Distance Metrics Library for MCDA Methods

Information systems based on Multi-Criteria Decision Analysis (MCDA) methods enable considering multiple attributes with contrary objectives. Information systems using MCDA simplify and automatize assessment toward automatizing decision support systems. Individual MCDA methods differ in their algorithms, implying different results for the same problem. Moreover, the diversity of algorithms refers to the MCDA methods and their techniques used at an individual stage, such as distance metrics. They are implemented in MCDA methods to measure alternatives’ distances from reference solutions. The most commonly used metric is the Euclidean distance. However, other distance metrics are also suitable for this purpose. Moreover, a broad set of metrics can be helpful in comparative analysis to test the robustness of particular scenarios. Therefore, the main contribution of a Python library for multi-criteria decision analysis called distance-metrics-mcda is providing a set of 20 distance metrics for benchmarking purposes. The implemented library offers an autonomous tool for evaluating any decision problem. The presented library is an important addition to decision support systems based on MCDA methods as it provides additional possibilities for analysis of scenarios’ reliability

Towards MCDA Based Decision Support System Addressing Sustainable Assessment

Nowadays, the efforts to achieve sustainable development in many areas are a challenge creating the necessity to introduce innovations in management. Objective assessment of realization of set goals requires taking into account many often conflicting criteria. This work aims to present an attempt to implement a decision support system (DSS) based on multi-criteria decision analysis methods for autonomous sustainable evaluation in any problem area. The system’s capabilities are illustrated in the example of using renewable energy sources in European countries. This problem is one of the current challenges related to climate change and exhaustion of natural energy resources, which forces changes in energy policy, taking into account, among others, the increase in the share of renewable energy sources in many branches of the economy. The obtained results prove that the system proposed by the authors is an appropriate and valuable tool for assessing sustainability in problems involving various areas

Temporal VIKOR - A New MCDA Method Supporting Sustainability Assessment

Sustainability is a widely incorporated trend into national policies. It implies developing metrics and indicators to measure sustainability. It is evidenced by the development of Sustainable Development Goals (SDG), included in the 2030 Agenda for Sustainable Development proposed by the United Nations (UN). A framework with a complete set of indicators requires a tool including all objectives simultaneously, such as Multi-Criteria Decision Analysis (MCDA) methods. Due to the dynamic nature of sustainable development, a static MCDA approach to evaluating current performance is insufficient. Therefore, this paper proposes a framework with a newly developed method called the Temporal VIKOR and measurement of data variability that allows aggregation of alternatives’ efficiency over investigated time. The practical application of this framework is presented in the temporal assessment of sustainable cities and communities concerning the UN’s SDG 11 frame. The results prove the proposed framework’s potential usefulness for sustainability assessment information systems

TOWARDS RELIABLE DECISION-MAKING IN THE GREEN URBAN TRANSPORT DOMAIN

Operational research is a scientific discipline related to the decision theory that allows determining solutions for specific problems related to, for example, widely understood transport. Increasingly popular in this field are issues related to the domain of the green urban transport. In order to support the decision-making process in this area, methods of multi-criteria decision analysis (MCDA) are used more and more often. However, if we solve a specific problem using different MCDA methods, we get different rankings, as each method has a different methodological basis. Therefore, the challenge is how to make a reliable decision. This paper presents a numerical example from the green urban transport domain, which is solved by six different MCDA methods that return a complete ranking. We measure the similarity of these rankings using coefficients rw and WS, and then we propose a simple way of determining a compromise solution. The obtained compromise ranking is guaranteed to be the best match to the selected MCDA methods' rankings, which is proved in the paper. Finally, possible directions for further development work are identified

Identification of weights in multi-cteria decision problems based on stochastic optimization

Many scientific papers are devoted to solving multi-criteria problems using methods that find discrete solutions. However, the main challenge addressed by our work is the case when new decision-making variants have emerged which have not been assessed. Unfortunately, discrete identification makes it impossible to determine the preferences for new alternatives if we do not know the whole set of parameters, such as criteria weights. This paper proposes a new approach to identifying a multi-criteria decision model to address this challenge. The novelty of this work is using a discretization in the space of the problem to identify a continuous decisional model. We present a hybrid approach where the new alternative can be assessed based on stochastic optimization and the TOPSIS technique. The stochastic methods are used to find criteria weights used in the TOPSIS method. In that way, we get assessed easily each new alternative based only on the initial set of evaluated alternatives

Towards Sustainable Cloud Services: MCDA Approach

Cloud technologies are important in the growing need for advanced information systems, enabling location-independent delivery of products or services. However, it is essential to consider their environmental sustainability amidst their expanding capabilities. In this work, we propose using the ARAS-COMET approach, a hybrid method for assessing the considered alternatives. Furthermore, to verify the robustness of the results, a simple sensitivity analysis is applied to examine the impact of different weight distributions on the ranking. The presented approach showed that it has the right to be used in a real-world scenario to make decisions regarding the sustainability of cloud solutions

