Multi-criteria Decision-making Approach Research Articles

Landslide vulnerability mapping using multi-criteria decision-making approaches: in Gacho Babba District, Gamo Highlands Southern Ethiopia

The landslide has been a life-threatening natural disaster in most districts of Gamo Highlands. This study was conducted to assess the status of landslide vulnerability in the Gacho Baba district of the Gamo zone in southern Ethiopia. The Geographic Information System Analytical Hierarchy Process and Weighted Linear Combination multi-criteria decision-making approaches were applied. Eight causative factors of landslide, namely, slope, elevation, aspect, distance from the stream, drainage density, soil type, distance from the road, and land use/cover were considered. The weight values of each causative factor were determined by previous studies, field observations, and experts’ judgment. The calculated weight of each causative factor is the slope (23%), elevation (21%), aspect (8%), stream distance and drainage density each (12%), soil type and road length each (9%), and land use/cover (6%). Moreover, the Consistency Index (0.13) and Consistency Ratio (0.08%) were calculated with acceptable values for comparison to weighted overlay analysis to produce a landslide vulnerability map of the study area. The result shows that the vast majority of the study district (86.6%) falls within a very high to moderate landslide susceptibility class and only (13.4%) falls within a very low and low susceptibility. This indicates that almost all the 11 villages in the district, are found within the high to moderate landslide vulnerability class which alerts responsible community and zonal disaster risk prevention and related offices to take action on the identified causative factors to reduce the occurrences of landslide hazard in the study district.

The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries

Supply chain performance signifies the success of countries in material and information flow within international trade activities. Countries strive to enhance their supply chain performance by increasing connectivity with partner countries and reducing dwell times at logistics hubs. In this study, the supply chain performance of countries based on international tracking indicators is examined using a multi-criteria decision-making approach. The paper encompasses two primary objectives. The first objective is the determination of the supply chain performance of countries. The second one is the development of a novel alternative ranking method. The study employs 10 supply chain key performance indicators as criteria and utilizes data from 72 countries. The data is sourced from logistics performance index reports published by the World Bank. The modified preference selection index (MPSI) is employed for criterion weighting. For the ranking of countries, a novel alternative ranking method named the “Alternative Ranking using two-step LOgarithmic Normalization (ARLON)” method is proposed. Through the implementation of the MPSI-ARLON hybrid method, supply chain performance and rankings of countries are computed. The findings are supported by sensitivity and comparative analyses. Furthermore, robustness tests are conducted for the ARLON method. The research concludes by presenting implications and managerial insights derived from the ARLON method.

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems.

Selection the optimal technology for electricity generation of 5 MW using a fuzzy multi-criteria decision-making approach

Today's challenges towards a sustainable energy development in most European nations are crucial and highly complicated. We find the need to choose between several technologies generation, whose task is very laborious. Multiple factors that affect the success of a Renewable Energy (RE) project must be analyzed and taken into account. The evaluation and comparison of possible technologies generation is a multi criteria decision problem because not only economic aspects must be considered but also technical, environmental and social aspects. This paper presents the case for selecting the optimal technology for electricity generation of 5 MW for a region located in the southeast of Spain. As most of the real-world multicriteria decision-making (MCDM) problems contain a mixture of quantitative and qualitative criteria; therefore discrete MCDM methods are inadequate for handling this type of decision problems.

Optimal location selection of a casual-dining restaurant using a multi-criteria decision-making (MCDM) approach

Optimal location selection of a casual-dining restaurant using a multi-criteria decision-making (MCDM) approach

Application of Multi-Criteria Decision Making Methods for Menu Selection

Nutritional information on menus can assist customers in making healthier eating choices. One technique being utilized to tackle the rise of overweight and obesity is the use of nutritional information on menus. Menu engineering strategies can be used to improve sales of generally healthier and higher margin items. For today's food and beverage companies, menu engineering has become essential. Companies must continually evaluate their menus in order to keep up with changing customer demands and the conditions of the competitive market. Menu engineering's core involves comparing the effectiveness of each menu. At this point, correct decision-making under numerous factors is thought to be a very challenging procedure. To evaluate alternatives according to many features, several Multi-Criteria Decision-Making (MCDM) approaches have been created. The main novelty of this paper is that four MCDM methods, including Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy TOPSIS, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Fuzzy VIKOR, are employed to evaluate menu options. Comparative analysis of MCDM methods is another contribution of this study. The process of evaluating and selecting healthier menu alternatives can become challenging and time-consuming. This study pointed out how crucial it is to conduct comparative analysis using various MCDA methods and to carefully determine the right ones when addressing the issue of selecting the best menu, taking into account the values of the criterion in fuzzy numbers.

Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed.

Performance Evaluation of Retail Warehouses: A Combined MCDM Approach Using G-BWM and RATMI

Background: The retail sector has experienced significant growth in recent years, necessitating efficient supply chain management and sustainable logistics practices. Evaluating the performance of retail warehouses is crucial for meeting customer expectations and enhancing operational efficiency. Methods: This study employed a combined multi-criteria decision-making (MCDM) approach, using the group best–worst method (G-BWM) for weighting criteria and ranking the alternatives based on the trace-to-median index (RATMI) for warehouse ranking. The performance criteria were cost, quality, time, productivity, and safety. Data were collected from four mega retail warehouses in the western region of Saudi Arabia for evaluation and analysis. Results: The evaluation of retail warehouse performance using the MCDM approach provided valuable insights for decision-makers and warehouse experts. The criteria weights were determined using the G-BWM, and the RATMI enabled the ranking of the warehouses based on their weighted performance scores. The results highlight the strengths and weaknesses of each warehouse, facilitating strategic planning, resource allocation, and operational improvements. Conclusions: This study presents a novel combined MCDM performance evaluation approach for retail warehouses. The study has implications for effective decision-making processes, resource allocation, and operational efficiency. Furthermore, it serves as a foundation for future research, exploring additional dimensions of warehouse performance and enabling sustainable logistics within the broader supply chain context.

A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters

This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic and environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the developed data-driven provides policy decision-making with an evaluation of REC in the residential sector.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

An improved task scheduling algorithm for conflict resolution in cloud environment

The performance of the cloud-based systems is directly associated with the resource utilization. The maximum resource utilization indicates the high performance of cloud computing. Further, effective task scheduling is the primary factor that significantly impacts resource utilization. Various researchers have proposed multiple algorithms to achieve optimum utilization, but a high rejection rate of tasks occurred due to conflict among similar tasks during backfilling. A hybrid approach is proposed for selecting the backfilled tasks using a multicriteria decision-making approach (MCDM), namely Distance-based approach with a traditional task scheduling algorithm. The parameters considered to find the suitable task for backfilling are arrival time, start time, deadline time, duration, size, and the number of virtual machines. The proposed algorithm is implemented on different datasets and compared with other state-of-the-art algorithms. The result shows a significant improvement in virtual machine utilization and task acceptance/rejection rate.

Overcoming Barriers to Sustainable Supply Chain Management in Small and Medium-Sized Enterprises: A Multi-Criteria Decision-Making Approach

Sustainable supply chain management (SSCM) integrates economic, social, and environmental goals within the supply chain to enhance long-term performance. It assists organizations in monitoring their performance concerning social, environmental, and economic factors to bolster sustainability. Yet, implementing SSCM poses significant challenges for most organizations, particularly small and medium-sized enterprises (SMEs). This study aims to identify key barriers and strategies for overcoming them in SMEs. A thorough literature review revealed 80 barriers across nine categories: technological, economic and financial, supplier, information, market and networking, human resources, social and cultural, regulatory and institutional, and organizational barriers. Eight experts from SMEs in focus group discussions considered 55 relevant barriers and prioritized them using the best–worst method. Then, based on the top 15 barriers they deemed key, they adapted and improved 24 strategies based on the literature that, in their opinion, could support the implementation of and improve the SSCM in SMEs. The findings from this study highlight economic and financial barriers as the foremost challenges to the implementation of SSCM, mainly due to the lack of funding and capital to make changes in activities to include sustainability. The findings are valuable for SMEs seeking to implement SSCM, offering insights into potential barriers and strategies for surmounting them. Moreover, this study provides a structured approach that can be replicated to identify the most pressing barriers to overcoming and improving long-term sustainability.

