Multi-criteria Decision-making Model Research Articles

Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

A Review of Recent Developments on Modeling Low Impact Development (LID) Technique

Urban areas are more susceptible to flooding and water body contamination due to the detrimental effects of urbanization. As a result, a sustainable urban drainage system, also known as low impact development (LID) technique, is required. Although this technique can be extensively applied, the planning and design processes are multi-dimensional, multi-variable, and site-specific, which must consider various local conditions and factors. Consequently, these processes can be very complicated and time-consuming for professionals, necessitating support from computer modeling. This study intends to thoroughly explore the idea of LID modeling, various available computer models, and other tools for its optimization and decision-making processes. The most recent trustworthy journal publications that addressed the subjects under discussion were reviewed. This paper used the descriptive and comparative approaches as the analytical methods. According to the findings of the review, Storm Water Management Model (SWMM) is the computer model in LID modeling that is most frequently employed. This model is a fundamental package for dynamic urban rainfall-runoff modeling, and it has the benefits of being lightweight, simple to use, and an intuitive user interface. Besides, this model is public domain (free to use), open source, and interoperable with many hydro modeling applications. A specific LID editor module is also included in this model for modeling different LID units. To acquire the best LID planning and design from multiple criteria and alternatives, it is also necessary to use metaheuristic algorithms as an optimization model and a multi-criteria decision-making (MCDM) model in addition to the rainfall-runoff model. The authors believe combining the hydrologic and hydraulics models integrated with geographical information systems (GIS), metaheuristic algorithms, and MCDM is the most comprehensive and appropriate method for LID modeling in urban watersheds.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

Purpose This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement. Design/methodology/approach Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions. Findings The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories. Research limitations/implications This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving. Originality/value The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Enabling organizations to strategically manage risks in circular supply chains

AbstractCircular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long‐term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision‐making (MCDM) methods. The model applies fuzzy Delphi‐based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance‐based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational costs.

Optimal Selection of Distribution, Power, and Type of Luminaires for Street Lighting Designs Using Multi-Criteria Decision Model

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangements. By employing a linear additive model to solve a multi-criteria decision model, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare and installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in MATLAB R2023b, with results validated through simulations in DIALux evo 12.0. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Enhancing Hajj Pilgrim Satisfaction: A Strategic Analysis of Service Quality Dimensions using the Analytic Hierarchy Process in Alignment with Saudi Vision 2030

Saudi Arabia every year receives more than two million Muslims from all over the world to perform the Hajj (the Muslim pilgrimage to Makkah). "It must take place during a specific period of the year in a specific and limited space." (“Asma BAHURMOZ | Faculty Member | Prof of Operations Research | King ...”) To manage and satisfy such crowd, Considering the variety of cultures, languages, and different levels of socio-economic background, is next to impossible. Saudi vision 2030 has taken it as its duty to ensure that pilgrimages will have joyful experience and good memories to take back home. Authorities develop four categories of packages based on quality dimensions to meet different budgets. To manage and provide quality service for the pilgrimagesis incredibly challenging task. In an effort from the authors to dir ect their research towards this goal. A multi criteria decision model MCDM is developed to evaluate the service quality dimensions (SQD) and identify essential criteria that achieve pilgrim’s satisfaction and keep up with hajj vision 2030, The aim is to provide an easy tool though a scientific one for companies providing these packages to adjust their services to match pilgrims ' expectations.

A multi-criteria decision-making model to determine the optimal share of variable renewable energy sources in a wholesale electricity market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly . Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24 MW, respectively.

Sustainable Afforestation Strategies: Hybrid Multi-Criteria Decision-Making Model in Post-Mining Rehabilitation

This article describes an effective approach for selecting suitable plant species for afforestation in post-mining rehabilitation. The research was conducted in the Western Black Sea region of Turkey. The aim of the research is to perform accurate criteria weighting and species prioritization for afforestation in post-mining degraded areas. This helps to ensure consistent conditions for the future use of the site as a forest, sustainability of nature, and selection of appropriate species adapted to the difficult post-mining conditions. In this study, which is a multi-criteria decision-making problem (MCDM), the weights of the criteria were determined by stepwise weight assessment ratio analysis (SWARA), and the priority ranking of the species was determined by the analytic hierarchy process (AHP). Analyses were carried out with 10 afforestation criteria and five tree species. According to the analysis, the top three ranked criteria are Economic Efficiency > Carbon Stock and Credit > Reducing Afforestation Cost. The five species’ priority ranking is Robinia pseudoacacia L. (0.456) > Alnus glutinosa subsp. glutinosa (0.248) > Populus nigra subsp. nigra (0.146) > Salix alba L. (0.103) > Quercus robur subs. robur (0.048). The hybrid approach is expected to increase the effectiveness of post-mining rehabilitation works.

