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Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper’s primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment.

This study deals with the selection of a sustainable supplier on the example of the agribusiness company Mamex from Bosnia and Herzegovina. The main problem of this research is the selection of a sustainable supplier as a part of the sustainable strategy of the Mamex company. One of the prerequisites is that suppliers must present sustainability principles in business by having an appropriate certificate. The results of the selection of sustainable suppliers are completed using a new hybrid fuzzy approach with the methods IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy TRUST (multi-normalization multi-distance assessment) CRADIS (compromise ranking of alternatives from distance to ideal solution) methods. The innovative approach is reflected in the use of a combination of these methods, especially by combining the TRUST and CRADIS methods into one method. The IMF SWARA method shows that the most important main criterion is the economic criterion, while the least important is the social criterion. By applying the fuzzy TRUST CRADIS method, it is found that out of the observed six suppliers, the second supplier has the best indicators. These results are confirmed by other fuzzy methods: MABAC (multi-attributive border approximation area comparison), WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), MARCOS (measurement of alternatives and ranking according to compromise solution), ARAS (a new additive ratio assessment), and TOPSIS (technique for order preference by similarity to an ideal solution). This research shows that applying more normalization when ranking alternatives reduces the influence of individual normalizations, and this approach should be used in future research.

One of the major issues of the construction industry has been the “reworks” that affect the time, quality, and cost of projects. Therefore, reworks and the ineffective use of site resources and materials will always result in significant losses on projects. The development of information technology has led to the widespread use of Building Information Modelling (BIM) to enhance the delivery of more sustainable building construction projects. The purpose of this study is to combine the Step-wise Weight Assessment Ratio Analysis (SWARA) method and BIM technologies to identify and reduce time delays caused by reworks in construction projects. Firstly, 49 rework causes in residential buildings were identified and ranked. Then, BIM was generated and compared to the initial model. It was observed that working hours were reduced by 4.6%. Moreover, using an Earned Value Management (EVM) system, a 0.06 increase in Schedule Performance Index (SPI) factor was illustrated. Results obtained by this study provide an effective step in reducing a project’s time in the construction industry.

This work applies a multi-criteria group decision-making (MCGDM) method to select the best treated wastewater reuse allocation alternative for augmenting water supply. The additive ratio compromise assessment (ARCAS) hybrid method is based on the integration of stepwise weight assessment ratio analysis (SWARA) (for weighting the criteria) with the adapted additive ratio assessment (ARAS) multi-criteria decision-making method in a group decision-making framework with new normalization scheme for the decision-making matrix̕s elements. For this purpose, four main criteria with 15 sub-criteria and six alternatives for treated wastewater reuse, i.e., landscape irrigation, agricultural irrigation, application in the industrial sector, artificial aquifer recharge, recreational sector, and supplying environmental demands are considered. Experts’ opinions are gathered and the steps of the ARCAS method are applied. The results show that the agricultural irrigation alternative is top ranked. The final ranking of the treated wastewater reuse alternatives is achieved by evaluating the alternatives and revising the criteria’s weights by the experts.

Hesitant fuzzy linguistic term sets (HFLTSs) and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) are two frequently used linguistic information forms in uncertain decision-making environments. However, they only include membership grades and cannot yield fuzzy information from a negative aspect. A bipolar fuzzy set can quantify evaluation information from positive and negative sides using positive and negative memberships, respectively. To address this issue, double hierarchy bipolar hesitant fuzzy linguistic term sets (DHBHFLTSs) are proposed, which can highlight the importance of the negative membership degree, and the objects can be evaluated from positive and negative aspects. Furthermore, DHBHFLTSs increase the reasonableness and comprehension of the evaluation information in the process of optimal talent selection. This paper proposed a framework involving the stepwise weight assessment ratio analysis (SWARA) method and the extended weighted aggregated sum product assessment (WASPAS) method. The extended WASPAS method is utilized to aggregate the evaluation information of all the alternatives under the DHBHFLTSs context. So, this proposed method increases the ranking accuracy. The SWARA method is extended to DHBHFLTSs to rank and determine the criteria. This weight determination method is helpful for coordinating and gathering data from experts. Therefore, the proposed method can obtain the weight values efficiently. Subsequently, a case of talent selection is utilized to show the feasibility and applicability of the proposed framework. Finally, the accuracy and comparison analyses with other methods illustrate the superiority of this framework.

