Multi-criteria Decision Analysis Problems Research Articles

Intuitionistic fuzzy gained and lost dominance score based on symmetric point criterion to prioritize zero-carbon measures for sustainable urban transportation

PurposeAchieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.Design/methodology/approachAn integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.FindingsThe results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.Originality/valueA new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

Hybrid Intuitionistic Fuzzy Entropy-SWARA-COPRAS Method for Multi-Criteria Sustainable Biomass Crop Type Selection

To select a biomass crop type of the highest sustainability for the purpose of producing biofuel is recognized as a problem of the multi-criteria decision analysis (MCDA) type, as it comprises different conflicting criteria. To effectively address this problem, the present paper introduces a novel integrated approach using the complex proportional assessment (COPRAS) method under the intuitionistic fuzzy sets (IFSs). The proposed approach works based on the IFSs operators as well as an innovative process utilized in evaluating the attributes’ weights. To evaluate these weights, the subjective weights using the step-wise weight assessment ratio analysis (SWARA) model are integrated with the objective weights achieved using an entropy-based approach in order to attain more realistic weights. As MCDA problems inevitably suffer from different degrees of uncertainty, the proposed approach could be of great help to those who are required to make decisions in uncertain settings. The paper took into consideration a sustainable biomass crop selection problem to exemplify the effectiveness of the presented approach in handling real MCDA problems. Moreover, a sensitivity analysis with respect to the diverse values of the attributes is presented in order to assess the stability of the introduced model. This study reveals that the combination of the objective and subjective weights enhances the stability of the introduced approach with diverse attribute weights. Finally, the results of the introduced model are compared to some existing intuitionistic fuzzy information-based methods. The findings of the comparison confirm the efficiency of the present approach in performing the defined tasks under uncertain environments.

Dual Probabilistic Linguistic Full Consistency Additive Ratio Assessment Model for Medical Equipment Supplier Selection

In comparison to probabilistic linguistic term sets (PLTSs), dual probabilistic linguistic term sets (DPLTSs) reveal the complexity and uncertainty of this realistic world more accurately. The power average operator can mitigate the consequences of some decision-makers’ strong biases while evaluating data. Additionally, the Dombi operators during the phase of aggregation are very adaptable with general parameter. Inspired by these topics, we propose some dual probabilistic linguistic Dombi power aggregation operators to aggregate the decision-making expert’s preferences and various elegant properties of these proposed dual probabilistic linguistic (DPL)-aggregating operators. Further, we establish an integrated method with the combination of proposed DPL-aggregation operators, full consistency method (FUCOM) and additive ratio assessment (ARAS) model called the “DPL-FUCOM-ARAS” methodology for solving multi-criteria decision analysis problems under dual probabilistic linguistic environment. In this methodology, the FUCOM is applied to determine criteria weight, while the ARAS model is presented to prioritize the options from dual probabilistic linguistic perspective. We present a case study on the selection of a medical equipment supplier to highlight the applicability and show the usefulness of the proposed DPL-FUCOM-ARAS methodology. Further, we discuss the sensitivity investigation and comparative discussion to check the stability and robustness of the proposed DPL-FUCOM-ARAS methodology.

A data-driven approach to objective evaluation of urban low carbon development performance

An effective evaluation of a city's low carbon development plays an essential role in promoting low carbon development strategies for achieving the city's sustainable development. This paper proposes a data-driven approach to objectively evaluating the low carbon development level of cities. The approach formulates the low carbon development evaluation problem as a multi-criteria decision analysis problem and incorporates the merits of bibliometric analysis, text mining and optimal weighting to evaluating the urban low carbon development performance. The bibliometric analysis is applied to systematically identify evaluation criteria and associated indicators and establish an evaluation system for measuring low carbon development levels of urban cities. Equipped with an objective weighting method based on text mining, the approach determines the local weights of the evaluation criteria and indicators for each city by extracting subjective preferential information from the policy documents available on the local government's websites. Two optimal weighting models are developed to determine the optimal global weights of the indicators and criteria by maximizing the low carbon development performance of all cities. The obtained criteria weighting thus can reflect both the preferences of local city governments and the best common interest of all cities involved in the evaluation. The approach is then illustrated with a case study on three cities involved in urban agglomeration planning in China. The results compare the low carbon development performance of the cities, identify the disparities between the cities and reveal each city's obstacle factors that hinder its development. Policy recommendations are then suggested for developing effective low carbon development policies.

