Multiple Criteria Hierarchy Process Research Articles

Enhancing hospital benchmarking: A multidimensional approach to quality assessment in Portugal

The healthcare policy reforms implemented in Portugal in recent years significantly impacted the quality of health care provided to Portuguese citizens. Since public hospitals play a pivotal role as secondary care providers within the National Health Service (NHS), it is essential to assess their quality. However, a generalized assessment of this multidimensional concept is insufficient to differentiate hospitals across different quality dimensions, limiting the efficiency of benchmarking processes. The conjoint use of the Electre Tri-nC method with the Multiple Criteria Hierarchy Process (MCHP) allowed us to assess quality in Portuguese public hospitals by encompassing dimension-based analysis and a holistic perspective. Electre Tri-nC is a multiple criteria sorting method that categorizes each alternative (in this case, a public hospital in Portugal) into pre-defined and ordered categories representing quality levels. The hospitals are assessed based on four non-elementary criteria (Timeliness, Effectiveness, Safety and Efficiency) structured hierarchically in logical subsets of elementary criteria, allowing MCHP to evaluate each hospital based on specific criteria subsets or the comprehensive level. The sample size is 25 hospitals, and the analysis period includes the years from 2019 to 2022. The results evidence a general downward trend in quality, culminating in poor quality levels in most public hospitals in 2022, particularly in the Effectiveness dimension. A robustness analysis was conducted to examine the resilience and reliability of the model. By establishing a framework for assessing quality assessment across different critical dimensions, this work provides an enhanced hospital benchmarking process for healthcare policymakers. This process can serve as a reference for implementing new hospital reforms based on replicating the successful practices adopted by the healthcare facilities that demonstrate superior quality results in specific dimensions or even allocating resources more efficiently across the entire network by identifying dimensions in which hospitals are performing poorly.

Blockchain service platform evaluation with probabilistic linguistic preference information based on Choquet integral and ExpTODIM

The application of Internet of Medical Things (IoMT) can improve the treatment efficiency of medical institutions, but it still has hidden dangers in security and privacy. The introduction of blockchain technology into IoMT is expected to prevent these hidden dangers. This study proposes a multiple criteria decision aiding model to select a suitable blockchain service platform for IoMT to enhance its safety and reliability. The proposed model combines the probabilistic linguistic term set (PLTS), multiple criteria hierarchy process (MCHP), 2-additive Choquet integral and ExpTODIM method to assist decision-makers (DMs) to obtain a reliable and low-risk decision result. The MCHP is employed to analyze decision-making problems by hierarchical criteria. The 2-additive Choquet integral is adopted to reflect interactions between criteria, and parameter values in the 2-additive Choquet integral are indirectly determined based on the preference information of DMs. PLTSs are used to represent the evaluation information of experts and the preference information of DMs. The ExpTODIM (Exponential TODIM) method is adopted to rank alternatives considering the psychological characteristics of DMs. An illustrative example demonstrates the applicability and merits of the proposed model with comparative analysis.

Consensus driven preference analysis model for incomplete group decision making with the multiple criteria hierarchy process

Most real-life group decision making issues involve incomplete assessments under hierarchical criteria due to limited knowledge and experience of decision makers, which entail diverse challenges including information filling, consensus reaching and preference fusion. To cope with them, an incomplete information-filling method is first presented based on the objective data and subjective willing. Then to improve the flexibility and compatibility, a novel group consensus measurement is defined based on the norm theory, which considers the influence of label preference ignored by the distance-based consensus measurements. Finally, differently from the existing group decision making methods that infer a consensus result through aggregating all individual preferences, this study develops a consensus driven preference analysis framework to derive the preference relationships among alternatives and further obtain the final ranking result using the representative value function. An application example of a sustainable supplier selection is carried out to illustrate the proposed methodology, and sensitivity and comparative analyses are performed to exhibit its flexibility and effectiveness.

Analysis of the innovation capacity of Mexican regions with the multiple criteria hierarchy process

This study attempts to use multicriteria decision aiding (MCDA) tools to analyse the innovation capacity of 32 regions in Mexico. In today’s competitive world, innovation in science and technology is the key to the growth and productivity of the regions. Understanding the current state of innovation capacity and identifying the factors that influence said capacity allows the government to make region-specific policies for future growth and development. However, measuring such a complex concept involves a large number of criteria, and to understand the impact of a region’s innovation capacity under a subset of criteria or with respect to high-level views it is necessary to gain in-depth insight for future policy design. To address this issue, we adopt the multicriteria hierarchy process (MCHP), which allows the decision-maker to express preferences of sub- group criteria and individual analysis by using subsets of criteria and different dimensions of the problem. Further, with the aim of managing the weighting of criteria and preference aggregation within the MCHP framework, we employ the hierarchical version of the deck of cards method for weight definition, the hierarchical ELECTRE III to aggregate preferences, and the distillation procedure to exploit the preference model. Using this methodological framework, the innovation capacity of 32 regions in Mexico is analysed under 52 decision criteria.

