criteria decision-making method

In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.     

Third-party logistics selection problem: A literature review on criteria and methods

This paper presents a literature review on Third-Party Logistics (3PL) selection decision in terms of criteria and methods. Based on the analysis of 67 articles published within 1994–2013 period, this review reveals that 3PL selection is empirical in nature and is related to a region/country, industrial sector, and logistics activities outsourced. In terms of 3PL selection criteria, 11 key criteria are identified; each one is defined by a set of attributes. Cost is the most widely adopted criterion, followed by relationship, services, and quality. In terms of methods for 3PL evaluation, they can be categorized in 5 groups, namely: MCDM techniques, statistical approaches, artificial intelligence, mathematical programming, and hybrid methods.     

META-DESIGN CONSIDERATIONS IN DEVELOPING MODEL MANAGEMENT SYSTEMS*

This paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge-representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem-solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems. In order to discuss issues pertinent to automatic modeling, a graph-based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.     

PAHAP: A Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences

In this paper, we present a Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences (PAHAP), a new method for solving discrete alternative multicriteria decision problems. Following the Analytic Hierarchy Process (AHP), PAHAP uses pairwise preference judgments to assess the relative attractiveness of the alternatives. By first aggregating the pairwise judgment ratios of the alternatives across all criteria, and then synthesizing based on these aggregate measures, PAHAP determines overall ratio scale priorities and rankings of the alternatives which are not subject to rank reversal, provided that certain weak consistency requirements are satisfied. Hence, PAHAP can serve as a useful alternative to the original AHP if rank reversal is undesirable, for instance when the system is open and criterion scarcity does not affect the relative attractiveness of the alternatives. Moreover, the single matrix of pairwise aggregated ratings constructed in PAHAP provides useful insights into the decision maker's preference structure. PAHAP requires the same preference information as the original AHP (or, altematively, the same information as the Referenced AHP, if the criteria are compared based on average (total) value of the alternatives). As it is easier to implement and interpret than previously proposed variants of the conventional AHP which prevent rank reversal, PAHAP also appears attractive from a practitioner's viewpoint.     

RANK GENERATION,PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS

Decision making has the objective of finding the best alternative or set of alternatives by considering a number of goals, objectives, criteria, competitors, and other important factors. The analytic hierarchy process is a decision aid used to assist a decision maker in sorting out the complexity of a decision problem and making use of his or her judgments. A decision maker must be assured that the arithmetic operations of any such decision process are the right ones—that they surface the correct ranking and values of the alternatives and preserve or alter ranks appropriately when new alternatives are added or deleted. In this paper it will be shown that with absolute measurement, rank always is preserved, with relative measurement, rank changes with nspect to scveral criteria only because of the structural dependence (involving both numbers and measurements) of criteria on alternatives. A discussion of the effect on rank of replicas and near replicas of the alternatives also is given.     

Estimation of True Salmonella Prevalence Jointly in Cattle Herd and Animal Populations Using Bayesian Hierarchical Modeling

The Finnish salmonella control program (FSCP) for beef production is based on both randomized and selective testing of herds and animals. Sampling of individual animals in abattoirs is randomized. Herds are selectively tested for salmonella on the basis of clinical symptoms and/or other factors. Risk assessment of FSCP is inherently difficult due to the complexity of the complete data set, especially if the detailed labeling of the testing types is lost. However, for a risk assessment of the whole production chain, methods for exploiting all available data should be considered. For this purpose, a hierarchical Bayesian model of true salmonella prevalence was constructed to combine information at different levels of aggregation: herds in geographical regions and individual animals arriving for slaughter. The conditional (municipality specific) probability of selection of a herd for testing was modeled given the underlying true infection status of the herd and information about the general sampling activity in the specific region. The model also accounted for the overall sensitivity of the sampling methods, both at the herd and at the animal level. In 1999, the 95% posterior probability intervals of true salmonella prevalence in the herd population, in individual cattle, and in slaughter animal populations were [0.54%, 1.4%] (mode 0.8%), [0.15%, 0.39%] (mode 0.2%), and [0.12%, 0.36%] (mode 0.2%), respectively. The results will be further exploited in other risk assessments focusing on the subsequent parts of the beef production chain, and in evaluation of the FSCP.     

A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy

Breast cancer is the leading cause of cancer deaths among women. The selection of an effective, patient-specific treatment plan for breast cancer has been a challenge for physicians because the decision process involves a vast number of treatment alternatives as well as treatment decision criteria, such as the stage of the cancer (e.g., in situ, invasive, metastasis), tumor characteristics, biomarker-related risks, and patient-related risks. Furthermore, every patient's case is unique, requiring a patient-specific treatment plan, while there is no standard procedure even for a particular stage of the breast cancer. In this paper, we first determine a comprehensive set of criteria for selecting the best breast cancer therapy by interviewing medical oncologists and reviewing the literature. We then present two analytical hierarchy process (AHP) models for quantifying the weights of criteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria from the AHP model, we propose a new multi-criteria ranking algorithm (MCRA), which evaluates a large variety of patient scenarios and provides the best patient-tailored breast cancer treatment alternatives based on the input of nine medical oncologists. We then validate the predictions of the multi-criteria ranking algorithm by comparing treatment ranks of the algorithm with ranks of five different oncologists, and show that algorithm rankings match or are statistically significantly correlated with the overall expert ranking in most cases. Our multi-criteria ranking algorithm could be used as an accessible decision-support tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our approach is also general in the sense that it could be adapted to solve other complex decision-making problems in medicine, healthcare, as well as other service and manufacturing industries.     