STUDY TOWARDS THE TIME-BASED MCDA RANKING ANALYSIS – A SUPPLIER SELECTION CASE STUDY

Decision-making processes increasingly use models based on various methods to ensure professional analysis and evaluation of the considered alternatives. However, the abundance of these methods makes it difficult to choose the proper method to solve a given problem. Also, it is worth noting whether different results can be obtained using different methods within a single decision problem. In this paper, we used three selected Multi-Criteria Decision Analysis (MCDA) methods called COMET, TOPSIS, and SPOTIS in order to examine how the obtained rankings vary. The selection of material suppliers was taken into consideration. The equal weights, entropy and standard deviation methods were used to determine the weights for criteria. Final preferences values were then compared with the WS similarity coefficient and weighted Spearman correlation coefficient to check the similarity of the received rankings. It was noticed that in the given problem, all of the methods provide highly correlated results, and the obtained positional rankings are not significantly different. However, practical conclusions indicate the need to look for improved solutions in the correct and accurate assessment of suppliers in a given period

Identification of an expert decision-making model to solving multi-criteria problems using the characteristic objects method

W artykule przedstawiono nowe podejście do rozwiązywania wielokryterialnych problemów decyzyjnych, polegające na identyfikacji ekspertowego modelu decyzyjnego w przestrzeni stanu problemu. Metoda obiektów charakterystycznych identyfikuje model decyzyjny z wykorzystaniem stałych punktów odniesienia oraz teorii zbiorów rozmytych. Metoda ta jest całkowicie odporna na zjawisko rank reversal, czyli odwracania rankingów przy dodaniu nowej alternatywy lub w momencie usunięcia alternatywy ze zbioru już rozpatrywanych obiektów. Za pomocą metody obiektów charakterystycznych identyfikowany jest model oceny ryzyka wystąpienia ataku serca u pacjenta w okresie najbliższych 10 lat, w celu lepszego zobrazowania działania metody COMET.Ahe paper presents a new approach to solving Multi-criteria decision-making problems. The presented approach identifies an expert decision-making model in the state of the problem. The characteristic objects method identifies a model by using fuzzy set theory and characteristic objects as reference objects. This method is complete free of the rank reversal phenomenon. It means that if new alternatives are added or removed from alternatives set then rank existing order will be not changed. The characteristic objects method will be used to identify the multi-criteria model of 10-year risk of having a heart attack

Reduction in the Number of Comparisons Required to Create Matrix of Expert Judgment in the Comet Method

Multi-criteria decision-making (MCDM) methods are associated with the ranking of alternatives based on expert judgments made using a number of criteria. In the MCDM field, the distance-based approach is one popular method for receiving a final ranking. One of the newest MCDM method, which uses the distance-based approach, is the Characteristic Objects Method (COMET). In this method, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic ob jects and their values. For this purpose, the domain and fuzzy numbers set for all the considered criteria are determined. The characteristic objects are obtained as the combination of the crisp values of all the fuzzy numbers. The preference values of all the characteristic ob ject are determined based on the tournament method and the principle of indifference. Finally, the fuzzy model is constructed and is used to calculate preference values of the alternatives. In this way, a multi-criteria model is created and it is free of rank reversal phenomenon. In this approach, the matrix of expert judgment is necessary to create. For this purpose, an expert has to compare all the characteristic ob jects with each other. The number of necessary comparisons depends squarely to the number of ob jects. This study proposes the improvement of the COMET method by using the transitivity of pairwise comparisons. Three numerical examples are used to illustrate the efficiency of the proposed improvement with respect to results from the original approach. The proposed improvement reduces significantly the number of necessary comparisons to create the matrix of expert judgment

Version [1.1]- [pyFDM: A python library for uncertainty decision analysis methods]

This paper describes an extension of the pyFDM library by extending the functionalities of the programming package with new Fuzzy Multi-Criteria Decision Analysis methods such as Combined Compromise Solution (CoCoSo), Stable Preference Ordering Towards Ideal Solution (SPOTIS), Weighted Aggregated Sum Product ASSessment (WASPAS), Weighted Product Model (WPM), and Weighted Sum Model (WSM). Furthermore, the proposed update includes new normalization techniques, new distance metrics, and defuzzification methods to allow users to examine the greater variety of research approaches in a Triangular Fuzzy environment. Furthermore, the update also extends with the implementations allowing for fuzzy calculations outside the multi-criteria field, which is allowed by the implementation of selected mathematical operations for Triangular Fuzzy Numbers. The opportunity to visualize the data within the fuzzy extension considered is also included. The extension of the pyFDM package broadens the scope of its usage in research approaches, making it a more suitable tool for multi-criteria decision analysis within the fuzzy environment