Multiple Criteria Decision Aid Methods Research Articles

A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE’s FTSE350 Index.

Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA

The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI) reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.

Combined optimisation and MCDA based solution of the tram depot location problem

This paper deals with an issue of technical facilities location in a public transport system. The decision problem is formulated as a selection of the most advantageous alternative, i.e. the location of a new tram depot among the already existing facilities of this type. The selection is preceded by the evaluation of the alternatives. The assessment is not a trivial task, because there are many groups of interest with usually contradictory points of view. Therefore, the evaluation of the new tram depot locations should represent different aspects, e.g., economical, technical, environmental, and organizational. To handle such a complex decision problem the authors propose a methodology, which is a composition of the optimisation and multiple criteria evaluation techniques. The developed methodology is experimentally applied to the selection of one out of five tram depot locations in the public transport system of the city of Poznan, Poland. All the computational experiments are performed by means of optimization and multiple criteria decision aiding (MCDA) methods and tools, i.e. a linear optimization engine Solver Premium Platform and AHP method with its application AHORNsimple. The calculations are the basis for recommending the location of a new depot in the central part of the transport system network, which is a reasonable solution taking into account, e.g. the proximity of the main railway line, the possibility of triple distribution of the transport means from depot. The proposed methodology of the decision problem solution gives also an opportunity to create the hierarchy of considered tram depot locations as well as to compare the position in the ranking of the best solution with the existing one. Since the proposed methodology assumes the selection of the most suitable MCDA method to the problem under consideration and the decision maker’s preferen¬ces, it guarantees that the result of analysis becomes reliable and the decision aiding process is credible.

Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem

The article presents the issue of designing a network of electric vehicle charging stations (EVCSs) for servicing passenger and commercial vehicles and taking into account the already existing locations. The authors propose a five-stage multicriteria- and GIS-based EVCS location methodology (5MAGISEV) for designing the EVCS network that includes defining potential EVCS locations (stage 1), constructing a family of evaluation criteria (stage 2), generating alternatives (stage 3), selecting an appropriate multiple criteria decision aid method (stage 4) and conducting a multicriteria evaluation of alternatives (stage 5). The methodology was verified by its experimental application for solving the problem of EVCS location in Poznan, Poland. As a result of its application, a set of over 155,000 alternatives was generated in view of different EVCS location determination policies. A family of eight criteria with subcriteria was proposed to assess the solutions, including the evaluation of the degree of integration of the considered EVCSs with such issues as public transport (C1); urbanized areas (C2); main road network (C3); industrial areas (C4); and the location with respect to transport areas (C5), shops of various types (C6), address points (C7) and DYI stores (C8). Based on the selected method of multiple criteria decision aid (LBS), a solution was selected that constitutes a compromise design of the EVCS network.

Towards cognitive decision support: A model of behavioural assessment of multi-criteria methods

Abstract The Negotiation Support Systems often implement multiple criteria decision aiding (MCDA) techniques for building a negotiation scoring system. Those formal models should meet the needs, motivations, expectations, and cognitive abilities of users. In this paper, we try to explore the effects of decision maker’s subjective perception of ease of use, time requirements, interface, preference representation, and efficiency of a particular MCDA method on the choice regarding the future use of this method. The multinomial logistic regression model is built and analysed. The analysis is based on data from online decision making experiments, where three MCDA methods were implemented, i.e. AHP, SMART, and TOPSIS. The study provides several interesting findings, concerning the behavioural aspects of multiple criteria decision aiding in software support systems. Most of the users recommended TOPSIS as the best one for supporting decisions in the future. This is a fast technique, for which we used an attractive graphical interface, suggesting that these factors play a crucial role in the users’ choices. However, the causative regression model showed that the user’s positive experience in using a particular method, i.e. its effectiveness in solving an exemplary numerical case, has the highest impact on the method’s choice for future use. The second most important factor is the adequacy in representing the user’s preferences by this method. We show, however, that the strengths of effects and their significance may vary across the methods. Understanding the decision maker’s evaluations of the MCDA techniques may help build a cognitive negotiation support system that satisfies the user’s expectations.

