Journal of Multi-Criteria Decision Analysis最新文献

This work describes the process of building two indicators in order to measure the efficiency of the Food Acquisition Program – Milk modality (PAA-Milk) in the States which implement it. The Analytic Hierarchy Process (AHP) methodology was used to develop the first indicator, while the Principal Component Analysis was used as a tool for cutting and simplifying the structure of the first indicator to obtain the second indicator. The results demonstrate the great potential of the AHP tool together with the statistical tools to develop indicators to diagnose and monitor public policies in Brazil. The states of Alagoas, Paraíba, and Ceará presented the best efficiency in the performance of the Program, while Bahia and Pernambuco presented the worst results.

A new approach to re-evaluating consistency in the analytic hierarchy process (AHP) using simulated consistent matrices is presented. The proposed consistency evaluation method makes use of statistically significant deviations from the average consistency measure for the simulated matrices. This addresses most of the deficiencies of the conventional consistency ratio (CR) method. A pairwise comparison matrix (PCM) is adjudged inconsistent by the proposed method if its consistency measure exceeds the modeled consistency threshold. Comparison of the consistency evaluation for simulated nearly-consistent matrices using the proposed method shows a statistically significant reduction of the order-specific bias in comparison with the CR method. The proportion of nearly consistent matrices which are evaluated as ‘inconsistent’ increases more than three-folds when the evaluation is done using the CR method. Several examples are presented which illustrate the advantages of the proposed method and differences in classification with the CR approach. Evaluation of consistency using the proposed method of statistically derived thresholds from simulated, nearly consistent matrices is more nuanced and objective, as well as intuitive in its interpretability.

In order to better design more sustainable farming systems, and prepare for the development of multi-criteria farm decision model, we investigate how farmers rank their main goals when making decisions. First, we identified the main goals used by farmers through in-depth interviews with randomly selected farmers in which we used small games to elicit the main goals they are using to make farm-level decisions. Then, we developed a best–worst scaling (BWS) experiment, in which farmers have to declare the “most” and the least “important” goals they use when making decisions. The experiment was conducted with 120 farmers. We first derive a ranking of the goals according to the population average, which showed the importance of rice self-sufficiency and transmission of farm capital. We then use a scale-adjusted latent class analysis. We identified four groups of homogenous preferences among farmers. The use of differentiated scale, a measure of choice inconsistencies, suggested different levels of certainty about the ranking, and the presence of more inconsistencies when asking the least important goal. While a large group focuses only on rice self-sufficiency, and farm transmission, we also identified a group of optimizers, and risk-averse farmers. Farmers of each group are likely to behave differently with regard to sustainable innovations. We also showed that some socio-economic variables describing the farms and the households influenced the probabilities for farmers to belong to one of the four classes. Overall, we showed that BWS scaling experiments provide a rich set of information about the diversity of rankings. It also provides the set of tools to evaluate the consistency and quality of respondents' choices.

One major challenge of social impact assessment concerns the implementation of multicriteria decision analysis (MCDA) to ascertain the vulnerability of households to environmental change. While MCDA has been widely used to combine vulnerability indicators into an aggregated vulnerability score, the sensitivity of vulnerability indices to uncertain appraisals and judgements of the magnitudes and weights of indicators has been largely ignored so far. In this work, based on vulnerability indicators previously selected and ranked using the analytic hierarchy process (AHP) technique, for household Brazil surveys carried out in 1998 and 2012, a sensitivity analysis (SA) was implemented to account for the variation of vulnerability indicators over time and space. In particular, two techniques were applied: the indicator removal and the threshold value tests. The indicator removal test involved setting to zero a particular indicator weight and rescaling the remaining indicator weights linearly. The threshold value test aimed to identify which indicators had the most relative influence on both indices. Finally, the critical threshold value showed the most important vulnerability indicators and allowed to summarise and contrast the standardized scores differences of the indicators between the two surveys. The results showed which indicators were the most important in increasing or decreasing the vulnerability and improved the understanding of how the overall vulnerability of rainfed farming households changed through time as a function of changes in sensitivity and adaptive capacity.