Recycling of Egyptian Shammi Corn Stalks for Maintaining Sustainable Cement Industry: Scoring on Sustainable Development Goals

This study focuses on recycling Shammi corn stalks in the cement industries, further avoiding air and soil pollution caused by their improper disposal. This crop residue was thermally treated at 700 °C for 2 h under an oxygen-rich environment to produce Shammi corn stalk ash (SCSA). This SCSA was used as a cement replacement material (2–10%, w/w), whereas the control sample included only cement. The compressive strength values for the 4% (w/w) replacement ratio at 2-, 7-, and 28-day ages were greater than those for the control by 26.5%, 15.8%, and 11.4%, respectively. This 4% (w/w) also maintained a better flexural strength than other mixtures, with proper initial and final setting times (135 and 190 min), workability (18.5 cm), and water consistency (27.5%). These mechanical/physical properties were integrated with socio-enviro-economic data collected from experts through a pairwise comparison questionnaire, forming the inputs of a multi-criteria decision-making (MCDM) model. Recycling SCSA in the cement-manufacturing process attained positive scores in the achievement of the three pillars of sustainable development, revealing an overall score greater than the control. Hence, the study outcomes could be essential in developing green concrete, cement blocks, and mortar, based on the sustainable development goals (SDGs) agenda.

Evaluating seismic risk by MCDM and machine learning for the eastern coast of India.

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic inducedhazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India.

An [formula omitted]-cut intervals based Fuzzy Best–Worst Method for Multi-Criteria Decision-Making

To address ambiguity arising from linguistic decision judgments, Guo and Zhao introduced a model of the well-known Multi-Criteria Decision-Making (MCDM) method, Best-Worst Method (BWM), using fuzzy sets, known as Fuzzy Best–Worst Method (FBWM). Despite its versatility, FBWM has limitations, such as criteria-weights being independent of fuzzy comparison values’ shape and reliance on approximate fuzzy arithmetic operations. In response to these limitations, we introduce a novel model of Fuzzy Best–Worst Method based on α-cut intervals. In this model, the calculation of optimal weights involves formulating an optimization problem that utilizes exact fuzzy arithmetic operations defined in terms of α-cut intervals. This approach aims to optimize the entire shape of fuzzy comparison values simultaneously. However, it turns out that, although we have proven the existence of optimal solution for this problem, solving it proves to be challenging. To resolve this issue, we approximate optimal weight set(s) using triangular fuzzy weight sets and assess the Error of Approximation (EA). The approximate weight set with the desired EA is then defuzzified to obtain a crisp weight set. In situations where there are multiple approximate weight sets with the same EA, interval-weights are calculated. Following that, we establish the necessary conditions for a Fuzzy Pairwise Comparison System (FPCS) to be consistent. Using these conditions, we calculate a lower bound of the Consistency Index (CI), providing a measure of the accuracy of weights. Finally, we discuss numerical examples and a real-world scenario, ranking of risk factors in supply chain 4.0, to validate the effectiveness of the proposed model.

Risk Analysis of the Use of Drones in City Logistics

Drone delivery in city logistics is gaining attention due to road congestion, environmental threats, etc. However, there are risks associated with using drones which can result in hazardous events, such as conflicts in the air, loss of control, and system failures. It is crucial to assess the risks involved in using different types of drones and choose the option with the lowest risk. The existence of different criteria important for this decision imposes the need to apply the multi-criteria decision-making (MCDM) method(s). This paper proposes a new hybrid model that combines the fuzzy Factor Relationship (FARE) method for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the alternatives. A single-rotor microdrone weighing up to 4.4 lb was chosen as the optimal solution, and after that, the most favorable are also the drones of this size (multi-rotor and fixed-wing microdrones). The establishment of a novel hybrid MCDM model, the identified risks, the set of criteria for evaluating the least risky drones, and the framework for prioritizing the drones are the main novelties and contributions of the paper.

Proportional intuitionistic fuzzy CODAS method: Cloud service provider selection application

Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented.

Distortion risk measures for tail-risk multicriteria decision-making based on a probabilistic linguistic environment

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed.

Application of Multi-Criteria Decision-Making (MCDM) to Select the Most Sustainable Power-Generating Technology

In response to the growing importance of sustainability and regulatory pressures, companies are increasingly engaging in sustainable projects to mitigate environmental and social harm. Therefore, it is crucial to incorporate sustainability considerations during selecting construction projects in the feasibility phase. This study aims to identify a comprehensive set of sustainability criteria and sub-criteria to help the owners of power-generating plants to select the most sustainable technology for their new projects. Sixteen criteria are identified and categorized under the pillars of sustainability: economic, social, and environmental, plus the technical category. To illustrate practical application, a case study demonstrates the use of these essential sustainability criteria through a hybrid multi-criteria decision-making (MCDM) model for power-generating technology ranking. The results suggest that when stakeholders’ perspectives are weighted approximately equally, considering all sustainability pillars, natural gas with carbon capture is favored for sustainability. A three-scenario sensitivity analysis was performed involving expert opinions from one of the largest power-generating companies in Canada. This integrated generic model can be utilized by industry experts to apply multi-dimensional rational decision-making techniques to solve the complex problem of selecting the most sustainable alternative in construction projects.