An example on the use and limitations of MCDA: The case of fuzzy VIKOR

VIKOR is one of the well known Multiple Criteria Decision Aid methods, used frequently and considered to produce robust results. However, it seems that it is not immune to the rank reversal phenomenon. The latter is a well studied phenomenon that occurs sometimes in many such methods and is the cause of often severe criticism. This short paper provides such an example (and a counter-example) in the case of a fuzzy version of VIKOR.

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.

The methodology of solving stochastic multiple criteria ranking problems applied in transportation

The aim of this paper is to present the methodology of solving complex multiple criteria problems with the presence of non-deterministic information. The author proposes a universal approach of ranking variants applied to solve decision problem in transportation. Since the nature of transportation process is stochastic, the evaluation criteria are expressed by non-deterministic values. Moreover, the preferential information is usually contradictory, with some level of uncertainty. As a result, the proposed methodology is composed of two phases, including: (1) the selection of the most suitable deterministic multiple criteria decision aiding (MCDA) method to decision problem and decision maker’s (DM’s) preferences expressed during the modelling process; (2) its application using non-deterministic information, i.e. transformation of non-deterministic information into deterministic values, computation of relations between variants resulting in many deterministic rankings, calculation of non-deterministic relations between variants, and construction of final ranking. The proposed methodology is verified on the exemplary decision problem in transportation. The application of the procedure increases the credibility of the final decision, giving an opportunity to use deterministic MCDA method in the stochastic transportation process.

A historical analysis of tax competitiveness between EU member states

We demonstrate that the dynamics of tax competitiveness in the EU has gained a new higher level. The shift in the tax competition can be noted after new countries have joined the EU. Both positive and negative effects of tax competitiveness are discussed in the paper and the question of trade-off between the strong tax competitiveness of a country and magnitude of its tax revenue is raised. Evaluation of chosen countries using multiple criteria decision aid methods (MCDA) shed more light on the opposition of two groups of countries, the old and new members of the EU, provided more details on the both the present state and dynamics of tax competitiveness in the. The evaluation methodology can be successfully used for monitoring the current state of tax competitiveness of each member country. Prominence of the multiple criteria evaluation TOPSIS method of MCDA evaluation is described.
 Keywords: Competitiveness of countries, tax system, tax competition, tax revenues, multiple criteria evaluation, TOPSIS.

Assigning a house for refugees: an application of a multiple criteria nominal classification method

The paper proposes the use of a multiple criteria decision aiding (MCDA) nominal classification method to tackle a dramatic current social problem: the accommodation system for refugees. In recent years, the number of people in need of protection and assistance as a consequence of forced displacement has drastically increased. As many other European cities, Turin (Italy) offers to refugees temporary places for a limited time span, but they are insufficient and often inadequate. Meanwhile, there are several underused buildings that can potentially be used to cover the refugees’ needs of housing and facility. Combining these two aspects, this study aims at contributing to the definition of an urban strategy and improve the decision aiding process related to the accommodation system in Turin. We adopt a recently developed MCDA method to tackle the problem of accommodation of migrants, that is, the Cat-SD (Categorization by Similarity–Dissimilarity) method. We face a nominal classification problem where underused buildings located in the city of Turin are assigned to categories (i.e., three migrant status). A decision model is constructed based on the judgments of the experts. Thus, the application of the method allows to identify an adequate category (or categories) of migrant status for each building.

On the increasing importance of multiple criteria decision aid methods for portfolio selection

In 1952, Markowitz published his famous paper on portfolio selection that transformed the field of finance. Although over 65 years have passed since then, the mean-variance model remains today the predominant model in portfolio selection. Having endured many criticisms over this period, the one that has perhaps been the most persistent is the fact that mainstream mean-variance theory is unable to accommodate additional criteria beyond expected return and variance. With investment decision-making having become more complex, this is a real problem as many problems with additional criteria exist and are only increasing in number and importance. In this paper, we review the papers that have been published that apply methods and procedures in an exact (as opposed to evolutionary) sense to address problems in portfolio selection with criteria beyond mean and variance. We also analyse the methodologies that allow the solution of the problem in a multiple criteria context, thus extending the features of the mean-variance approach that have caused portfolio theory to have such impact.