Interval-valued fermatean fuzzy sets with multi-criteria weighted aggregated sum product assessment-based decision analysis framework.

Fermatean fuzzy set, a generalization of the fuzzy set, is a significant way to tackle the complex uncertain information that arises in decision-analysis procedure and thus can be employed on a wider range of applications. Due to the inadequacy in accessible data, it is hard for decision experts to exactly define the belongingness grade (BG) and non-belongingness grade (NG) by crisp values. In such a situation, interval BG and interval NG are good selections. Thus, the aim of the study is to develop the doctrine of interval-valued Fermatean fuzzy sets (IVFFSs) and their fundamental operations. Next, the score and accuracy functions are proposed for interval-valued Fermatean fuzzy numbers (IVFFNs). Two aggregation operators (AOs) are developed for aggregating the IVFFSs information and discussed some axioms. Further, a weighted aggregated sum product assessment method for IVFFSs using developed AOs is introduced to handle the uncertain multi-criteria decision analysis problems. A case study of e-waste recycling partner selection is also considered to elucidate the feasibility and efficacy of the introduced framework. Finally, sensitivity and comparative analyses are given to elucidate the reliability and robustness of the obtained results.

A Study on Evaluating Water Resources System Vulnerability by Reinforced Ordered Weighted Averaging Operator

Evaluating the vulnerability of a water resources system is a multicriteria decision analysis (MCDA) problem including multiple indictors and different weights. In this study, a reinforced ordered weighted averaging (ROWA) operator is proposed by incorporating extended ordered weighted average operator (EOWA) and principal component analysis (PCA) to handle the MCDA problem. In ROWA, the weights of indicators are calculated based on component score coefficient and percentage of variance, which makes ROWA avoid the subjective influence of weights provided by different experts. Concretely, the applicability of ROWA is verified by assessing the vulnerability of a water resources system in Handan, China. The obtained results can not only provide the vulnerable degrees of the studied districts but also denote the trend of water resources system vulnerability in Handan from 2009 to 2018. And the indictor that most influenced the outcome is per capita GDP. Compared with EOWA referred to various indictor weights, the represented ROWA shows good objectivity. Finally, this paper also provides the vulnerability of the water resource system in 2025 based on ROWA for water management in Handan City.

Sustainable design of large wastewater treatment plants considering multi-criteria decision analysis and stakeholders’ involvement

The typical treatment scheme of a large municipal wastewater treatment plant (LWWTP) is almost always the result of design based on technical and economic criteria. Once a threshold in terms of population equivalent (PE) is reached, it is possible that additional sludge thermal treatment might be required. Aspects such as greenhouse gas (GHG) emissions and land use for the construction of the WWTP or the service landfill are considered marginal in current design practice; in a world that requires increasingly attention to the environment, these criteria cannot be ignored when defining the treatment scheme of a LWWTP. With the intent of providing a sustainable approach to design, this study aims to identify the best treatment scheme for a LWWTP with a 720,000 PE size. Methodologically, the study involves the development of an approach based on multi-criteria decision analysis (MCDA). Six alternative treatment schemes were considered; two simplified schemes, without primary sedimentation, with extended aeration activated sludge processes and aerobic sludge stabilization; four full schemes, with primary sedimentation and anaerobic sludge digestion. Some schemes differ for the organic loading rate applied; others for the use of sludge incineration. Subsequently, six evaluation criteria (ECs) such as GHG emissions, electricity consumption, running costs, WWTP planimetric area, surface for the service landfill, as well as WWTP as biorefinery have been considered. The weighting of the ECs involved the participation of the main stakeholders in such a decision-making process, following a bottom-up approach. The resolution of the MCDA problem allowed the identification of the full scheme based on primary sedimentation, biological activated sludge at low organic load (0.210 kgBOD5/kgVSS/d) and anaerobic sludge digestion as the best solution. The sensitivity analysis, able to indirectly take into account the multitude of decision makers involved, allowed corroborating the results. The obtained treatment scheme was different from that generally adopted in current design practice for LWWTPs.

