Hybrid Multi-criteria Decision-making Approach Research Articles

Assessing the obstacles to green supply chain management in Bangladesh's footwear industry using SWARA and ISM-MICMAC methods

This study aims to identify and analyze the barriers to Green Supply Chain Management (GSCM) in the footwear industry of Bangladesh, which is essential for improving sustainability and gaining a potential benefit in the global market. Despite the growth of Bangladesh’s footwear industry, environmental challenges and a lack of green practices have hindered its ability to meet international standards. The study employs a hybrid multi-criteria decision-making approach, combining Stepwise Weight Assessment Ratio Analysis (SWARA) and Interpretive Structural Modelling (ISM) with MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis. Using literature reviews and expert opinions from industry professionals, 19 barriers were initially identified, which were narrowed down to 12 for further analysis. SWARA was used to rank these barriers, while ISM-MICMAC was applied to assess the interrelationships between them. The findings reveal that the most significant barriers to GSCM implementation are insufficient training and poor supplier commitment, followed by a lack of knowledge and experience. The ISM-MICMAC analysis highlights the interdependencies among these barriers, suggesting that addressing one may impact others. This research is novel in its application of a hybrid SWARA-ISM-MICMAC approach to the footwear industry, a sector in Bangladesh that has not been extensively studied in the context of GSCM. The study provides practical implications for managers and policymakers by offering insights into the most critical barriers and their interrelations, guiding targeted interventions. By addressing these barriers, the footwear industry can enhance its sustainability practices, improve market competitiveness, and meet growing environmental demands.

Financial fortitude: Indian pharmaceutical sector’s performance before and during COVID-19 using fuzzy AHP & TOPSIS

The COVID-19 pandemic has significantly impacted the financial performance of various sectors across the globe, including the Indian pharmaceutical industry. This study aims to evaluate the financial performance of ten Indian pharmaceutical companies listed in the S&P BSE Healthcare Index over two distinct periods: before COVID-19 (2018–2020) and during the pandemic (2020–2022). A hybrid multicriteria decision-making (MCDM) approach, integrating the Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to assess companies based on five key financial dimensions and several performance indicators. Results indicate that profitability, valuation, and growth ratios were the most critical dimensions, with weights of 0.21 each, followed by liquidity (0.19) and efficiency (0.18). Furthermore, among the companies evaluated, Divis Labs and Abbott India emerged as top performers, both during and before the pandemic, with Divis Labs registering closeness coefficients of 0.871 and 0.814 during 2020–2021 and 2021–2022. The findings highlight the financial resilience of these companies, offering valuable insights for stakeholders in formulating strategies to sustain financial stability during future crises.

BWM-MARCOS: A new hybrid MCDM approach for mineral potential modelling

Here we describe the application of a novel hybrid multi-criteria decision-making (MCDM) approach termed “Best-Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution” (BWM-MARCOS) to mineral potential modelling (MPM). The newly proposed BWM-MARCOS technique combines two mathematical frameworks in which the BWM approach is utilised to weight decision criteria (i.e., predictor maps) whilst the MARCOS approach is applied to rank alternatives. A BWM-MARCOS model of orogenic gold mineral systems in the well-endowed Granites-Tanami Orogen (GTO) in Australia's Northern Territory was generated utilising the same study area and set of competent spatial proxies as previously developed and described by Roshanravan et al. (2020), facilitating the benchmarking of the new results against those obtained from previous models. We found the BWM-MARCOS approach to MPM performed better than any of the knowledge-driven (i.e., fuzzy inference system), continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) and data-driven (i.e., feed-forward deep neural network and ‘original’ random forest) mineral prospectivity models previously developed for the GTO by Roshanravan et al. (2020, 2023b). To further constrain the BWM-MARCOS outputs, the modeled gold potential zones were delimited utilising a concentration-area fractal approach. The areas covered by the prioritised 1st order (top priority) and 2nd order (high priority) targets represent significant reductions of the search space (i.e., >303 times or two orders of magnitude, and >93 times or one order of magnitude, respectively). Any order of magnitude, or greater, reduction of the search space, as achieved in this study, can be considered a hallmark of a well-performing, practically useful targeting approach.

