Multiple Criteria Decision Analysis Methods Research Articles

Assessing climate change vulnerability of coastal roads

Climate change is a global phenomenon, which affects in several ways different regions all around the world, beyond the rise in global temperature. Among the different climate change issues, the management of transport infrastructures is crucial. Particularly, their vulnerability against changes in climatic conditions should be assessed. Vulnerability indicators are based on the IPCC concept of vulnerability and can be defined as a function of Climate Exposure, Sensitivity, and Adaptive Capacity. These dimensions need to be addressed during the assessment making and can be modelled as a Multiple Criteria Decision Analysis (MCDA) problem. This study proposes an integrated approach of several MCDA methods as a possible tool for ranking the climate change vulnerability of coastal roads in Malta. The application covers six coastal roads in the islands of Malta, classified by three different MCDA methods. The results indicate that the proposed approach can produce a consistent ranking of the climate change vulnerability of coastal roads. The study provides policy and decision-makers with a definition of a coastal road, an inventory of such roads, a list of climate change impacts, and a mathematical model incorporating climate change vulnerability indicators. The model can be used to prioritize investment and plan climate change adaptation strategies for infrastructural works on coastal roads.

Impact of Industry 4.0 Technologies on Sustainable Manufacturing

Industry 4.0 (I4.0) and its associated technologies are increasingly becoming a key aspect of the manufacturing industry in an age where sustainable manufacturing has become a fundamental consideration. Despite Industry 4.0 technologies such as robotics, autonomous systems and Internet of Things (IoT) considered dominant enablers of sustainable manufacturing, the precise impact of these technologies on sustainability remains relatively unexplored as the research in this context remains limited. Therefore, the need for meticulous study and the development of a framework for the assessment of I4.0 technologies’ impact towards enhancement of sustainable manufacturing is evident. The report contains an extensive literature review on several I4.0 technologies, the Triple Bottom Line (TBL) aspects of sustainability and how the respective I4.0 technologies impact the factors contributing to the TBL aspects of sustainability. Through a combination of theoretically available information and practical case studies in the Sri Lankan manufacturing industry, a conclusive framework is developed on how the implementation of the identified Industry 4.0 technologies contribute to achieving holistic sustainability across the Triple Bottom Line aspects. Additionally, through further study and multiple-criteria decision analysis methods, the most sustainably impactful technologies are determined with regards to the manufacturing industry. KEYWORDS: Industry 4.0, Sustainable Manufacturing, Impact of Industry 4.0, Sustainability, Triple Bottom Line

Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: An application of accident prediction models in Pakistan

In Pakistan, the assessment of road safety measures within road safety management systems is commonly seen as the most deficient part. Accident prediction models are essential for road authorities, road designers, and road safety specialists. These models facilitate the examination of safety concerns, the identification of safety improvements, and the projection of the potential impact of these modifications in terms of collision reduction. In the context described above, the goal of this paper is to utilize the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), a new and useful decision tool with a strong ability to address uncertain or imprecise information in practical decision-making processes. In addition, for dealing with the multi-attribute group decision-making problems in road safety management, this paper proposes a new 2TLq-ROF integrated determination of objective criteria weights (IDOCRIW)-the qualitative flexible multiple criteria (QUALIFLEX) decision analysis method with a weighted power average (WPA) operator based on the 2TLq-ROF numbers. The IDOCRIW method is used to calculate the weight of attributes and the QUALIFLEX method is used to rank the options. To show the viability and superiority of the proposed approach, we also perform a case study on the evaluation of accident prediction models in road safety management. Finally, the results of the experiments and comparisons with existing methods are used to explain the benefits and superiority of the suggested approach. The findings of this study show that the proposed approach is more practical and compatible with other existing approaches.

Adding value to the VRIO framework using DEMATEL

PurposeStrategic management plays a pivotal role in the growth and success of organizations, significantly impacting their business performance and competitive edge. In today’s globalized markets, strategic management is gaining prominence as a means to enhance company performance and distinctiveness. Within this landscape, the evaluation of organizational effectiveness takes on heightened significance, complemented by the emergence of strategic management frameworks designed to secure enduring competitive advantages for businesses.Design/methodology/approachOne of the best known approaches to organizational resource assessment is the value, rarity, inimitability and organizational-oriented (VRIO) framework. However, this tool comes with inherent limitations that have hindered its advancement, primarily related to the need for less subjective means of identifying and assigning weights to resources and capabilities during the evaluation process. This study, therefore, endeavored to refine the VRIO framework, making it more transparent and empirically robust. To achieve this aim, the framework was combined with the decision-making trial and evaluation laboratory (DEMATEL) method (i.e. a multiple criteria decision analysis (MCDA) method), and a real-life application was conducted.FindingsThe evaluation system created was tested, and the results demonstrate that the dual methodology used can increase the understanding of resource appraisal and lead to more informed and potentially better evaluations of resources and capabilities. The strengths and shortcomings of this new structured evaluation model are also analyzed.Originality/valueThe authors know of no prior work reporting the integrated use of VRIO and DEMATEL in this study context.

Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return

Financial performance analysis is of vital importance those involved in a business (e.g., shareholders, creditors, partners, and company managers). An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results. Integrated performance measurement, by its nature, consists of multiple criteria with different levels of importance. Multiple Criteria Decision Analysis (MCDA) methods have become increasingly popular for solving complex problems, especially over the last two decades. There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods. This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA, CRITIC, and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance. In this study, we propose "stock returns" as a benchmark in comparing and evaluating MCDA methods. Moreover, we calculate the "rank reversal performance of MCDA methods". Finally, we performed a "standard deviation" analysis to identify the objective and characteristic trends for each method. Interestingly, all these innovative comparison procedures suggest that PROMETHEE II (preference ranking organization method for enrichment of evaluations II) and FUCA (Faire Un Choix Adéquat) are the most suitable MCDA methods. In other words, these methods produce a higher correlation with share price; they have fewer rank reversal problems, the distribution of scores they produce is wider, and the amount of information is higher. Thus, it can be said that these advantages make them preferable. The results show that this innovative methodological procedure based on 'knowledge discovery' is verifiable, robust and efficient when choosing the MCDA method.

Applicability of multi-criteria decision analysis in the site selection procedure for high-level radioactive waste final disposal: robustness-oriented comparison of emplacement concepts

Abstract. The verbal and argumentative approach predominantly practiced in previous repository projects is only comprehensible to the general public to a limited extent (FKT, 2021; Blattman et al., 2023). One option to improve the transparency and the comprehensibility of decision-making processes is to supplement verbal and argumentative approaches with multiple-criteria decision analysis (MCDA) (Geldermann and Lerche, 2014; Frieling et al., 2020; Blattman et al., 2023). In nuclear waste management, MCDA has been used sporadically with respect to disposal strategies and site selection procedures (Achillas et al., 2013; Frieling et al., 2020). For the German site selection procedure, Frieling et al. (2020) identified the potential suitability as well as the development and testing needs of MCDA. Collaborators of the Chair of Repository Safety at RWTH Aachen University are investigating the applicability of MCDA for specific decision problems in the German site selection procedure. One decision problem is the emplacement of high-level radioactive waste (HLW) packages, which is being researched in the context of a doctoral thesis. For this decision problem, influencing factors and interdependencies between decision criteria, for example operational and long-term safety, and between other decision processes in the site selection procedure need to be considered. In the research project presented here, the emplacement of radioactive waste packages is categorized into three concepts: (1) drift emplacement and (2) horizontal and (3) vertical borehole emplacement. All concepts are tracked internationally. In principle, the Bundesgesellschaft für Endlagerung (BGE) evaluates the three concepts as feasible. However, for the performance of the representative preliminary safety investigation (rvSU), the drift emplacement concept is preferred, which is verbally and argumentatively weighed in BGE (2022). In this present research, the applicability of MCDA to the decision of the emplacement concept with respect to the German site selection process is explored in four main iterative steps based on Belton and Stewart (2003): (1) decision problem definition, (2) problem structuring, (3) decision model creation, and (4) verification. Currently the decision problem is being structured. Preliminarily, testing of the applicability of the sequential MCDA methods Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II is planned, although the method selection has not been finalized (Brans, 1982; Brans and de Smet, 2016). Furthermore, the combination of different MCDA methods will be elicited. The expected outcome of the research project is a basic decision model for robustness-based comparisons of disposal concepts of HLW. The decision model has the potential to provide decision support and to identify areas for further research regarding safety investigations as well as an increase in transparency, understanding, and acceptance in the site selection procedure. When MCDA methods are adequately applied by the decision maker, benefits are possible in terms of problem structuring, information processing, integration of qualitative and quantitative factors, deeper insights into relevant factors, and identification of compromises (Geldermann and Lerche, 2014; Frieling et al., 2020). Furthermore, the research results are particularly significant for international site selection procedures that are at the beginning of the selection procedure or at a comparable level in the German procedure.