A resilient inter-organizational workflow assignment plan selection approach: Application to a digital health use case

Workflow Management Systems are rapidly evolving and increasingly used in a variety of industries, allowing organizations to automate and enhance their business processes. Hence, they are open to collaboration throw inter-organizational workflows to achieve common objectives. Thus, organizations could thereby adapt to highly dynamic environments and abrupt sudden changes like during epidemics. One of the particular interests for security researchers is to ensure workflow resiliency, which means ensuring the completion of the workflow execution given a defined set of security constraints without violating the security policy. However, the rigid application of security constraints restricts some task-organization assignments, which leads to the workflow satisfiability problem. Moreover, a wise selection of collaborating organizations is very important for secure resilient inter-organizational workflow execution. Therefore, we aim in this paper to propose a resilient inter-organizational workflow assignment plan selection approach to select a valid task-organization plan that allows a resilient inter-organizational workflow complete execution. Our proposed approach, uses the task priority concept, Bayesian inference, and a hybrid multi-criteria decision-making approach based on the methods Analytical Hierarchical Process and Grey Technique for Order of Preference by Similarity to Ideal Solution. As a result, the proposed approach selects the best assignment plan for a resilient execution of the Inter-Organizational Workflow, in addition to delegates for each task-organization assignment if available.

Multi-Criteria Decision Making Model For Hotel Selection Problem Under Complex Dual Hesitant Fuzzy Information

ABSTRACT The notion of the “complex dual hesitant fuzzy set (CDHFS)” is the combination of the “dual hesitant fuzzy set (DHFS)” and the “complex fuzzy set (CFS).” It is characterized by two degrees, namely the membership and nonmembership, in the form of a finite subset on a unit disc in the complex plane. CDHFS is useful for dealing with real-world problems involving uncertain or hard-to-predict information. Also, to approximate smoothly, the Einstein operators are well-known aggregation operators, while prioritized operators are effective tools for prioritization among criteria. Therefore, the goal of this study is to develop some prioritized aggregation operators under the CDHFS environment; namely the complex dual hesitant fuzzy prioritized averaging (CDHFPA) operator, the complex dual hesitant fuzzy prioritized geometric (CDHFPG) operator, the complex dual hesitant fuzzy Einstein prioritized averaging (CDHFEPA) operator, and complex dual hesitant fuzzy Einstein prioritized geometric (CDHFEPG) operator. Some properties of the proposed operators are investigated in detail. In addition, a multi-criteria decision-making (MCDM) method based on the proposed operators with the complex dual hesitant fuzzy setting is developed. Moreover, a numerical example is given for the application and effectiveness of the developed MCDM approach. A comparison study is also done with existing methods to show that the proposed MCDM method is better and more reliable. The study finds that if the expert’s preference is used to choose the right aggregation operators, the decision maker will have access to a wide range of compromise solutions.

Multi-criteria decision making to validate performance of RBC-based formulae to screen beta-thalassemia trait in heterogeneous haemoglobinopathies

BackgroundIndia has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies.MethodsWe compared the performance of a recently developed formula SCS_{BTT} and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden’s Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures.ResultsMCDM methods revealed that the Shine & Lal and SCS_{BTT} were the best-performing formulae. Further, a modification of the SCS_{BTT} formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS_{BTT} along with the condition MCVle 80 fl was recommended for a higher heterogeneous population set. It was found that SCS_{BTT} can classify all BTT samples with 100% sensitivity when MCVle 80 fl.ConclusionsWe addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS_{BTT} and its web application SUSOKA can provide 100% sensitivity when MCVle 80 fl.