A hybrid multi-criteria decision-making approach for hospital sustainability performance assessment

PurposeNowadays, the sustainability of healthcare services is of increasing importance. In particular, hospitals have ceased to be only treatment-oriented institutions and have begun to operate on the principles of sustainability in their environmental, economic and social dimensions. In this context, a comprehensive method is required to evaluate and improve the performance of hospitals.Design/methodology/approachIn this study, it is recommended to combine D-DEMATEL (D number theory and decision-making trial and evaluation laboratory methods) and objectives matrix (OMAX) methods, which are two important methods in determining hospital performance. D-DEMATEL is a technique used to analyze complex relationships and interactions that reduces subjective judgments because it is based on the opinions of many decision-makers and can be applied even in cases of incomplete information. OMAX, on the other hand, provides a comprehensive framework for measuring performance and allows different performance indicators to be evaluated together.FindingsThe novel performance assessment model is applied to a hospital in real life. Its performance value, according to 36 determined performance indicators, is calculated at 56.91%. The indicators of the hospital that need improvement are defined by the traffic light system method. The performance indicator importance ranking of D-DEMATEL is compared to the ranking obtained by the fuzzy DEMATEL method.Originality/value Important indicators to be used in later sustainable hospital performance evaluation studies were determined. Also, an integrated D-DEMATEL and OMAX method for evaluating sustainable hospital performance is presented.

F-BWM-MARCOS approach for performance evaluation of Türkiye's top 500 industrial enterprises in the pre-pandemic and pandemic era

PurposeThe purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.Design/methodology/approachTo achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.FindingsThe COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.Research limitations/implicationsThe study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.Practical implicationsDecision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.Social implicationsThe study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.Originality/valueThe originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

Leveraging Enablers and Performance Metrics for Building Industrial Circular Supply Chain: a Hybrid Multi-Criteria Decision-Making Approach

Leveraging Enablers and Performance Metrics for Building Industrial Circular Supply Chain: a Hybrid Multi-Criteria Decision-Making Approach

Selection of an alternate cementitious mortar using ceramic tile dust waste: a hybrid MCDM approach.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.

A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection

Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability, posing a significant barrier to adopting intelligent approaches in supply chains. To address this issue, we propose a hybrid supplier selection framework that combines interpretable data-driven AI techniques with multi-criteria decision-making (MCDM) approaches: the former aims to reduce the complexity of the supplier selection problem, while the latter ensures familiarity to supply chain stakeholders by retaining MCDM at the heart of the supplier selection process. The framework is validated through two real-world case studies supporting supplier selection decisions in oil, gas, and aerospace manufacturing companies. Preliminary results from our case studies suggest that the framework can achieve comparable performance to approaches utilising only machine learning while offering the added benefits of end-to-end explainability and increased familiarity.

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

This study focuses on the application of process mining in the healthcare sector. Despite its potential to enhance efficiency, reduce costs, and improve patient satisfaction, the selection of process-mining software poses significant challenges due to the diverse nature of healthcare processes and the lack of comprehensive evaluation methods. To bridge this gap, this study employed a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating the Neural Network-Augmented Analytical Hierarchy Process (NNA-AHP) and Grey Relational Analysis—a technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS). The study evaluated process mining software on functionalities, ease of use, cost, technical support, scalability, and security with their respective sub-criteria. The principal results indicate that Disco is the top-performing alternative, followed by Celonis and ProM. Sensitivity analysis was conducted to understand the influence of variations in criteria weights on evaluating alternatives. In the NNA-AHP, Celonis consistently scored the highest. The GRA-TOPSIS method provided performance scores, indicating that higher scores yield better performance. The new hybrid method consolidates evaluations from all methods and offers the most comprehensive and dependable alternative assessment. Disco and its alternatives, Celonis and ProM, are recommended for optimizing healthcare processes. Further research is needed to investigate the integration of NNA-AHP and GRA-TOPSIS in healthcare management, especially in areas beyond business process analysis. This study provides valuable insights for professionals and researchers in the field and contributes to understanding the effectiveness of process mining.

Application of hybrid multi-criteria decision-making approach to analyze wastewater microalgae culture systems for bioenergy production

Bioenergy generation from microalgae can significantly contribute to climate mitigation and renewable energy production. In this regard, several multi-criteria decision-making method were employed to prioritize appropriate microalgae culture system for bioenergy production. Entropy weight, Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the employed MCDA method. Fourteen microalgae culture systems were selected as a case study, which contain teen monoculture and four dual-culture. Initially, through ans in-depth review of the literature and expert views, four categories total eight indicators were selected as the evaluation indices of the study, namely 1) Proliferation: Half growth cycle and Max growth rate,2) Biomass output: Bio-crude yield and Lipid yield, 3) Nutrient utilization: residual concentration of total Nitrogen and total Phosphorus, and, 4) Stability: coefficient of variation of Bio-crude yield and Lipid yield. The result indicated that "Pediastrum sp. &Micractinium sp." was identified as the most bioenergy potential microalgae culture system, and the evaluation results of entropy weight method and CRITIC method are similar. It is pertinent to note that 1)the entropy weight method exhibits lower sample size requirements, 2) the critic method excels when dealing with larger sample sizes, and 3) the TOPSIS method necessitates the incorporation of appropriate weighting methods to ensure credible results. In the application stage, the key indicators related to cost can be further included in the evaluation indices.