Evaluation of Jining Mining Subsidence Susceptibility Based on Three Multiple-criteria Decision Analysis Methods

This study selected ten condition factors from the perspectives of topography, geological structure, hydrology and human for mining subsidence mapping by two types of models (knowledge-driven model and data-driven model) by taking Jining City, China as an example. The knowledge-driven model is achieved by analytic hierarchy process (AHP), and the data-driven model is achieved by frequency ratio (FR), combined with fuzzy comprehensive evaluation (FCE) and entropy evaluation method (EWM). Based on these models, the areas under receiver operating characteristic curve are 0.777, 0.949, and 0.938, which means FR-FCE obtained the highest accuracy. The differences reflected by the three methods are concentrated in Liangshan County and central to northern areas of Weishan County. The best model was proposed for the protection measures and land use management. Finally, the factors system and methods used in this study can be widely applied to the mining subsidence susceptibility evaluation in plain areas.

A Multi-Criteria Decision-Making Model for Selecting a Maturity Model

This paper proposes a decision model for a maturity model choice problem via the multiple-criteria decision analysis method. The authors implemented the model in FITradeoff decision support system to select a project management maturity model for a Brazilian industry operating in the sector of distributing natural gas. FITradeoff is a flexible and interactive procedure of elicitation for multi-criteria additive models that requires only partial information from the decision maker (i.e., there is no need to elicit very detailed information from the decision maker, an approach that the decision maker can find laborious and tiring). The authors observed that the use of a multi-criteria approach imposes certain rigor and pattern on the decision process to select a maturity model in project management. Applying the model enabled comparison of information from the four maturity models and therefore selecting a project management maturity model based on the decision-maker preferences.

Review] Exploring an integrated decision-making model to enhance the employee-oriented built environment in urban green buildings: A Review

Integrating building technology systems into urban green buildings is an emerging building movement in developed countries to create a paradigm of a human-oriented built environment inside the workspaces. This paper thrives on aiming at the conversion of green buildings to smart-green facilities initiated to develop an employee-oriented built environment in urban green office buildings using decision-making models. The study identifies the factors affecting employee satisfaction as building-related factors, non-building-related factors, Multiple criteria decision analysis (MCDA) and Structural equation model (SEM) through a comprehensive literature review. Extensive bibliographic research has been driven for 21 years to understand the relationship between decision-making models and employee satisfaction in green office buildings. This paper provides an overall ideology of the impact of green office buildings and the conversion to buildings focusing on employee satisfaction. The study concludes by introducing an integrated MCDA and SEM method to create a lesser complex hybrid decision-making model to create an improved employee-friendly built environment.

Review] Proposing an integrated decision-making model to enhance the employee-oriented built environment in urban green buildings - A Review

Integrating building technology systems into urban green buildings is an emerging building movement in developed countries to create a paradigm of a human-oriented built environment inside the workspaces. This paper thrives on aiming at the conversion of green buildings to smart-green facilities initiated to develop an employee-oriented built environment in urban office green buildings using decision-making models. The study identifies the factors affecting employee satisfaction as building-related factors, non-building-related factors, Multiple criteria decision analysis (MCDA) and Structural equation model (SEM) through a comprehensive literature review. Extensive bibliographic research has been driven for 21 years to understand the relationship between decision-making models and employee satisfaction in green office buildings. This paper provides an overall ideology of the impact of green office buildings and the conversion to buildings focusing on employee satisfaction. The study concludes by introducing an integrated MCDA and SEM method to create a lesser complex hybrid decision-making model to create an improved employee-friendly built environment.

Review] Proposing an integrated decision-making model to enhance the employee-oriented built environment in urban green buildings: A Review

Integrating building technology systems into urban green buildings is an emerging building movement in developed countries to create a paradigm of a human-oriented built environment inside the workspaces. This paper thrives on aiming at the conversion of green buildings to smart-green facilities initiated to develop an employee-oriented built environment in urban office green buildings using decision-making models. The study identifies the factors affecting employee satisfaction as building-related factors, non-building-related factors, Multiple criteria decision analysis (MCDA) and Structural equation model (SEM) through a comprehensive literature review. Extensive bibliographic research has been driven for 21 years to understand the relationship between decision-making models and employee satisfaction in green office buildings. This paper provides an overall ideology of the impact of green office buildings and the conversion to buildings focusing on employee satisfaction. The study concludes by introducing an integrated MCDA and SEM method to create a lesser complex hybrid decision-making model to create an improved employee-friendly built environment.