The inferring of preferences from indifferences in multicriteria decision analysis

Rivett [5] has proposed that an approximate preference ordering may be deduced from statements of pairwise indifferences between decision alternatives by using multi-dimensional scaling. In this paper it is demonstrated that much stronger evaluations of preference are possible by applying formal statistical inferential procedures to a simple parametric model, relating indifference to closeness on a scale defined by a linear function of attribute values. This can be used to screen out a considerable proportion of less desirable decision alternatives. The method is illustrated by application to Rivett's problem of the hypothetical Town of Brove, for which a satisfactory matching with Rivett's utilities is obtained. It is also shown that the method can provide useful preference orderings on the basis of less than 20% of all possible pairwise comparisons between alternatives.     

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.     

基于心理阈值的多属性决策问题目标调整研究

将决策者在决策指标上存在心理阈值的现状应用到多属性决策问题,提出先对决策目标调整再进行决策的方法。首先,对效益型、成本型、中间型指标构建相应的指标满意度函数和算法,针对不同类型的指标选择相应的算法,计算决策者在每个备选方案存在心理阈值属性上的满意度;其次,根据决策者满意度与心理阈值,调整决策目标组建新的决策信息表;再次,提出基于决策者满意度的属性赋权与信息集结算法模型,获得最优决策方案;最后,通过一个实际生活中的决策者购房案例,表明该方法的科学合理性。     

AN ASSIGNMENT APPROACH TO CHOOSING R&D EXPERIMENTS

The criteria upon which R&D experiments are evaluated are subjective; therefore in practice it is extremely difficult to obtain a series of weights so that the trade-offs between the various criterion can be analyzed. The problem is compounded when the experiments represent basic research and no prior history is available for utility estimation. This paper presents a graph-theoretic interpretation of the selection process based only on the rank-order of the alternatives thus eliminating the need for utility estimation. Index numbers are then obtained as the solution of an integer program. An application and the theoretical foundations of the procedure are discussed.     

科学基金项目立项评估:综合评价信息可靠性的多指标证据推理规则研究

公正合理的科研项目立项评估与选择是国家自然科学基金管理活动的关键环节。综合考虑科研项目的评估指标体系和选择流程,提出了利用历史评估准确性度量专家提供信息可靠性的方法,进而提出了一种系统性的基于证据推理规则的科学基金项目评估决策模型。该模型使用证据推理合成规则对多专家多指标评估信息进行集结。在集结过程中：充分考虑评估指标的权重以及评估等级的多样性;鉴于参与项目评估的专家具有不同的知识背景和经验,提出利用历史评估结果的准确性衡量专家提供的评价信息可靠性的方法;应用信度分布表征项目的整体评价结果,包含了更丰富的信息。国家自然科学基金项目评估的实例分析证明了该评估决策模型的有效性。     

基于前景理论的新产品开发方案选择方法

为了解决新产品开发中考虑竞争产品方案评价信息的方案选择问题,提出了一种基于前景理论的决策分析方法。在该方法中,依据新产品开发方案与竞争产品方案相比的绩效确定前景参考点,并给出了前景价值函数和权重函数;通过计算和比较新产品开发方案的前景值和竞争产品方案的前景值,得到新产品开发方案的优选结果;最后给出一个算例,说明该方法的可行性和有效性。     

Fading Frames: Data Presentation and Framing Effects*

This article describes three experiments that investigate the impact of data presentation on framing, which is the differential evaluation of objectively equivalent information given different frames of reference. The experiments were designed to vary the amount of information and the format of presentation (tables versus graphs) provided to the decision maker to gain an understanding of the mechanism underlying framing. The inferred information model proposed by Johnson and Levin [11] is utilized to explain the results of the three experiments in terms of the redistribution of weights attached by the decision makers to the attributes of the prospective outcomes. The results of the three experiments indicate that framing effects are observed only when the presentation of data facilitates the shifting of weights among the attributes of the available alternatives. The results further indicate that framing is eliminated as more information is displayed. However, presentation of additional information does not eliminate framing when a graphical format is utilized. Finally, no framing effects are observed when uneven amounts of information are used to describe two alternatives in the same decision task.     

Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach

The design of reverse logistics networks has now emerged as a major issue for manufacturers, not only in developed countries where legislation and societal pressures are strong, but also in developing countries where the adoption of reverse logistics practices may offer a competitive advantage. This paper presents a new model for partner selection for reverse logistic centres in green supply chains. The model offers three advantages. Firstly, it enables economic, environment and social factors to be considered simultaneously. Secondly, by integrating fuzzy set theory and artificial immune optimisation technology, it enables both quantitative and qualitative criteria to be considered simultaneously throughout the whole decision-making process. Thirdly, it extends the flat criteria structure for partner selection evaluation for reverse logistics centres to the more suitable hierarchy structure. The applicability of the model is demonstrated by means of an empirical application based on data from a Chinese electronic equipment and instruments manufacturing company.