Integrated operational model to analyse policies and socio-demography leading to selection of electric vehicle - A case study on Bihar

In the push for sustainability in the power sector, Electric Vehicles show substantial promise. However, they still face hurdles in replacing traditional combustion engine cars, which have dominated the market for decades. Consumer awareness, especially regarding new technologies like Electric Vehicles, remains a significant barrier to adoption. This study aims to bridge the gap between technical aspects and consumer knowledge and a case study is done on the Indian state of Bihar. Using a hybrid Multi-Criteria Decision-Making approach with Triangular Fuzzy Numbers, a customized model is presented to help residents choose the right Electric Vehicle based on their needs and resources. Results are analyzed from customers’ perspective using Quality Function Deployment and conduct Sensitivity Analysis to explore cost trends. This survey examines seven Electric Vehicle models available in India, with MG ZS Electric Vehicle emerging as a potential standout. It's important to note that this research work does not endorse any specific Electric Vehicle model; rather, a mathematical framework is build to guide customers in selecting an Electric Vehicle that suits their requirements.

A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks

This study presents a hybrid multicriteria decision-making approach for evaluating stocks in the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust evaluation methodology to inform their investment decisions. With a market value of USD 2.89 trillion dollars in September 2022, the Saudi Stock Market is of significant importance for the country’s economy. However, navigating the complexities of stock market performance poses investment challenges. This study employs the best–worst method and the technique for order preference by similarity to identify an ideal solution to address these challenges. Utilizing data from the Saudi Stock Market (Tadawul), this study evaluates stock performance based on financial criteria, including return on equity, return on assets, net profit margin, and asset turnover. The findings reveal valuable insights, particularly in the banking sector, which exhibited the highest net profit margin ratios among sectors. The hybrid multicriteria decision-making-based approach enhances investment decisions. This research provides a foundation for future investigations, facilitating a deeper exploration and analysis of additional aspects of the Saudi Stock Market’s performance. The developed methodology and findings have implications for investors and stakeholders, aiding their investment decisions and maximizing returns.

Hybrid multicriteria decision‐making model for process selection for drinking water treatment

AbstractThe overuse of pesticides in agriculture poses significant threats to human health and the environment. This study addresses the issue by proposing a hybrid multi‐criteria decision‐making (MCDM) method, utilizing the analytic hierarchy process (AHP), to select the most suitable pesticide removal method for drinking water treatment. Focusing on the priority and selection of treatment options for specific pesticide groups, such as diuron and cypermethrin, based on toxicity and physicochemical properties, the study concludes that nanofiltration is best option for diuron removal, while granular activated carbon adsorption is effective for cypermethrin. The innovative hybrid MCDM (AHP + TOPSIS and AHP + VIKOR) approach, unprecedented in assessing pesticide removal methods for drinking water treatment, proves promising in ranking treatment processes for pesticides. The study emphasizes the serious consequences of excessive pesticide use, highlights treatment solutions like NF and GAC adsorption, and introduces cutting‐edge hybrid MCDM techniques for selection treatment process that meet technical, economical, and environmental requirements. All of these factors combine to provide a novel approach not only used to select the best option from alternatives, but also to prioritize alternative processes in drinking water treatment at pesticide removal practices.

Research performance of higher education in OECD countries: A hybrid multi-criteria decision-making approach

This study aims to analyze the research performance of 38 OECD countries in the last 10 years. For this purpose, the research performance of these countries was analyzed using multi-criteria decision-making (MCDM) methods with the productivity and publication impact data of 38 countries. The analyses were conducted both normal data and also with adjusted data based on the population and income level of the countries. Firstly, the importance level of research performance criteria was determined by three different MCDM methods. Using the weight values obtained from these methods, countries were ranked by three different MCDM methods used for ranking purposes. According to the results of the study, the publications in the highest cited 1% were determined as the most important and distinctive criterion. Secondly, the criterion expressing the relative impact of publications on the world was determined as the second most important criterion. In the analyses conducted without normalizing the data, USA showed the highest research performance. In the analyses conducted by considering the population of the countries, Switzerland showed the highest research performance. In the analyses conducted by taking into account the income level of the countries, Estonia has the highest research performance. As R&D expenditures, development level of countries and international collaboration increased, research performance of countries increased. However, with the increase in domestic collaboration, there has been a decrease in research performance. In addition, this research has shown how to use MCDM in the measurement of research performance by using objective methods with different backgrounds.