Promethee ii is a multi-criteria decision aid method working as follows: firstly, all alternatives are compared two by two to form a pairwise comparison matrix. Then the net flow score procedure is applied on this matrix to assign a score to each alternative according to which they are ranked. Methods of the Promethee family may suffer from rank reversal occurrences. The legitimacy of methods suffering from this phenomenon has been largely debated in the literature. The aim of this work is to provide a characterization of the Promethee ii net flow scores as a combination between two distinct methods based on different hypotheses. The first one is based on the direct pairwise comparison of the two considered alternatives while the second is based on the comparison with respect to all the other alternatives of the problem. This combination highlights the dependence on third alternatives as an inherent characteristic of the Promethee ii method.

We provide a comprehensive overview of the literature of algorithmic approaches for multiobjective mixed-integer and integer linear optimization problems. More precisely, we categorize and display exact methods for multiobjective linear problems with integer variables for computing the entire set of nondominated images. Our review lists 108 articles and is intended to serve as a reference for all researchers who are familiar with basic concepts of multiobjective optimization and who have an interest in getting a thorough view on the state-of-the-art in multiobjective mixed-integer programming.

To enhance preparedness for diverse pandemic situations we aim to predict the performance of various pharmaceutical intervention strategies. We gathered domain experts and ran a series of decision conferences where a scenario-based multi-criteria decision analysis (MCDA) model was interactively defined and implemented. Assuming an influenza pandemic, a micro simulation model was used to estimate societal health impact, a health-economic model was used to estimate economic losses, and expert preferences were elicited to define trade-offs between multiple criteria and synthesize various estimates. Sensitivity analysis to address various forms of uncertainty was also conducted. Nine intervention strategies, including the baseline “no interventions” strategy, were evaluated and ranked under five pandemic scenarios for Sweden's population. We conclude that a scenario-based MCDA approach relying on multiple models for assessment of consequences is instrumental in defining robust interventions and support decision-making at the pre-pandemic and pandemic situations.

Prioritizing information technology (IT) projects for resource direction is a complex decision-making process that involves the analysis of qualitative and subjective criteria. The uncertainty of the subjective and imprecise assessments derived from estimated data that represent the analyzed criteria characterizes the decision-making process. To reduce the uncertainties related to IT project priority setting, we proposed a model based on a hybrid multicriteria method composed by the best–worst method to establish the weights of the criteria and fuzzy-TOPSIS to define the project ranking. The model allows us (1) to define strategic subcriteria, (2) adjust the weights according to the company's reality, (3) to deal with the uncertainties of the evaluations of the decision makers, and (4) provide the project ranking that translates reality into the distribution of resources in the IT sector. Finally, we applied the model to a case study in the IT sector of a Brazilian agroindustrial cooperative. For the managers, the method provides a precise decision aiding tool. Although the model has been applied to the prioritization of IT projects in a cooperative, the same can be generalized to the IT sectors of other companies.

Decisions in supply chain management (SCM) are subject to numerous conflicting criteria and multiple objectives. For such decisions, multiple criteria decision making (MCDM) methods are definitely appropriate. The implementation of the healthcare supply chain (HSC) is more complex to manage than any other supply chains, as it involves human life, causing conflicts of interest and hindering the final decision. Previous researchers suggested different SC models for healthcare products such as drugs, vaccines, and other medical equipment. This article provides an overview of published articles in the application of MCDM methods in HSCM at the strategic, tactical, and operational levels. We studied and categorized 139 articles published in 2006–2021, providing academic researchers, practitioners, and governments with insights into the application of different MCDM methods. The review allows us to establish guidelines for the selection of appropriate methods for HSC management and provide support to the management of issues in the healthcare and pharmaceutical sector.