An Extension of Root Assessment Method (RAM) Under Spherical Fuzzy Framework for Optimal Selection of Electricity Production Technologies Toward Sustainability: A Case Study

This paper integrated the root assessment method (RAM) with a spherical fuzzy set (SFS) to rank the alternatives and select the best electricity production technology. The SF‐Entropy is used to compute the factor’s weights, and the SF‐RAM method ranks the alternatives. This study used four main factors, 29 subfactors, and 24 alternatives. The four main factors are economic, environmental, social, and political. The results show that economic factors are most important, followed by social, environmental, and political factors. The results of the SF‐RAM method show that hydropower and wind onshore are the highest‐rank technologies in Egypt. The sensitivity and comparative analysis are conducted in this study. The sensitivity analysis was conducted with 30 scenarios in changing factors’ weights and three scenarios in changing of expert’s weight. The sensitivity analysis results show the alternatives’ rank is stable under different scenarios. The comparative analysis was conducted using various multicriteria decision‐making (MCDM) methods. The results of the comparative analysis demonstrate that the proposed methodology outperforms other MCDM approaches. This study supports governments in mitigating environmental impacts, ultimately leading to lower morbidity rates.

Identification of flood hazard zones in Afghanistan using GIS and multi-criteria decision approach

Identification of flood hazard zones in Afghanistan using GIS and multi-criteria decision approach

A two-phase model for resilient hub and mobile distribution centers location

Hub location is crucial for resilient and uninterrupted supply chain operations, particularly during disruptions or unforeseen events. In this paper, we propose a resilience hub location framework for Third Party Logistics (3PL) companies with two key objectives: optimizing demand flows and establishing a resilient network capable of with-standing sudden disruptions. The study aims to identify the key criteria that contribute to the successful implementation of the resilient center. The proposed structure utilizes a two-phase decision-making methodology. The first phase presents a new Multi-Criteria Decision-Making (MCDM) approach called SWARA-EDAS method that evaluates and ranks potential locations based on resiliency criteria. The second phase proposes an optimization model to determine the optimal hub location. To illustrate the approach, a real-world case study of a 3PL company in Tehran is included. Due to the absence of precise demand data in the case study, a novel clustering approach is proposed to estimate the demand flow. Each individual cluster can be considered as a distinct demand point, and a clustering analysis involving 122 regions within Tehran is conducted, taking into account various factors such as population, economic index, accessibility to the Internet, and number of business units. To enhance the resiliency of the network, mobile distribution centers are also deployed. These mobile centers not only provide flexibility but also serve as backup capabilities in the event of a disruption or failure at the fixed hub. The proposed structure offers practical in-sights for 3PL companies seeking to implement a resilient network structure.

A Fusion Selection Approach of the Best Plan for Energy Remodeling Hospital Wards Using a Multi-Criteria Decision-Making

Energy policy implementation relies heavily on assessing savings from retrofitting for energy efficiency. Because of their unique purpose, hospitals need energy-efficient renovations to improve indoor air quality and create a pleasant space for staff and visitors. Because of this crucial distinction, investors' preferences must be considered when deciding on refurbishment plans. Considering elements including energy savings, financial viability, and thermal comfort, this research provides a multi-criteria decision-making (MCDM) approach to guide investors in choosing the most effective remodeling plan for hospital wards. We used the MABAC method as an MCDM fusion method to combine the various criteria and alternatives to select the best one. We used ten criteria and ten alternatives in this study. We compute the weights of criteria to rank the criteria. Then, we used the MABAC fusion to rank the alternatives. The results show the financial viability has the least weight and the building envelope has the highest. We conducted a sensitivity analysis to show the stability of the results in this study.

Efficient last-mile logistics with service options: A multi-criteria decision-making and optimization methodology

The rapid growth of online shopping has intensified the need for cost-effective and efficient delivery systems, posing a significant challenge for businesses worldwide. This study proposes an innovative two-phase methodology that uses a hybrid multi-criteria decision-making (MCDM) approach for efficient last-mile logistics with service options (ELMLSO) such as home delivery, self-pickup, and differently-priced services. This approach aims to streamline last-mile logistics by integrating these service options, resulting in a more comprehensive and effective delivery network that enhances customer satisfaction and maintains a competitive edge. The first phase employs the Ordinal Preference Analysis - Evaluation based on Distance from Average Solution (OPA-EDAS) method to select optimal pickup and delivery centers. The second phase identifies the optimal route using a bi-objective mixed-integer mathematical model, striving to balance cost minimization and customer satisfaction maximization. The Normalized Normal Constraint Method (NNCM) is utilized to solve this model. The application of these methods results in considerable cost savings and improved customer satisfaction, offering valuable insights for managers within the last-mile logistics industry.