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

Local Action Groups and Rural Sustainable Development. A spatial multiple criteria approach for efficient territorial planning

Rural Sustainable Development is a very important topic under the European Union policy, and it is currently promoted through the European Agricultural Fund for Rural Development 2014–2020. This fund is managed at sub-regional level by the Community-Led Local Development approach that involves Local Action Groups in order to promote the objectives of Rural Sustainable Development within rural municipalities. Each Local Action Group applies the Strengths, Weaknesses, Opportunities and Threats analysis in order to identify for its own rural municipalities the strategic elements to which it will allocate the European Agricultural Fund for Rural Development budget. Nevertheless, this analysis has some general shortcomings, including difficulties in managing a large number of Strength and Weakness factors. In addition, the importance of each factor cannot be measured quantitatively, and the same factor may be characterized both as a Strength and a Weakness. Further difficulties may occur in the case of partnerships between different Local Action Groups, such as disagreement about whether a given factor is a Strength or a Weakness, lack of information about the relationships between Strength and a Weakness factors and decision alternatives, as well as impossibility of ranking the decision alternatives.Thus, this research aims to overcome the drawbacks of the Strengths, Weaknesses, Opportunities and Threats analysis and to support Local Action Group partnerships in the sustainability evaluation of their rural municipalities, and therefore to aid the identification of a common Rural Sustainable Development strategy to allocate the European Agricultural Fund for Rural Development budget. This decision problem was tackled by applying a Multiple Criteria Spatial Decision Support System that integrates a Geographic Information System with the Multiple Criteria Decision Aiding methods “Technique for Order Preference by Similarity to Ideal Solution” and “Dominance-based Rough Set Approach”.In order to demonstrate the validity of this methodological approach, this Multiple Criteria Spatial Decision Support System was applied to a study area of thirteen rural municipalities located in Apulia Region (Southern Italy); these municipalities belong to the same landscape unit, but they are managed by five different policy makers that represent the Local Action Groups. The results provided the maps of environmental, economic and social sustainability rankings of rural municipalities as well as their overall sustainability value. Based on these rankings, a specific Rural Sustainable Development strategy was identified for the allocation of the European Agricultural Fund for Rural Development. This methodology provided a common decision making framework that can also be applied to Local Action Group partnerships within the European Union.

Handling imprecise evaluations in multiple criteria decision aiding and robust ordinal regression by n-point intervals

We consider imprecise evaluation of alternatives in multiple criteria ranking problems. The imprecise evaluations are represented by n-point intervals which are defined by the largest interval of possible evaluations and by its subintervals sequentially nested one in another. This sequence of subintervals is associated with an increasing sequence of plausibility, such that the plausibility of a subinterval is greater than the plausibility of the subinterval containing it. We explain the intuition that stands behind this proposal, and we show the advantage of n-point intervals compared to other methods dealing with imprecise evaluations. Although n-point intervals can be applied in any multiple criteria decision aiding (MCDA) method, in this paper, we focus on their application in robust ordinal regression which, unlike other MCDA methods, takes into account all compatible instances of an adopted preference model, which reproduce an indirect preference information provided by the decision maker. An illustrative example shows how the method can be applied in practice.

SMAA-GAIA: a complementary tool of the SMAA-PROMETHEE method

PROMETHEE and GAIA are well-known multiple criteria decision aid methods. Given an evaluation table and preference parameters they allow to rank the alternatives, to visualise the problem, to perform sensitivity and robustness analysis, etc. Unfortunately, it is often hard for the decision maker (DM) to estimate the precise values of these parameters. Therefore an alternative option is to give ranges of potential values in order to apply stochastic multi-criteria acceptability analysis. This has been recently studied in the context of the SMAA-PROMETHEE method. The aim of this contribution is to propose a SMAA extension of GAIA. We show how this tool can be useful and provide complementary information to SMAA-PROMETHEE. This is illustrated on a pedagogical example.

SMAA-GAIA: a complementary tool of the SMAA-PROMETHEE method

PROMETHEE and GAIA are well-known multiple criteria decision aid methods. Given an evaluation table and preference parameters they allow to rank the alternatives, to visualise the problem, to perform sensitivity and robustness analysis, etc. Unfortunately, it is often hard for the decision maker (DM) to estimate the precise values of these parameters. Therefore an alternative option is to give ranges of potential values in order to apply stochastic multi-criteria acceptability analysis. This has been recently studied in the context of the SMAA-PROMETHEE method. The aim of this contribution is to propose a SMAA extension of GAIA. We show how this tool can be useful and provide complementary information to SMAA-PROMETHEE. This is illustrated on a pedagogical example.