A Proactive Decision-Making Model for Evaluating the Reliability of Infrastructure Assets of a Railway System

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode ‘Broken and defective rails’ has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse.

Multicriteria-decision support for master plan scheduling: urban regeneration of an industrial area in Northern Italy

Urban regeneration programs involve a series of intricate activities that utilize human, material, and financial resources, executed at varying times and in diverse manners. Within this complex scenario, a major challenge lies in effectively managing concurrent developments across multiple buildings and infrastructure projects, while also harmonizing the interests of all parties engaged in urban regeneration initiatives. This paper introduces a multi-step strategy designed to enhance and streamline urban management amid the multifaceted decision-making processes characteristic of urban regeneration efforts. Specifically, the multi-criteria decision-making model ELECTRE TRI-B is employed to sort the array of basic tasks within a complex transformation endeavor into priority groups based on various evaluative criteria. To ascertain the significance of these criteria and rank each task according to the interests of the stakeholders likely to be impacted, a panel of specialists, including construction experts, economists, and environmental assessors, is convened. Following this, project actions are organized in a Gantt chart reflecting the construction priority determined by the evaluation model. This approach facilitated the temporal organization of master plan activities, incorporating insights from a broad spectrum of experts including municipal technical staff, professionals, and the community at large. The findings indicate that this comprehensive and interactive approach to performance measurement enabled the handling of extensive datasets and the refinement of financial, economic, environmental, and social resources, all while adhering to logical constraints and urban policy demands.

A novel multi-criteria approach for evaluating social discrimination in OECD countries

The Organisation for Economic Co-operation and Development (OECD) was established to unite countries from different geographical regions to achieve prosperity and economic progress. Different geographical regions have different cultures. The sociological perspective on gender varies in these cultures. As a result, gaps occur in terms of gender discrimination and its dimensions between countries. While social progress is a prerequisite for economic progress, society must be in harmony for social progress. The Social Institutions and Gender Index (SIGI) was developed to measure progress in different countries. This study proposes a Multi-Criteria Decision-Making (MCDM) model over the SIGI index to measure gender discrimination levels in OECD countries. However, since the differences in gender discrimination resulting from the multicultural structure of OECD lead to significant variability in terms of countries, countries are first clustered. Then, SIGI criteria significance levels are evaluated with a novel Importance of Criteria’s Indifference to Normalization (ICIN) method based on the differences between the two normalization approaches. Subsequently, a novel Modified Extended Alternative Ranking Order Method Accounting for Two-Step Normalization (ME-AROMAN) approach is employed to rank the member countries. While discrimination in the family is the most prominent factor for countries better at social equality and access to productive and financial assets is the major factor that separates the countries worse at social equality, the sensitivity and comparative analysis demonstrate the applicability and validity of ICIN and ME-AROMAN in MCDM literature.

Identifying intrusion of seawater in coastal aquifers by modified GALDIT (M-GALDIT) index

Contamination of coastal aquifers by seawater intrusion (SWI) is a major environmental problem. A modified GALDIT method (M-GALDIT), and multi-criteria decision model AHP has been used to identify regions vulnerable to seawater intrusion. We have used the groundwater suitability index for seawater intrusion (GQISWI) in estimating the M-GALDIT index. While the GALDIT analysis indicates that ∼28% of the area is moderately vulnerable, and 8% of the area is relatively highly vulnerable, our estimates using AHP and M-GALDIT method indicate ∼19% of the area as moderately vulnerable, ∼22% of the area as relatively highly vulnerable zones. The GQISWI values in the region vary between 0 and 96.96, indicating that most of the groundwater samples belong to mixed water types. Faults and fractures, population distribution, water-rock interaction, ionic exchange reactions, and evaporation appear to be the dominant mechanisms, besides seawater intrusion that control the overall groundwater geochemistry in the region. Ca2+-HCO3-, Ca2+-Na+-HCO3−, and Ca2+-Mg2+-Cl− are the most dominant water types in the study area. The seawater mixing index (SMI) reveals that ∼34.5% of samples are of relatively freshwater types, whereas ∼61% of samples appear contaminated to some extent, presumably due to the mixing of seawater, or anthropogenic activities such as mining, agricultural activities, etc. Furthermore, analysis of the HFE-D diagram indicates that ∼23% of samples are directly affected by seawater intrusion.

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India