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization.

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.

Parallel and Distributed Computing for large raster-based Spatial Multicriteria Decision Analysis Problems: A Computational Performance Comparison

This article focuses on a cluster-based parallel and distributed approach for large raster datasets in the context of Spatial Multicriteria Decision Analysis (S-MCDA). The research addresses a land-prioritization model with respect to conservation practices. The reliability of the model results is examined using a variance-based Spatially-Explicit Uncertainty and Sensitivity (SEUSA) framework. The original case study area to which we applied the model was located in southwest Michigan, USA, and incorporated millions of mapping units (pixels). As part of the model sensitivity analysis, several thousand intermediate raster datasets representing suitability surfaces are generated by means of a Monte Carlo Simulation (MCS). The creation of the suitability surfaces represents the most time-consuming and memory-intensive step within the SEUSA framework. Sequential computational approaches to implementing SEUSA often have to accept a compromise with respect to problem size and the number of simulations, resulting in the low quality of the model sensitivity measures. This article presents the concept and implementation of a distributed and parallel solution based on the Python-Dask framework in order to improve the quality of SEUSA results for computationally-intensive spatial models.

A framework for selecting cloud computing services based on consensus under single valued neutrosophic numbers

Many organizations are seeking to contract services from cloud computing with many cloud servicesnumerous criteria that should be counted in the selection process. Therefore, the selection process of cloud services can be cosidered as a type of multi-criteria decision analysis problems with multiples stakeholders. In this paper a framework for selecting cloud services taking into account consensus and using single valued neutrosophic numbers for indeterminacy representation is presented. The proposed framework is composed of five activities: framework, gathering parameters, eliciting preferences, computing consensus degree, advice generation,automatic search mechanisms for conflict areas and recommendations to the experts to bring closer their preferences. An illustrative example that corroborates the applicability of the model is presented. The paper ends with conclusion and further areas or research recommendations.

NMCDA: A framework for evaluating cloud computing services

Many organizations are currently seeking to contract services from cloud computing rather than owing the possessions to supply those services. Due to the fast expansion of cloud computing, many cloud services have been developed. Any organization that tries to achieve the best flexibility and quick response to market requests, they have the options to use cloud services. Due to the diversity of cloud service providers, it is a very significant defy for organizations to select the appropriate cloud services which can fulfill their requirements, as numerous criteria should be counted in the selection process of cloud services. Therefore, the selection process of cloud services can be considered as a type of multi-criteria decision analysis problems. In this research paper, we present how to aid a decision maker to estimate different cloud services by providing a neutrosophic multi-criteria decision analysis (NMCDA) approach for estimating the quality of cloud services. Triangular neutrosophic numbers are used to deal with ambiguous and incompatible information which exist usually in the performance estimation process. An efficacious model is evolved depending on neutrosophic analytic hierarchy process (NAHP). The aim is to solve the performance estimation problem and improve the quality of services by creating a strong competition between cloud providers. To demonstrate the pertinence of the proposed model for disbanding the multi-criteria decision analysis, a case study is presented.

Multicriteria Decisions in Urban Energy System Planning: A Review

Urban energy system planning (UESP) is a topic of growing concern for cities in deregulated energy markets, who plan to decrease energy demand, reduce their dependency on fossil fuels, and increase the share of renewable energy sources. UESP being a highly multi-sectoral and multi-actor task, multi-criteria decision analysis (MCDA) methods are frequently used in the decision processes. These methods may provide support in organizing and identifying solutions to problems with conflicting objectives. However, knowing which method to use is generally not straightforward, as the appropriateness of a method or combination of methods depends on the decision problem’s context. This article therefore reviewed scientific papers to characterize and analyze MCDA problems and methods in the context of UESP. The review systematically explores issues such as the scope of the problems, the alternatives and criteria considered, the expected decision outcomes, the decision analysis methods and the rationales for selecting and combining them, and the role of values in driving the decision problems. The final outcome is a synthesis of the data and insights obtained, which may help potential users identify appropriate decision analysis methods based on given problem characteristics.