Quality Evaluation Method of Dissertation for Master Degree Based on AHP

How to evaluate the quality of a dissertation for master degree is important for the postgraduate education. The evaluation of dissertation is a complex assignment. The evaluation process in certain extent is accompanied by randomness, fuzziness, uncertainty/intangible and instability. The Analytic Hierarchy Process (AHP) is a multiple criteria decision-aiding method developed by Saaty. In this paper a quantitative method for the evaluation of dissertation is put forward based on the analytic hierarchy process. Multiple evaluation criteria and sub-criteria are organized into a hierarchy. The relative importance of these criteria is accessed and different weight values are assigned to evaluation criteria. As the result, the evaluation process of the dissertation for master degree becomes more simple and convenient. The evaluation result is more objective and exact.

A new prioritized multi-criteria outranking method: The prioritized PROMETHEE

The Multiple Criteria Decision Aiding (MCDA) has been a fast growing area of operations research and management science during the last decades, while the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) is one of the well-known MCDA methods with ranking the alternatives from the best to the worst. In this paper, we propose a new outranking method called Prioritized PROMETHEE (P-PROMETHEE) method, which is used to deal with the ranking problem of Prioritized Multiple Criteria Decision Making (PMCDM). First, we use the idea of entropy to get the information of each criterion in the same prioritized level, which is used to get the weight of each criterion. Then, we use the weighted average operator to aggregate all the criteria in the same prioritized level. Moreover, we apply Yager's prioritized measure-guided aggregation operator to the classical PROMETHEE method to get the P-PROMETHEE method. Finally, we give a practical example to illustrate the effectiveness of the P-PROMETHEE method.

Non Additive Robust Ordinal Regression for urban and territorial planning: an application for siting an urban waste landfill

In this paper we deal with an urban and territorial planning problem by applying the Non Additive Robust Ordinal Regression (NAROR). NAROR is a recent extension of the Robust Ordinal Regression family of Multiple Criteria Decision Aiding methods to the Choquet integral preference model which permits to represent interaction between considered criteria through the use of a set of non-additive weights called capacity or fuzzy measure. The use of NAROR permits the Decision Maker (DM) to give preference information in terms of preferences between pairs of alternatives with which she is familiar, and relative importance and interaction of considered criteria. The basic idea of NAROR is to consider the whole set of capacities that are compatible with the preference information given by the DM. In fact, the recommendation supplied by NAROR is expressed in terms of necessary preferences, in case an alternative is preferred to another for all compatible capacities, and of possible preferences, in case an alternative is preferred to another for at least one compatible capacity. In the considered case study, several sites for the location of a landfill are analyzed and compared through the use of the NAROR on the basis of different criteria, such as presence of population, hydrogeological risk, interferences on transport infrastructures and economic cost. This paper is the first application of NAROR to a real-world problem, even if not already with real DMs, but with a panel of experts simulating the decision process.

Use of multiple criteria decision aid methods in case of large amounts of data

Cases of large amounts of data and high numbers of criteria for evaluation of socio–economic objects are rather frequent. Evaluation can comprise thousands of entries of data and dozens of different criteria. Quantitative methods of processing large amounts of data could be classified into two broad categories: statistical methods and multiple criteria decision aid (MCDA) methods. Statistical methods impose a number of rather strong limitations on data. In contrast, multiple criteria evaluation methods can deal with ill–defined problems and with multi–dimensional data. Results yielded by statistical methods can be comprised by specialists, while results yielded by the MCDA methods are specifically designed for decision–makers. The MCDA methods provide results in the form of ranking of alternatives by their preference to decision–makers of various backgrounds. Even if is a convenient way, it is not well–informative. In the paper, various techniques of choosing the most important criteria, of building a hierarchy of criteria, of retrieval of results of evaluation broadening usage of multiple criteria methods are proposed, making emphasis on cases with large amounts of data.