Robust measurement of innovation performances in Europe with a hierarchy of interacting composite indicators

ABSTRACT For a long time, the measurement of innovation has been at the forefront of policymakers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. The European Innovation Scoreboard (EIS) is a relevant tool for benchmarking innovation in Europe. Still, the EIS lacks a proper scheme for weighting the included indicators according to their relative importance. Moreover, despite there is a consensus on the importance of interaction between different dimensions of innovation, there is no composite indicator taking into account of this. We propose an appraisal methodology permitting to take into consideration the interaction of dimensions to be aggregated and robustness concerns related to the elicitation of the weights assigned to the elementary indicators structured in a hierarchical way. With this aim, we apply a multiple-criteria decision-making approach being the conjunction of three methodologies, namely, the multiple-criteria hierarchy process, the Choquet integral and the stochastic multicriteria acceptability analysis. It helps the users to rank and benchmark countries' innovation performance at partial and global level taking into account the importance and interaction of dimensions as well as robustness concerns.

Multiple criteria hierarchy approach for analyzing competitiveness of States in Mexico

Competitiveness is related to welfare, economic progress and prosperity and has been an important topic for countries, enterprises, investors and citizens alike. This paper presents a new Multiple Criteria Hierarchy Process (MCHP) for rank the competitiveness of States in Mexico through the analysis of multiple criteria such as legality, economic, social, politic, governmental and environmental, precursors and international relations aspects. An important aspect of the new MCHP proposed is that considers the interaction between criteria and measure the performance of a large number of criteria. The study presents a competitiveness analysis of Mexican regions, regarding the interaction in different level of criteria hierarchy and evaluating the performance of 100 criteria to establish the competitiveness level of States in Mexico. Keywords: Multicriteria, hierarchy approach, competitiveness JEL Codes: C69, C81, D81 Received: 10/07/2020. Accepted: 05/11/2020. Published: 01/12/2020

Multiple criteria hierarchy process for sorting problems under uncertainty applied to the evaluation of the operational maturity of research institutions

Despite the availability of highly qualified research personnel, up-to-date research facilities and experience in developing applied research and innovation, many worldwide research institutions face challenges when managing contracted Research and Development (R&D) projects. These difficulties are mainly due to expectations from Industry (private sector), particularly regarding the applied development procedures, managerial processes and timing. Such difficulties have motivated funding agents to create evaluation processes to check whether the operational procedures of funded research institutions are sufficient to provide timely answers to demand innovation from industry, and also to identify aspects that require quality improvement in research development. For this purpose, several multiple criteria decision-making approaches can be applied. In this context, the research institutions are considered as alternatives for funding and their processes for research development as decision criteria. Among the available multiple criteria approaches, sorting methods are one prominent tool to evaluate operational capacity. However, the first difficulty that one may face when applying multiple criteria sorting methods is the need to hierarchically structure multiple criteria in order to represent the intended decision process. Additional challenges include the elicitation of the preference information and the definition of criteria evaluation, since these are frequently affected by some imprecision. In most approaches, all these critical points are neglected, or, at best, only partially considered. In this paper, a new sorting method is proposed to deal with all of those critical points simultaneously. To consider multiple levels for the decision criteria, the FlowSort method is extended to account for hierarchical criteria. To deal with imprecise data, FlowSort is integrated with fuzzy approaches. To yield solutions that consider fluctuations from imprecise weights, the Stochastic Multicriteria Acceptability Analysis (SMAA) is used. Finally, the proposed method is applied to the evaluation of research institutions, classifying them according to their operational maturity for the development of applied research.

The introduction of the SRF-II method to compare hypothesis of adaptive reuse for an iconic historical building

The paper shows how multiple criteria decision aiding (MCDA) tools can support the analyses of six hypotheses of adaptive reuse of an iconic historical building in Turin, Italy (called stock exchange) to identify the preferred alternative. In the last 2 years, the debate around the requalification of the building has been huge for several reasons: it is perceived as a “monument” by citizens; it shows architectural and typological values nationally recognized; it involves public and private interests. In this context, we tested the applicability of a recently proposed multicriteria decision making methodology to architecture field. This decision support approach is a conjunction of four MCDA methods, namely: multiple criteria hierarchy process (MCHP), permitting to consider structural relationships between criteria; ELECTRE III, considering three types of interaction effects between criteria (strengthening, weakening and antagonistic effects); the imprecise SRF method, supplying an easily understandable approach to collect information from the Decision Maker; and stochastic multicriteria acceptability analysis (SMAA), providing robust recommendation, in terms of rankings and relations of preference, indifference and incomparability between project alternatives, at each level of the hierarchy. We propose a modification of the SRF methodology, called SFR-II, to increase the reliability of the decision aid procedure, which could constitute a significant advance for the same SRF method.

Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - Stochastic multicriteria acceptability analysis - Choquet integral method

Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China’s urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development.

Multiple criteria decision aiding as a prediction tool for migration potential of regions

This study presents the potential of multiple criteria decision aiding (MCDA) as a tool for prediction analysis. We apply MCDA methodology for the determination of internal migration potential of regions within a country. The ELECTRE Tri-C together with the Multiple Criteria Hierarchy Process (MCHP), the imprecise SRF method and the Stochastic Multicriteria Acceptability Analysis (SMAA) will be applied to investigate the case of the internal migrations in Serbia. We shall give recommendations regarding the migration potential of Serbian municipalities in the following years. In particular, the ELECTRE Tri-C will be applied to classify the regions in four classes ordered with respect to regions’ migration potential, MCHP will be used to take into account the hierarchical structure of criteria on which the municipalities are evaluated. Finally, the imprecise SRF method, as well as the SMAA methodology, will be used to take into account the preferences of numerous economic agents.

SMAA‐QUALIFLEX methodology to handle multicriteria decision‐making problems based on q‐rung fuzzy set with hierarchical structure of criteria using bipolar Choquet integral

The qualitative flexible multiple criteria method (QUALIFLEX) is a convenient outranking technique to handle multicriteria decision-making (MCDM) problems due to its less complexity and high applicability, while the multiple criteria hierarchy process (MCHP) allows decision makers to deal with the hierarchical structure of criteria set where decision makers can even estimate results for a particular subcriterion at some intermediate level of the hierarchy. The main focus of our study is the amalgamation of the MCHP and QUALIFLEX methodology with special emphasis on modeling interaction among the criteria using the concept of bipolar Choquet integral. To give the decision makers more freedom for expressing their cognition about membership and nonmembership grades, the q-rung orthopair fuzzy (q-ROF) environment is adopted to expresses the criteria measurement. To facilitate this, it is first proposed a revised closeness index for q-ROF to identify the appropriate ordering. Further, it is aimed to establish a new framework by implementing stochastic multiobjective acceptability analysis in our proposed extended QUALIFLEX method to take into account a variety of parameters compatible with the descriptive information regarding the relative importance and interaction of different criteria provided by the decision maker. Finally, a numerical example based on the supplier selection problem is presented to illustrate the proposed methodology in the decision problem.

Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach

The evaluation of sustainable development –and, in particular, rural development– through composite indices requires taking into account a plurality of indicators, which are related to economic, social, and environmental aspects. The points of view evaluated by these indices are naturally interacting: thus, a bonus has to be recognized to units performing well on synergic criteria, whereas a penalisation has to be assigned on redundant criteria. An additional difficulty of the modelization is the elicitation of the parameters for the composite indices, since they are typically affected by some imprecision. In most approaches, all these critical points are usually neglected, which in turn yields an unpleasant degree of approximation in the computation of indices. In this paper we propose a methodology that allows one to simultaneously handle these delicate issues. Specifically, to take into account synergy and redundancy between criteria, we suitably aggregate indicators by means of the Choquet integral. Further, to obtain recommendations that take into account the space of fluctuation related to imprecision in non-additive weights (capacity of the Choquet integral), we adopt the Robust Ordinal Regression (ROR) and the Stochastic Multicriteria Acceptability Analysis (SMAA). Finally, to study sustainability not only at a comprehensive level (taking into account all criteria) but also at a local level (separately taking into account economic, social, and environmental aspects), we apply the Multiple Criteria Hierarchy Process (MCHP). We illustrate the advantages of our approach in a concrete example, in which we measure the rural sustainability of 51 municipalities in the province of Catania, the largest city of the East Coast of Sicily (Italy).