Review] Proposing an integrated decision-making model to enhance the employee-oriented built environment in urban green buildings

Integrating building technology systems into urban green buildings is an emerging building movement in developed countries to create a paradigm of a human-oriented built environment inside the workspaces. This paper thrives on aiming at the conversion of green buildings to smart-green facilities initiated to develop an employee-oriented built environment in urban office green buildings using decision-making models. The study identifies the factors affecting employee satisfaction as building-related factors, non-building-related factors, Multiple criteria decision analysis (MCDA) and Structural equation model (SEM) through a comprehensive literature review. Extensive bibliographic research has been driven for 21 years to understand the relationship between decision-making models and employee satisfaction in green office buildings. This paper provides an overall ideology of the impact of green office buildings and the conversion to buildings focusing on employee satisfaction. The study concludes by introducing an integrated MCDA and SEM method to create a lesser complex hybrid decision-making model to create an improved employee-friendly built environment.

Review] Integrated decision-making model to enhance the employee-oriented built environment in urban green buildings

Integrating building technology systems into urban green buildings is an emerging building movement in developed countries to create a paradigm of a human-oriented built environment inside the workspaces. This paper thrives on aiming at the initiation of developing an employee-oriented built environment in urban office green buildings using decision-making models. The study identifies the factors affecting employee satisfaction as building-related factors, non-building-related factors, Multiple criteria decision analysis (MCDA) and Structural equation model (SEM) through a comprehensive literature review. Extensive bibliographic research has been driven for 21 years to understand the relationship between decision-making models and employee satisfaction in green office buildings. This paper provides an overall ideology of the impact of green office buildings and the conversion to buildings focusing on employee satisfaction. The study concludes by introducing an integrated MCDA and SEM method to create a lesser complex hybrid decision-making model to create an improved employee-friendly built environment.

A machine learning driven multiple criteria decision analysis using LS-SVM feature elimination: Sustainability performance assessment with incomplete data

The move towards an era of big data structured problems has led to the development of state-of-the-art solutions, mainly in decision sciences. Large Scale Decision-Making (LSDM) and Data-Driven Decision-Making (DDDM) approaches have emerged from a rapidly evolving research field. A common challenge in LSDM problems formed from various heterogeneous databases is incomplete data. Multiple criteria decision analysis approaches have been utilized as the primary element of LSDM techniques to solve similar complex problems, and these methods cannot function under incomplete data. Large number of dimensions in LSDM problems are usually problematic, which has previously been addressed by machine learning driven methods. Moreover, while in traditional multiple criteria decision analysis methods, the criteria are based on beneficial and non-beneficial attributes, in LSDM problems, decision-makers define specific objectives for attributes adjusting the problem with their specific preferences. This study proposed a machine learning-driven DDDM method for solving LSDM problems with incomplete data and a substantial number of decision attributes. Incomplete data imputation was conducted using the Expectation–Maximization (EM) algorithm, and Recursive Feature Elimination (RFE) with Least Square Support Vector (LS-SVM) was used to extract the core criteria. Target-based COmbined COmpromise Solution (T-CoCoSo) and Target-based Multi-Attributive Border Approximation Area Comparison (T-MABAC) methods combined with Shannon’s Entropy weighting method were used for the performance assessment stage. Ultimately, to validate the reliability of the proposed method, sustainability performance of all countries worldwide under Sustainable Development Goals (SDGs) with incomplete data was evaluated. The outcomes show that the proposed machine learning-driven data-driven decision-making method is a reliable tool for such LSDM problems.

Exploiting temporal features in multicriteria decision analysis by means of a tensorial formulation of the TOPSIS method

A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the criteria’ time-series since it can provide essential information for the decision-making (e.g., an improvement of the criteria). To deal with this issue, we propose a new approach to rank the alternatives based on the criteria time-series features (trend, variance, etc.). In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the tensors, instead of the classical matrix representation used in MCDA. For this, we propose an extension of the TOPSIS method to handle tensors rather than matrices. Computational results confirm that the proposed approach allows to rank the alternatives from a new perspective that can be meaningful to the decision-maker.

DGRA: Multi-sourcing and supplier classification through Dynamic Grey Relational Analysis method

Most multiple criteria decision analysis methods hardly guide decision-makers beyond supplier evaluation (by providing them with a ranking), and supplier selection and classification is effectively the decision-makers' job. The relative difference between the performance of different suppliers is usually overlooked even though this fact is functionally important in real-life multi-sourcing decisions. The current study proposed a system that not only evaluates suppliers through the Dynamic Grey Relational Analysis (Dynamic GRA), a generalized form of Deng's GRA, but also helps the decision-makers to distinguish the most reliable suppliers from the least reliable ones using a novel Rank Product Score (RPS), which allows the decision-makers to visualize the performance of each supplier on an exponential curve. Unlike the benchmark models, the normalization of input data is not mandatory in the proposed method, and uncertainty in input can be quantified even when it is not explicitly visible in the input. The new framework enables the procurement managers looking for strategic global multi-sourcing to select the suppliers from the top cluster on this curve. It is tested on a practical case from a big engineering organization with diverse suppliers, demonstrating its validity. The comparative analyses on three dimensions confirm its effectiveness and efficiency. It is of immediate practical use for building intelligent decision support systems in the purchasing environment.

