Hybrid Decision-making Approach Research Articles

Optimized Grid Partitioning and Scheduling in Multi-Energy Systems Using a Hybrid Decision-Making Approach

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

Optimizing CI Engine Ethanol Fuel Induction Techniques Using the AHP-PROMETHEE II Hybrid Decision Model

Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. Both indices fall below the predetermined threshold limits, indicating a high level of consistency of the model.

Building sustainable resilient supply chain in retail sector under disruption

Blockchain technologies have played a crucial role in transforming the retail industry, leading to remarkable advancements in recent times. Its pivotal role in managing risky environments by offering preventive and proactive measures cannot be overstated. The research contribution lies in introducing a set of criteria for assessing the adoption of Blockchain technology, specifically designed to evaluate the resilience of the retail sector. This study aims to ensure the establishment of a sustainable, resilient supply chain across diverse retail categories, particularly in the face of uncertain circumstances. A hybrid decision-making approach that combines the Best-Worst Method (BWM) and Fuzzy TODIM has been employed to achieve these objectives. This study encompasses various types of retail companies to assess and compare their resilience levels by adopting Blockchain technology. The results of this study robustly suggest that speciality retailers with well-established, long-term partnerships are more predisposed to embrace and leverage the capabilities of Blockchain technologies.Conversely, discount retailers in Turkey face various challenges that impede their effective integration of Blockchain technologies. These challenges encompass collaborating with suppliers on short-term agreements and the unavailability of product tracking, among other factors. As a result, the outcomes of this study offer valuable insights for retailers in the sector, suggesting that they should consider modifying their operational strategies to better align with the adoption and integration of Blockchain technologies in the future.

An intuitionistic fuzzy based hybrid decision-making approach to determine the priority value of indicators and its application to solar energy feasibility analysis

Solar plant feasibility involves assessing its practicality and viability for successful development and operation. It involves evaluating various factors to determine whether the project is technically, economically, and environmentally feasible. Feasibility analysis considers solar resource availability, site selection, technology options, financial viability, grid integration, regulatory compliance, and potential risks. By conducting a comprehensive feasibility study, developers can determine if a solar plant is a feasible and sustainable solution to generating renewable energy at a specific location. An intelligent mitigation measure is proposed in this paper to control the trade-off by using certain indicators that have the greatest influence on the feasibility of a solar power plant. This study proposes a novel subjective weighting technique known as the Intuitionistic Fuzzy Consistency Method (IF-FUCOM) to achieve the highest degree of precision when considering fuzziness. Intuitionistic Fuzzy Set (IFS) handles imprecise information flexibly. Therefore, the IFS has gained interest from experts and is being applied to various fields, especially decision-making. Furthermore, the study identifies the local weights of alternatives using the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP). This study proposed a hybrid method, IF-FUCOM-AHP, for determining the global weight. Its robustness and effectiveness are demonstrated by comparing the proposed fuzzy model with three existing hybrid methods and developing a novel index for evaluating solar project feasibility and location. Finally, a scenario analysis and sensitivity analysis of different options are conducted for different solar power plants. The benefit of the suggested approach lies in its IFS utilization. This method resolves the issue by delineating the non-membership and membership degrees of elements within a fuzzy set, while also providing a means to articulate a particular level of uncertainty.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 μgL−1 (carbamazepine) and higher than 61.56 μgL−1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 μgL−1 (diazepam) and higher than 54.91 μgL−1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Sustainable construction project of electric vehicle charging stations: A risk-based hybrid decision-making approach

Nowadays, a serious environmental challenge is the pollution emitted by combustion engine vehicles. Governments have found that replacing these with electric vehicles (EVs) is a viable solution for sustainable transportation. The sustainable operation of fast charging stations for EVs is a critical parameter in these vehicles' popularity. Herein, a propose a model for the sustainable evaluation of energy sources of charging stations based on the R-numbers and combined compromise solution method (R–COCOSO). The construction of EV charging stations is done based on new and developing technologies, and they are often expensive, complex, and high-risk. Thus, we developed the combinative distance-based assessment (CODAS) method based on R-numbers to assess the project risk. Moreover, a linear programming model was developed to assess the contractors’ risk response and the optimal allocation of their workload based on R-numbers and the ε− constraint approach. A real case of building charging stations in Tehran, Iran, was presented to test the accuracy of the model.

Evaluating a sustainable circular economy model for the Indonesian fashion industry under uncertainties: a hybrid decision-making approach

ABSTRACT There is a large increase in the wastes and emissions from the Indonesian fashion industry under economic and population growth. To address this issue, the sustainable circular economy is proposed to reach the goal of sustainable development and zero waste. Previous studies neglect to provide a specific interrelationship model to guide practice to reach a sustainable circular economy. This study proposes a hybrid method integrating exploratory factor analysis, the fuzzy synthetic method and the decision-making trial and evaluation laboratory approach to overcome these shortcomings. It contributes to enhancing the understanding of theory through visual diagrams. The hybrid method makes it possible to interpret complex interrelationships and leads to improvements under resource limitations based on the model. The results reveal that emission reduction and welfare improvement are the causal aspects.