GAIA-SMAA-PROMETHEE for a hierarchy of interacting criteria

In this paper, we propose an extension of the PROMETHEE methods. Despite PROMETHEE are among the most applied methods in Multiple Criteria Decision Aiding (MCDA), some drawbacks can be underlined with respect to their applicability since they do not take into account few characteristics of multicriteria decision problems that are very relevant in real world applications: (i) robustness regarding the plurality of compatible preference parameters; (ii) interaction between criteria; (iii) hierarchies of criteria. Even if different extensions of the PROMETHEE methods have taken into account all these aspects singularly, we propose to deal with all of them simultaneously presenting a new version which incorporates Robust Ordinal Regression (ROR), Stochastic Multicriteria Acceptability Analysis (SMAA), bipolar Choquet integral and Multiple Criteria Hierarchy Process (MCHP). ROR and SMAA permit to consider all the instances of the considered preference model compatible with the preference information provided by the DM; the bipolar Choquet integral is able to represent the possible positive and negative interactions between criteria as well as the antagonistic effect between some of them; finally, the MCHP permits to decompose the problem in small parts so that each of them can be analyzed more in detail with respect to the problem at hand. Moreover, we also introduce an extension of the GAIA technique to handle visualization in MCDA problems presenting interactions and antagonistic effects between criteria organized in a hierarchy. Furthermore it gives the possibility to display the plurality of instances of the preference model considered by SMAA. A didactic example will illustrate the proposed methodology.

A robust ranking method extending ELECTRE III to hierarchy of interacting criteria, imprecise weights and stochastic analysis

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.

Multiple Criteria Hierarchy Process for ELECTRE Tri methods

Sorting methods, in particular ELECTRE Tri methods, are widely used in Multiple Criteria Decision Aiding to deal with ordinal classification problems. Problems of this kind encountered in practice involve the evaluation of different alternatives (actions) on several evaluation criteria that are structured in a hierarchical way. In order to deal with a hierarchical structure of criteria in decision problems, Multiple Criteria Hierarchy Process (MCHP) has been recently proposed. In this paper, we apply the MCHP to the ELECTRE-Tri methods. In particular, we extend ELECTRE Tri-B, ELECTRE Tri-C and ELECTRE Tri-nC methods. We also adapt the MCHP concept to the case where interaction among evaluation criteria has either strengthening, or weakening, or antagonistic effect. Finally, we present an extension of the SRF method to determine the weights of criteria in case they are hierarchically structured.

Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model

The paper deals with two important issues of Multiple Criteria Decision Aiding: interaction between criteria and hierarchical structure of criteria. To handle interactions, we apply the Choquet integral as a preference model, and to handle the hierarchy of criteria, we apply the recently proposed methodology called Multiple Criteria Hierarchy Process. In addition to dealing with the above issues, we suppose that the preference information provided by the Decision Maker is indirect and has the form of pairwise comparisons of criteria with respect to their importance and pairwise preference comparisons of some pairs of alternatives with respect to some criteria. In consequence, many instances of the Choquet integral are usually compatible with this preference information. These instances are identified and exploited by Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis. To illustrate the whole approach, we show its application to a real world decision problem concerning the ranking of universities for a hypothetical Decision Maker.

Multiple criteria hierarchy process for sorting problems based on ordinal regression with additive value functions

A hierarchical decomposition is a common approach for coping with complex decision problems involving multiple dimensions. Recently, the multiple criteria hierarchy process (MCHP) has been introduced as a new general framework for dealing with multiple criteria decision aiding in case of a hierarchical structure of the family of evaluation criteria. This study applies the MCHP framework to multiple criteria sorting problems and extends existing disaggregation and robust ordinal regression techniques that induce decision models from data. The new methodology allows the handling of preference information and the formulation of recommendations at the comprehensive level, as well as at all intermediate levels of the hierarchy of criteria. A case study on bank performance rating is used to illustrate the proposed methodology.

Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE

Robust Ordinal Regression (ROR) supports Multiple Criteria Decision Process by considering all sets of parameters of an assumed preference model, that are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters, or for at least one compatible set of parameters, respectively. In this paper, we propose an extension of ELECTRE and PROMETHEE methods to the case of the hierarchy of criteria, which was never considered before. Then, we adapt ROR to the hierarchical versions of ELECTRE and PROMETHEE methods.

Multiple Criteria Hierarchy Process in Robust Ordinal Regression

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all evaluation criteria are considered at the same level, however, it is often the case that a practical application is imposing a hierarchical structure of criteria. The hierarchy helps decomposing complex decision making problems into smaller and manageable subtasks, and thus, it is very attractive for users. To handle the hierarchy of criteria in MCDA, we propose a methodology called Multiple Criteria Hierarchy Process (MCHP) which permits consideration of preference relations with respect to a subset of criteria at any level of the hierarchy. MCHP can be applied to any MCDA method. In this paper, we apply MCHP to Robust Ordinal Regression (ROR) being a family of MCDA methods that takes into account all sets of parameters of an assumed preference model, which are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters or for at least one compatible set of parameters, respectively. Applying MCHP to ROR one gets to know not only necessary and possible preference relations with respect to the whole set of criteria, but also necessary and possible preference relations related to subsets of criteria at different levels of the hierarchy. We also show how MCHP can be extended to handle group decision and interactions among criteria.