Proper and improper uses of MCDA methods in energy systems analysis

Over the past few decades, the strategies to perform energy systems analysis have evolved into multiple criteria-based frameworks. However, there still remains a lack of guidance on how to select the most suitable Multiple Criteria Decision Analysis (MCDA) method. These methods provide different decision recommendations for the Decision Makers, including ranking, sorting, choice, and clustering of the alternatives (e.g., technologies or scenarios) under evaluation. They deal with a variety of data typologies and preferences, and lead Decision Makers in shaping the energy systems of the future. Here, we evaluate the MCDA methods used in 56 case studies performing energy systems analysis at different scales. We find that close to 60% of these studies chose an MCDA method that was not the most adequate for the respective decision problem. In particular, this concerned the use of weighting methods (e.g., Analytical Hierarchy Process) in MCDA approaches not suited for this type of weights, sub-optimal selection of MCDA techniques for specific types of problem statements, and lack of handling rather evident interactions in preference models. Our analysis demonstrates that these deficiencies can be overcome by using a recently developed methodology and software that support Decision Makers and analysts in selecting the most suitable MCDA method for a given type of decision-making problem.

Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals

In this paper, the optimization and multiple-criteria decision analysis (MCDA) of a stand-alone photovoltaic and battery energy system (PV-BES) has been used to supply power to a desalination plant in the United Arab Emirates (UAE). To provide a continuous power supply, different types of battery technologies have been used as a renewable energy storage system in this study as Nickel Iron (NiFe), Lithium Iron Phosphate (LiFePO4), and Lead Acid (PbSO4) with three different depths of discharges. Six different configurations of the PV-BES were modeled. In total, nine metaheuristic optimization algorithms were used in the MATLAB environment to provide an optimal sizing of the PV-BES. The mayfly optimization algorithm has provided the best optimal Annual Levelized Cost (ALC) values compared with the remaining algorithms. The mayfly algorithm has more robustness and faster convergence in providing the optimal global best values. Furthermore, three different approaches of MCDA and weights methods were used. The inputs and the results for the optimization process in addition to the sustainable development goal (SDGs) from the united nation (UN) were used as criteria for MCDA. The PV-Li-ION at 50 % depth of discharge (DOD) was the best option among all cases based on the six configurations and nine optimization algorithms.

Developing supply chain resilience: a robust multi-criteria decision analysis method for transportation service provider selection under uncertainty

ABSTRACT This study examines critical success factors (CSFs) for supply chain resilience regarding the selection of transportation service providers. Due to extensive global sourcing, many companies are dependent on multiple suppliers and service providers. Using a Delphi approach, this article identifies the CSFs relevant to the resilience of transportation service providers in supply chains. A novel approach using Multiple Criteria Decision-Analysis (MCDA) is then developed to rank a group of CSFs. In particular, the Best-Worst Method (BWM) and Multi-attributive Border Approximation Area Comparison (MABAC) methods are used to rank resilience-related CSFs for transportation service providers in uncertain environments using Hesitant Fuzzy Sets (HFS). Based on our results, hybrid MCDA methods can be applied in order to develop an effective method for determining resilience-related CSFs when selecting transportation service providers in uncertain environments.

Deep preference learning for multiple criteria decision analysis

We propose preference learning algorithms for inferring the parameters of a threshold-based sorting model from large sets of assignment examples. The introduced framework is adjusted to different scores originally used in Multiple Criteria Decision Analysis (MCDA). They include Ordered Weighted Average, an additive value function, the Choquet integral, a distance from the ideal and anti-ideal alternatives, and Net Flow Scores built on the results of outranking-based pairwise comparisons. As a concrete application of these models, we use Artificial Neural Networks with up to five hidden layers. Their components and architecture are designed to ensure high interpretability, which supports the models’ acceptance by domain experts. To learn the most favorable values of all parameters at once, we use a variant of a gradient descent optimization algorithm called AdamW. In this way, we make the MCDA methods suitable for handling vast, inconsistent information. The extensive experiments on various benchmark problems indicate that the introduced algorithms are competitive in predictive accuracy quantified in terms of Area Under Curve and the 0/1 loss. In this regard, some approaches outperform the state-of-the-art algorithms, including generalizations of logistic regression, mathematical programming, rule ensemble and tree induction algorithms, or dedicated heuristics.