Journal of Multi-Criteria Decision Analysis最新文献

This study delves into the flow and structure of knowledge prevalent in Analytic Network Process (ANP) literature. It traces the evolutionary pathway of ANP research, spotlighting seminal works and unfolding the development of topics over time. Analysing 4583 ANP literature records from 1986 to 2022, this research utilizes two pivotal data sources: Web of Science (WoS) and Dimensions. This main path analysis (MPA) approach is coupled with techniques such as citation analysis, social network analysis, and centrality measures to identify most influential papers, as well as using a search path algorithm (SPC) to provide higher weights to papers that serve as bridges connecting different research paths rather than simply counting all citations equally. Key works emerged as focal points in both datasets, including notable contributions from Lee, Kheybari and Büyüközkan, with the Dimensions dataset uniquely highlighting Saaty as the initial conceptual knowledge source of the ANP. The centrality measures unearthed persistent bridge roles undertaken by key contributors such as Saaty and Sarkis over different time periods. Furthermore, a detailed topic analysis discerned eight distinctive thematic clusters among the two data sources: analytical network process, network process, fuzzy analytic, evaluation laboratory, supply chain, quality function, hierarchy process, and information systems. These clusters emerged, providing a comprehensive map of ANP topics and their evolution. The study offers a comprehensive framework for scholars to navigate the expansive domain of ANP research. Identifying pivotal works and emergent themes provides direction for future research endeavours. It allows practitioners to gain invaluable insights into the foundational and transformative works in the ANP realm, enhancing their understanding and application of the methodology. This paper proposes of a dual methodological strategy: First, by using traditional academic data sources (WoS) along with a more encompassing Dimensions database; and second, by using a SPC algorithm to weight MPA papers based on their connecting role among different research paths. At the discipline level, this study carves out a novel vantage point in understanding the ANP research landscape. It offers an integrated portrayal of the field's conceptual evolution, identifying specific cluster topics as an indispensable resource for scholars, practitioners, and students.

Today, global concerns are at their peak due to the fast-growing number of refugees. The world faces the dilemma of the refugee crisis, which needs to be addressed by the United Nations (U.N.) sustainable development goals (SDGs). Pakistan hosts about 1.4 million registered refugees in camps and urban areas. The majority consist of Afghans who departed due to severe poverty, conflict, and security concerns. These refugees bring socio-economic distress to the host country. In order to comprehend the whole crisis, this study primarily focuses on three areas: the unstable political and security situation in the country of origin, the refugee concerns and their implications. This research is unique throughout the literature as it applies the interpretive structural modelling (ISM) tool to view the refugee crisis holistically. Results from ISM show that the ‘Political situation of Afghanistan’ is the most influential factor with very high driving power. Afterward, 10 resulting factors were opted from the ISM model, which demonstrate the implications on the host country. The relative importance index (RII) approach was used to rank the resultant factors. Ranking from RII depicts a significantly different impact of refugees while residing in urban areas. Using the Prisoner's dilemma of cooperative game theory (GT), this study concludes by recommending refugees to get registered and the host country to settle them in the local population. The use of cooperative GT with the nexus of ISM and RII is also exceptional and will be helpful for researchers and policymakers to resolve any conflict.

The recent PROMETHEE-Scoring approach offers a method for generating cardinal scores through an outranking procedure utilizing Stochastic Multicriteria Acceptability and $��\sigma �$–$��\mu �$ analysis. In this paper, we illustrate some undesirable effects of this method and explore possible approaches to address them. First, we modify the original LP by inserting a new constraint that limits negative impacts caused by alternatives with low flow mean and standard deviation. Also, clustering is used to identify outliers in the decision matrix that may induce undesirable effects. A toy example demonstrates the issues and explores the proposed implementations. In addition, a numerical application is presented where a set of countries is ordered based on criteria from an economic freedom assessment. Both the numerical application and the toy example show that the propositions can improve the consistency of the results and prevent inconsistent outputs. The results include comparisons between the rank of alternatives when using purely the PROMETHEE-Scoring method, the proposed improvements, and the expected results from SMAA-PROMETHEE.

Health technology assessment is a systematic, transparent, robust, and unbiased assessment of healthcare technology that links evidence and decisions. Healthcare decisions usually require trading-off, and using a structured approach, such as multi-criteria decision analysis (MCDA), could improve decision quality. This study aimed to adapt an MCDA-based framework for oncological drug evaluation in an Egyptian clinical setting to explore the perspectives of different stakeholders. The 10th edition of the Evidence and Value: Impact on decision making framework was translated, validated, adapted, and applied to explore key differences between the priorities of different stakeholders for coverage decisions in the oncology field in Egypt. A direct rating scale or hierarchical point allocation was used to elicit criteria weights. Normalized weights were calculated and compared using the preset cut-off value of 0.008. Understandability, willingness to use the adopted weighting techniques, and cognitive burden were assessed and compared. Ninety participants completed the criteria-weighting interview for the adapted tool. The elicited criteria weights differed between the two adopted weighting techniques when comparing stakeholders' priorities using the preset cut-off (0.008). However, there was no significant difference in the weighting technique understandability, future use willingness, and cognitive burden parameters on categorization by either the weighting technique or stakeholder group. This study provides a specially tailored, straightforward procedure for Egyptian stakeholders to evaluate oncologic drugs in a standardized manner by combining various inputs. It also highlights the main variations across Egyptian stakeholder preferences.