Fuzzy multi-criteria acceptability analysis: A new approach to multi-criteria decision analysis under fuzzy environment

New MCDA approach under fuzzy contexts implements acceptability analysis concept.Fuzzy Rank Acceptability Analysis gives a ranking and a confidence degree about it.A fuzzy extension of MAVT method within the FMAA is implemented.The study of the overestimation problem with fuzzy arithmetic is studied and fixed. Uncertainty is one of the main difficulties that increases the complexity of multi-criteria decision analysis (MCDA) problems, and often uncertainty cannot be managed by probabilistic models. In such cases, the use of fuzzy methods has been successfully applied to multi-criteria decision methods in which the ranking of fuzzy quantities is crucial for the decision analysis. This paper aims to introduce a new approach to MCDA problems defined under fuzzy contexts that implements the concept of acceptability analysis, Fuzzy Multi-Criteria Acceptability Analysis (FMAA), based on the Fuzzy Rank Acceptability Analysis (FRAA), that provides a ranking and a confidence degree about the ranking of fuzzy quantities. Based on the fuzzy extension of MAVT method, the FMAA is implemented and then applied to a case study, and its results are compared with other well-known MCDA methods in order to show its validity, interpretability and consistency.

The integrated model of GIS and AHPVIKOR for evaluating sustainable land-use management

Evaluation of sustainable land-use management (ESLM) is basically a multicriteria decision analysis (MCDA) because it always has to take into considerations several different issues from socioeconomic to ecological and environmental ones. In this study a model is developed to handle MCDA problems by integrating GIS and AHP - VIKOR (The name VIKOR appeared in 1990 from Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multicriteria Optimization and Compromise Solution). The model consists of three steps: (i) Identify sustainability indicators using the FAO approach (1993b, 2007); (ii) Calculate the weights of each sustainability indicators using AHP; (iii) thematic layers connected to each sustainability indicator are built in GIS then overlaid to calculate suitability index (Si) for each region using VIKOR, finally rank Si to determine regions suitable for each land use purpose. Comparing of the results of this model (GIS and AHP-VIKOR) and the results of GIS and AHPTOPSIS, GIS and AHP -WAM (calculate Si for each region by Weighted Average Method) under the same conditions of Duc Trong district - Lam Dong province. The similarities and differences of three models are also discussed, and then evaluated each model.

Integrated fuzzy analytic hierarchy process and VIKOR method in the prioritization of pavement maintenance activities

Maintenance activities and pavement rehabilitation require the allocation of massive finances. Yet due to budget shortfalls, stakeholders and decision-makers must prioritize projects in maintenance and rehabilitation. This article addresses the prioritization of pavement maintenance alternatives by integrating the fuzzy analytic hierarchy process (AHP) with the VIKOR method (which stands for ‘VlseKriterijumska Optimizacija I Kompromisno Resenje,’ meaning multi-criteria optimization and compromise solution) for the process of multi-criteria decision analysis (MCDA) by considering various pavement network indices. The indices selected include the pavement condition index (PCI), traffic congestion, pavement width, improvement and maintenance costs, and the time required to operate. In order to determine the weights of the indices, the fuzzy AHP is used. Subsequently, the alternatives’ priorities are ranked according to the indices weighted with the VIKOR model. The choice of these two independent methods was motivated by the fact that integrating fuzzy AHP with the VIKOR model can assist decision makers with solving MCDA problems. The case study was conducted on a pavement network within the same particular region in Tehran; three main streets were chosen that have an empirically higher maintenance demand. The most significant factors were evaluated and the project with the highest priority was selected for urgent maintenance. By comparing the index values of the alternative priorities, Delavaran Blvd. was revealed to have higher priority over the other streets in terms of maintenance and rehabilitation activities.

Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.