Developing sustainable competitive advantages from the lens of resource-based view: evidence from IT sector of an emerging economy

ABSTRACT The present study examines the IT industry’s sustainable competitive advantages (CAs) from a resource-based view (RBV). Therefore, a list of CAs in three areas of the triple bottom line (TBL), including economic, social, and environmental, are extracted by thoroughly studying the relevant literature. These attributes are then evaluated by small and medium enterprises (SMEs) managers in the IT sector by implementing a Multi-level hybrid Decision-Making approach. This step employs a modified Delphi technique combined with linguistic z-numbers. Further, by reviewing the literature, the list of intangible resources is counted, and the importance of these resources is measured via the linguistic z-number BWM (Best Worst Method). Finally, the degree of the controllability of critical CAs is prioritised by z-number TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and then classified found on the resources. Consequently, different classes are discussed, and appropriate strategies for dealing with each are presented.

Materials selection of 3D printed polyamide-based composites at different strain rates: A case study of automobile front bumpers

The selection of 3D printed composite materials considering multiple criteria has become a challenging issue for designers/engineers and can be regarded as a typical decision-making problem. Especially for diverse engineering applications, the different strain rates encountered need to be addressed in the decision-making process. In this study, a systematic hierarchical evaluation model of 3D printed polyamide-based composites with 27 indicators is established considering economic, environmental, social and mechanical aspects. The mechanical responses at four strain rates (8 × 10−4 s−1, 8 × 10−3 s−1, 1700 s−1 and 3510 s−1) are incorporated into the model. A hybrid decision-making approach combining the fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory)-Entropy and fuzzy TODIM (Tomada de Decisão Interativa Multicritério)-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) methods is proposed to solve the composite material selection problem. An automobile front bumper, as an empirical application, is investigated to demonstrate the proposed model and decision-making approach. The results show that PACF-C, that is the short carbon fibre reinforced polyamide printed with a concentric strategy, is the optimal alternative. This study provides an effective approach for solving the material selection problem of 3D printed composites.

A hybrid decision-making approach for locating rescue materials storage points under public emergencies

PurposeIn modern urban governance, rescue materials storage points (RMSP) are a vital role to be considered in responding to public emergencies and improving a city's emergency management. This study analyzes the siting of community-centered relief supply facilities.Design/methodology/approachCombining grey relational analysis, complex network and relative entropy, a new multi criteria method is proposed. It pays more attention to the needs of the community, taking into account the use of community hospitals, fire centers and neighborhood offices to establish small RMSP.FindingsThe research results firstly found suitable areas for RMSP site selection, including Hanyang, Qiaokou, Jiangan and Wuchang. The top 10 nodes in each region are found as the location of emergency facilities, and the network parameters are higher than ordinary nodes in traffic networks. The proposed method was applied in Wuhan, China and the method was verified by us-ing a complex network model combined with multi-criteria decision-making for emergency facility location.Practical implicationsThis method solves the problem of how to choose the optimal solution and reduces the difficulty for decision makers. This method will help emergency managers to locate and plan RMSP more simply, especially in improving emergency siting modeling techniques and additionally in providing a reference for future research.Originality/valueThe method proposed in this study is beneficial to improve the decision-making ability of urban emergency departments. Using complex networks and comprehensive evaluation techniques, RMSP is incorporated into the urban community emergency network as a critical rescue force. More importantly, the findings highlight a new direction for further research on urban emergency facilities site selection based on a combination of sound theoretical basis as well as empirical evidence gained from real life case-based analysis.Highlights:Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.Build emergency site selection facilities centered on urban communities.Use a complex network model to select the location of emergency supplies storage sites.

Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning

The aim of this study was to assess the environmental impact of hydroelectric power generation projects and classify them according to their scale of environmental impact. To achieve this objective, the combination of Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN) techniques was applied to 53 hydroelectric power plant projects in the evaluation phase in Brazil. The main results were: a) the proposed index indicates that 7 of the 10 worst hydroelectric projects are of the Large Hydropower Plant (LHP) type; b) the neural model for predicting the environmental impact of hydroelectric projects has an error of less than 0.001; c) the neural model for classifying hydroelectric projects in terms of their environmental impact reached a performance of 99.0% accuracy. In general, this study contributes to the use of a hybrid decision-making approach based on a combination of DEA-ANN for energy policies, in addition to enabling an improvement in the evaluation of hydroelectric generation projects.