FDM process parameter selection by hybrid MCDM approach for flexural and compression strength maximization

Fused deposition modelling (FDM) is one of the mostly used additive technologies, due to its ability to produce complex parts with good mechanical properties. The selection of FDM process parameters is crucial to achieve good mechanical properties of the manufactured parts. Therefore, in this paper, a hybrid multi-criteria decision-making (MCDM) approach based on Preference Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed for the selection of optimal process parameters in FDM printing of polylactic acid (PLA) parts. Printing temperature, layer thickness and raster angle were considered as input process parameters. In order to prove the effectiveness of the proposed hybrid PSI – TOPSIS method, the obtained results were compared with the results obtained with different MCDM methods. The obtained best option of process parameters was confirmed by other MCDM methods. The optimal combination of process parameters to achieve the maximal flexural strength, maximal flexural modulus and maximal compressive strength is selected using the hybrid PSI-TOPSIS method. The results show that the hybrid PSI-TOPSIS approach could be used for optimisation process parameters for any machining process.

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

Development of Partition-Designed Outdoor Workwear with Optimal Fabric Selection

The necessity for anti-heat stress workwear to ensure the safety and performance of outdoor workers in hot, humid environments is clear. Yet, there is a gap in fabric selection techniques that consider multi-criteria decisions and account for the varying functional needs across different body regions. Here, we proposed a hybrid multi-criteria decision-making approach, merging the efficacy coefficient method, analytic hierarchy process, entropy weight, and technique of order preference by similarity to the ideal solution for developing ergonomic modular outdoor workwear. This method is tailored for anti-heat stress workwear, balancing competing functional demands. Initial research involved surveying workwear requirements in terms of human, clothing, and environmental factors, leading to the selection and testing of 15 fabrics for 6 partitioned designs. We established data standardization by the efficacy coefficient method and six evaluation index systems for human body requirements, with analytic hierarchy process and entropy methods determining subjective and objective criteria weights. The technique of order preference by similarity was used to produce the ideal solution then ranking of pre-screened fabrics, culminating in the optimal selection. For validation, three uniform prototypes were developed and tested through sweating manikin experiments and human wear trials to affirm the effectiveness of our approach. The partitioned design method proved more effective for anti-heat stress workwear for outdoor workers compared to existing solutions.

A Multi-Criteria Decision-Making Framework to Evaluate the Impact of Industry 5.0 Technologies: Case Study, Lessons Learned, Challenges and Future Directions

AbstractSmart technologies have demonstrated striking outcomes regarding the early diagnosis of diseases and the delivery of the necessary healthcare in the last decade. However, by emphasizing the core fundamentals of social justice and sustainability, together with digitalization and smart technologies that predicate raising productivity and flexibility, Industry 5.0 has proven to achieve more efficient results. Industry 5.0 technologies provide more intelligent ways for human employees and higher efficiency development while also improving safety and performance in many applications. In this research, the contribution is focused on the healthcare and how Industry 5.0 technologies demonstrate several advantages for the healthcare sector, starting with automated and precise disease prediction, moving on to aiding medical personnel in continual surveillance and monitoring and concluding with successful digital automation of smart equipment. The objective of this study is to apply a hybrid multi-criteria decision-making approach under a neutrosophic environment to evaluate the advantages of industry 5.0 technologies in the healthcare sector. Industry 5.0 primary value is to reach human-centric, sustainable, and resilient industries. While Industry 5.0 technologies sub-values regarding the healthcare sector are determined and distinguished according to the 3-main values mentioned previously based on literature. The methodologies applied in this study are: The Analytical Hierarchy approach (AHP) evaluates the main values and sub-values. Subsequently, the effectiveness of industry 5.0 technologies according to their values to the healthcare sector are ranked by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach is constructed under uncertainty based on a neutrosophic environment to achieve accuracy in the evaluation process. The results show that the most influential technology in healthcare are AI and cloud computing, while nano-technology, drone technology, and robots are at the end of the ranking. While validating the suggested technique, outcome comparisons were carried out to demonstrate the benefits of the methodologies. A sensitivity study indicates that adjusting the weightings of the sub-values has no significant effect on the ranking of technologies.

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.

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.