The article describes an approach, ensuring higher credibility of expert estimation results, based on specific order of pair-wise comparisons. The order of pair-wise comparisons is, in its turn, based on the distance between estimated objects in the ranking. According to the suggested approach (and some human psychophysiological features), the most ordinally distant objects should be compared before ordinally closer ones. In order to empirically confirm this assumption, a special experiment involving real experts has been conducted. The results of the experiment indicate that if objects are presented to the expert for comparison in the suggested order, then in the majority of cases relative weights of objects, obtained using eigenvector method, most adequately reflect this expert's priorities. Moreover, pair-wise comparison matrices constructed using the suggested comparison order tend to be slightly more consistent. The suggested approach to re-ordering of pair-wise comparisons can be applied as part of the AHP algorithm in weakly structured subject domains, influenced by multiple intangible criteria. It also provides conceptual basis for reduction of the number of pair-wise comparisons, required to obtain credible results, in AHP without loss or distortion of expert data. It can also be used for modification of combinatorial pair-wise comparison aggregation method, based on spanning tree enumeration. And, finally, it will improve the overall multi-criteria decision-making process in diverse subject domains, characterized by high uncertainty levels.

The nondominated set of a multiple objective discrete optimization problem is known to contain unsupported nondominated points, which outnumber the supported ones and are more difficult to obtain. We treat supported nondominated points as a representation and analyse their quality using different metrics beyond their sheer numbers. Under different data generation schemes on multiobjective knapsack and assignment problems, we observe that supported nondominated points almost always provide a good representation of the entire nondominated set.

Verbal decision analysis (VDA) is a family of methods for multi-criteria decision analysis that require no numerical judgements from the agent. Although many such methods have been developed, they share the potential issue of asking the agent many more questions than necessary, particularly under multilevel approaches. Furthermore, whether VDA improves decisions, compared to no intervention, has yet to be investigated empirically. I introduce a new VDA method, Artiruno, with a freely licensed implementation in Python. Artiruno makes inferences mid-interview so as to require minimal input from the agent, while using a multilevel scheme that allows it to ask complex questions when necessary. Inferences are facilitated by an axiom allowing comparisons to be partitioned across groups of criteria. Artiruno's performance in a variety of simple and complex scenarios can be verified with automated software tests. For an empirical test, I conducted an experiment in which 107 people from an Internet subject pool considered an important decision they faced in their own lives, and were randomly assigned to use Artiruno or to receive no intervention. These subjects proved mostly able to use Artiruno, and they found it helpful, but Artiruno seemed to have little influence on their decisions or outcomes.

Strategic problem-solving enables organizations to pursue opportunities and address emerging threats proactively. However, traditional problem-solving methods often rely on business processes and organizational procedures, which may not be available at the strategic level. This article investigates potential gaps in strategic problem-solving methods through a Systematic Literature Review. The study analyses the existing literature on the potential of current problem-solving methods to identify and resolve root causes of strategic problems when formal business processes and procedures are unavailable. A rigorous literature search process guided by focused research questions examines Problem Structuring Methods, Lean Thinking, Six Sigma, Theory of Constraints, Balanced Scorecard, SWOT Analysis, and other techniques. The synthesis of findings reveals limitations in strategic root cause analysis. In addition, the study introduces a supplementary decision-making frame of reference to aid the selection of appropriate methods across problem-solving, decision-making, and solution implementation stages. This framework addresses the common challenges decision-makers face in navigating organizational complexity and choosing suitable approaches, as well as visually maps methods to stages based on Content, Organizational, and Analytical complexity dimensions. The framework builds on the study's findings that using a single methodology may be insufficient for a complete decision process. The proposed decision-making framework also offers valuable guidance for integrating diverse methods aligned to decision situations.

Empirical evidence suggests that decision-makers are ill-equipped to identify all relevant objectives in a decision problem. We examine the effect of an incomplete set of objectives using a Monte Carlo simulation to compare a baseline model to a reduced model incorporating only a subset of objectives. We assess the performance of reduced models varying in the number of objectives, the number of alternatives, the correlations among objectives, and attribute weights. Results suggest that missing objectives will most impact multiattribute models with negative correlations between objectives; similarly, models with equally weighted objectives suffer more than models with unequal weights. Decision problems with more objectives tend to be less impacted by missing objectives, given the same proportion of missing objectives. In contrast, decision problems with more alternatives are more impacted for some performance measures but less on others. However, the variation in model performance due to the number of objectives and alternatives is relatively minor compared to the variation due to the nature of the correlation between objectives.