Multi-criteria decision assessments using Subjective Logic: Methodology and the case of urban water strategies

Summary Planning of regional and urban water resources, and in particular with Integrated Urban Water Management approaches, often considers inter-relationships between human uses of water, the health of the natural environment as well as the cost of various management strategies. Decision makers hence typically need to consider a combination of social, environmental and economic goals. The types of strategies employed can include water efficiency measures, water sensitive urban design, stormwater management, or catchment management. Therefore, decision makers need to choose between different scenarios and to evaluate them against a number of criteria. This type of problem has a discipline devoted to it, i.e. Multi-Criteria Decision Analysis, which has often been applied in water management contexts. This paper describes the application of Subjective Logic in a basic Bayesian Network to a Multi-Criteria Decision Analysis problem. By doing this, it outlines a novel methodology that explicitly incorporates uncertainty and information reliability. The application of the methodology to a known case study context allows for exploration. By making uncertainty and reliability of assessments explicit, it allows for assessing risks of various options, and this may help in alleviating cognitive biases and move towards a well formulated risk management policy.

ASSESSMENT OF NUCLEAR WASTE REPOSITORY OPTIONS USING THE ER APPROACH

Two technically feasible nuclear waste repository options have been identified in Belgium. To select one for implementation, a study was carried out to compare the public perception and acceptance of the two options. In this paper, it is described how the study and selection process can be supported, and how the diversity and uncertainty in public opinions can be rationally modeled and analyzed by applying the Evidential Reasoning (ER) approach. The ER approach is a recent advancement for multi-criteria decision analysis (MCDA). By using belief decision matrices to model MCDA problems, both qualitative and quantitative information with various types of uncertainties can be taken into account in decision-making processes in a unified and logical format. Following an illustration of the ER approach and an outline of the selection problem, it is described how the ER approach can be used to model the problem, aggregate information, and facilitate sensitivity analysis. Some suggestions are made for future assessment studies.

The Impact of Aggregating Benefit and Cost Criteria in Four MCDA Methods

Multicriteria decision analysis (MCDA) problems (also known as multicriteria decision-making or MCDM) involve the ranking of a finite set of alternatives in terms of a finite number of decision criteria. Often times such criteria may be in conflict with each other. That is, an MCDA problem may involve both benefit and cost criteria at the same time. Although this is a frequent characteristic of many real-life MCDA problems, this subject has not received adequate attention in the literature. This paper examines the use of four key MCDA methods when two approaches for dealing with conflicting criteria are used. The two approaches are the benefit to cost ratio approach and the benefit minus cost approach. The MCDA methods used in this study are the weighted sum model, the weighted product model, and the analytic hierarchy process (AHP) along with some of its variants, including the multiplicative AHP. Not surprisingly, these two approaches for aggregating conflicting criteria may result in a different indication of the best alternative or ranking of all alternatives when they are used on the same problem. As it is demonstrated here, it is also possible for the two approaches to even result in the opposite ranking of the alternatives. An extensive empirical analysis of this methodological problem revealed that the previous phenomena might occur frequently on simulated MCDA problems. The WSM, the AHP, and the revised AHP performed in an almost identical manner in these tests. The contradiction rates in these tests were rather significant and became more dramatic when the number of alternatives was high. Although it may not be possible to know which ranking is the "correct" one, this study also theoretically proved that the multiplicative AHP is immune to these ranking inconsistencies.

Multicriteria decision aid application to a student selection problem

The selection of students is a complex decision making process, in which multiple selection criteria often need to be considered and where subjectiveness and imprecision are usually present, resulting that fuzzy and imprecise data should be used. This paper formulates the student selection process as a multicriteria decision analysis problem, concretely as a ranking problem, by using the ELECTRE III methodology to construct a fuzzy outranking relation, and then a genetic algorithm to exploit it and to obtain a ranking in decreasing order of preference. An empirical study of a real selection problem in the Universidad de Occidente in Mexico is presented. After performing calculations and obtaining the final ranking of compared alternatives, a sensitivity analysis was carried out. The study was supported with a new decision aid system for rank a finite set of multicriteria alternatives.