A Recommendation Model for Selling Rules in the Telecom Retail Industry

The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer’s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a hybrid decision-making approach based on K-means and the C5.0 decision tree to analyze the historical sales information of telecom retailers. To evaluate the efficacy of the proposed recommendation model, it was used to analyze original data from a case company. The results indicated that the proposed hybrid decision-making approach resulted in sales content with a high gross profit and high agreement rates. The experimental results show each cluster that can be used to identify rules for the combination of good tariff items in different tariff ranges. Rules for the recommendation of special tariffs are also established to assist salespeople.

Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach

Effective crowdfunding platforms positively contribute toward improving microgrid energy management systems. Therefore, a comprehensive analysis is required to understand the key factors responsible for success in crowdfunding systems by considering various criteria. This study evaluates crowdfunding platforms for microgrid project investments. In this context, a novel fuzzy decision-making model that includes two different stages is proposed. First, the selected criteria for the crowdfunding platforms for microgrid project investments are evaluated. Second, alternatives, regarding the microgrid project investments, are ranked. In this process, a multi-stepwise weight assessment ratio analysis (M-SWARA) approach based on q-rung orthopair fuzzy sets (q-ROFSs) is considered. Intuitionistic and Pythagorean fuzzy sets are also used in the calculation process to make a comparative evaluation. Similarly, a sensitivity analysis of the ranking alternatives is also conducted with 12 different q values. All the results are rather similar; thus, the findings are reliable. Another model is also created for this purpose with the help of the decision-making trial and evaluation laboratory (DEMATEL) and the technique for order preference by similarity to ideal solution methodologies to check the performance of the proposed model. It is defined that by considering the q-ROF DEMATEL weights, the ranking results vary for different cases. The proposed model with a M-SWARA is more reliable than the model created via the DEMATEL method. This situation provides information regarding the superiority of the model proposed in this study. It is concluded that security is the most important factor in crowdfunding platforms for smart-grid project investors. Additionally, solar panels and energy storage systems/batteries are the most significant alternatives for microgrid project investors. Necessary measures should be taken to forestall the risk of fraud that may occur on this platform. Therefore, the website to be established must be secure against possible hacking attacks. Another important conclusion of this study is that solar panels should be preliminarily developed to increase the effectiveness of microgrid systems.

Bipolar q-ROF Hybrid Decision Making Model With Golden Cut for Analyzing the Levelized Cost of Renewable Energy Alternatives

Energy costs are the key factors regarding the selection of appropriate renewable energy (RWG) alternatives. All costs of a power plant, such as investment, operation, maintenance, and repair are considered in the scope of levelized costs. Therefore, for the effective determination of the selling price of the energy, levelized cost has a guiding role. Because the levelized costs of RWG alternatives develop the sustainable production and energy consumption for the long term, the leading indicators of these costs should be analyzed significantly. Accordingly, in this study, it is aimed to investigate the levelized cost of RWG alternatives by using bipolar q-rung orthopair fuzzy (q-ROF) hybrid decision-making approach. The novelty of this study is to recommend an integrated decision-making model based on bipolar and q-ROFSs with golden cut. At the first stage, bipolar q-ROF multi stepwise weight assessment ratio analysis (M-SWARA) is employed for weighting the selected criteria of levelized costs of RWG alternatives. At the following stage, bipolar q-ROF technique for order preference by similarity to ideal solution (TOPSIS) is considered to rank the alternatives in terms of the levelized cost performance. On the other side, vise kriterijumska optimizacija i kompromisno resenje (VIKOR) model is also considered to rank the alternatives. In addition to this issue, the sensitivity analysis is also performed with four cases comparatively. Hence, consistency, reliability and coherency of the proposed model can be measured. It is identified that capacity loss has the greatest importance regarding the levelized cost of RWG projects. Solar is found as the best clean energy type with respect to the levelized cost management performance. In this context, it would be appropriate for investors to design projects close to the center. This will contribute to increasing the efficiency and productivity of these projects.

Assessment of sustainable urban development based on a hybrid decision-making approach: Group fuzzy BWM, AHP, and TOPSIS–GIS

The present study aimed to develop a practical framework consisting of GIS and MCDM to assess the development sustainability of Karaj, a city in the west of Tehran, Iran. Firstly, the influencing factors on sustainable urban development including seven criteria and 24 sub-criteria were screened and selected based on literature review, interviews with experts and Delphi technique. Then, the criteria were weighted and prioritized using Group Fuzzy BWM (GFBWM) and Analytic Hierarchy Process (AHP) methods. Fuzzy logic and Weighted Linear Combination (WLC) methods in GIS were used to determine the sustainability of Karaj for urban development. Finally, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the identified areas. Our findings showed that the socioeconomic criterion and employment sub-criterion were the most important in AHP and GF-BWM methods. Both methods estimated that about 30% of the entire study area was highly sustainable in terms of urban development with no significant difference between the two methods. Finally, TOPSIS selected the center and north of the city as those with the highest priority. The integrated approach used in this study provided a flexible tool for analysis of the sustainable development, useful for planners and city managers.

Forecasting sustainability of supply chains in the circular economy context: a dynamic network data envelopment analysis and artificial neural network approach

Purpose The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs. Design/methodology/approach It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal. Findings A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously. Originality/value We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

Green sourcing in the era of industry 4.0: towards green and digitalized competitive advantages

PurposeIn the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.Design/methodology/approachA new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.FindingsThe proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.Originality/valueCompetitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

Evaluating key capabilities for developing global collaborative networks using a multi-layer decision-making approach

PurposeThis paper proposes a multi-layer hybrid decision-making approach to evaluate the capability alternatives for developing a collaborative network to operate in the international market.Design/methodology/approachThe present study is contextualised in the Iranian pistachio export industry. An extensive review of the state-of-the-art literature on supplier collaboration was conducted to identify key capabilities that are essential to establish a collaborative network. The set of defined capabilities were then optimised through interviews with 14 experts from the relevant industry, academics and export authorities. A combination of the fuzzy Delphi method and the best–worst method (BWM) approach was, respectively, used to reduce the number of capability alternatives and assign priority weights to these alternatives. Subsequently, a weighted aggregated sum product assessment method (WASPAS) was employed to rank and evaluate the ability to creating a collaborative network for the export of pistachio.FindingsFrom the extant literature review, 18 capabilities for the formation of coordination networks in the international markets were identified. Then, the prominent indicators in forming a global network were extracted. After ranking the top pistachio export countries/regions to formalise an efficient collaborative network, it was revealed that although Iran exports approximately 30% of the global market, it falls behind the USA and European Union. The competitors have scored higher in critical criteria, including “trust and commitment”, “strategy and management”, “managerial control and standardization” and “financial resources”.Originality/valueThe proposed hybrid approach encompassing fuzzy Delphi–BWM–WASPAS offers to solve the capability evaluation and selection as well as ranking the possible alternative to formalise a collaborative network in an integrated fashion. This combination of methods is capable to first identify the most important factors, then measuring their importance and eventually rank the possible alternatives. The suggested framework provides an approach to deal with the uncertainty of global collaborative network formation.

Determining the optimal portfolio for healthcare processes management using a hybrid decision-making approach

Due to the increasing progress in various industries, paying attention to the internal processes of the organizations is more visible to stay on the competitive scene. Therefore, many organizations attempt to simplify and evaluate their internal processes using re-engineering. By reviewing the conducted studies, it can be stated that one of the existing problems in the implementation of re-engineering projects is the selection of the optimal portfolio of processes. Hence, this study aims to provide a bi-objective mathematical model for selecting processes in the re-engineering project by considering two key assumptions include improvement in achieving organizational goals and staff resistance. To this end, first, the impact of processes on organizational goals is specified by experts and then the goals’ weights are obtained using a fuzzy Best Worst Method. Finally, the proposed model is solved by an augmented e-constraint method and the optimal portfolio of processes is selected. Also, a public Hospital of Sari as a real-world case study is employed to set the values of model parameters. Finally, the obtained results are reported and using a sensitivity analysis, several directions are provided. The results show that changes in the staff resistance directly affects the second objective function, while changes in the improvement created by each process affect the first objective function. Also, changes in costs have little effect on either objective functions.

Selection of smart fuel opus for diesel engine depending on their fuel characteristics: an intelligent hybrid decision-making approach.

Internal combustion engines are the inevitable prime movers in the contemporary engineering era. The suitability of proper bio-fuel and their blends plays a vital role in engine behaviour. This study aims to select smart fuel opus depending on Aegle marmelos (AM) fuel properties with nano additive blends for diesel engines by using intelligent hybrid decision-making tools. Physicochemical properties of CuO and novel graphene nano sheets added bio-oil combinations were studied. The assessment of an appropriate blend depends on the analysis of fuel properties. The Fuzzy Analytical Hierarchy Process (FAHP) integrated with Grey relational analysis (GRA) was employed for optimum fuel blend selection. The FAHP model was used to identify the criteria weights, whereas GRA was hired to rank alternative fuel blends. Pairwise analysis and ranking of the alternatives were compared to get the optimum fuel blend through FAHP and GRA amalgamation. The addition of nanoparticles enhanced engine performance and reduced emission. The obtained ascending order of preference of the bio-oil blends from FAHP and GRA analysis is AC15G15>AG30>AC30>A10>A20. From FAHP, GRA, and engine test results, it is observed that AC15G15 opus is the most suitable fuel blend for diesel engines. Lower fuel consumption (0.37 kg/kW hr) and emissions (CO level of 0.21%, which is 0.34% for diesel, HC value of 134 ppm, which is 184 ppm for diesel) of AC15G15 aids in contributing towards a green and clean environment.