Public transport plays an essential role in helping people escape from the congested traffic in large and crowded cities. Evaluating customer satisfaction is the key to the continuous quality improvement of public transport services. This work proposes a new public transport customer satisfaction evaluation approach based on an extended thermodynamic method with q -rung orthopair fuzzy sets. First, we use the q -ROFSs to handle the ambiguity and uncertainty of customer satisfaction evaluation information for public transport. Then, we extend the thermodynamic method to determine the customer satisfaction levels of public transport lines. Moreover, the stepwise weight assessment ratio analysis method is utilized to specify the weights of evaluation criteria as it is simple and time-saving. The effectiveness of the proposed approach is illustrated with a customer satisfaction evaluation of the rail transit network in Shanghai, China. Results showed that Line 7 has the highest customer satisfaction and Line 2 has the lowest customer satisfaction among the considered rail transit lines. Besides, nine evaluation criteria need to be optimized to improve the customer satisfaction level of Line 2 and the most critical one is train crowding.

The freight transport industry is one of the primary sectors responsible for excessive energy consumption and greenhouse gas emissions. Restructuring international and domestic freight transport chains based on sustainability and green transportation is critical for practitioners and policymakers to reduce pressure on the logistics and transportation industries. This study aims to develop a mathematical model for selecting the most appropriate transportation type, and accordingly, the optimal route in transportation operations to improve the sustainability performance of the freight transportation industry. Therefore, the main goal is to choose the most suitable route and transportation type which contributes to create a more eco-friendly and sustainable transportation system. For this purpose, Neutrosophic Number-based Delphi (NN-Delphi), m-Generalized q-Neutrosophic Sets (mGqNSs)-based Stepwise Weight Assessment Ratio Analysis (MGqNS-SWARA) and mGqNSs-based Additive Ratio Assessment (mGqNS-ARAS) are developed and implemented to set the influential criteria, compute the weights of these criteria, and identify the sustainability performance of the freight mode variants, respectively. According to the final results, \"Cargo security\" and \"Accident rates\" are the most important criteria with a relative importance score of 0.0237, contributing to the sustainability of load transport modes. Moreover, \"Maritime Transport Mode\" is identified as the most sustainable transportation type with a relative importance score of 0.7895. Finally, it is revealed that there is a positive relationship between maritime transport and sustainability.

The present study aims to analyze the impact of the COVID-19 outbreak resulting in Cold Supply Chain (CSC) disruptions and shed new light on the potential opportunities yielded from the pandemic. In addition, the work also aims to explore the most appropriate strategies to minimize CSC disruption due to the COVID-19 outbreak and to repurpose to create conditions as they were before the pandemic. The impact of the COVID-19 outbreak on CSCs has been analyzed theoretically and empirically, considering seven broader assessment criteria. To diminish the disruption due to COVID-19, eight of the most appropriate remedial strategies have been proposed in this study. A new hierarchical model was developed to articulate the analysis and consolidate various issues pertinent to CSC disruption caused by COVID-19 in one frame. The developed model was analyzed using a hybrid approach of SWARA-based MULTIMOORA methods. The SWARA method has been used to analyze the significance of considered assessment criteria, while the MULTIMOORA method has been used to analyze the mutual importance of proposed strategies. The findings of this paper show that ‘structural impact’ and ‘business and financial impact’ are the two most affected traits of CSC during the COVID-19 pandemic throughout the world. Meanwhile, the strategies ‘development of safe and healthier work scenario for partners of the cold chain’ and ‘successful monitoring and implementation of COVID-19 protocols’ are the two most important proposed strategies that might help management to mitigate the influence of the COVID-19 outbreak on CSCs. Findings of this research enable CSC managers and policy-makers to develop potential and robust plans for their operations to respond to disruptive situations like COVID-19 and turn them into opportunities for organizational growth and improving the health of society.

PurposeThe current study aims to deliver a consolidated view of environmental sustainability in cold supply chain performance systems (CSCPS), incorporating theoretical and empirical analysis for improving environmental standards. For this purpose, this study firstly aims to explore and analyze the various crucial challenging factors for environmental sustainability in the cold supply chain (CSC). Secondly, it discovers the most effective sustainable strategies for improving the environmental sustainability of CSCPS.Design/methodology/approachThe exploration of the crucial challenging factors and the proposed sustainable strategies have been done using a systematic literature review relevant to the sustainable performance of CSC. At the same time, semi-structured brainstorming sessions were conducted with the domain professionals having an industrial and academic background to finalize the strategies. Empirical analysis has been performed using an intuitionistic fuzzy (IF) based hybrid approach of SWARA and COPRAS methods.FindingsThe key findings of the study address that “higher energy consumption during refrigerated transportation and storage” is the most crucial challenge for environmental sustainability in CSC. In addition, “managerial refrain to profit decline due to sustainability implementation” is the second most crucial challenge that hinders the adoption of sustainable practices in CSCs. Meanwhile, the governmental attention to motivating organizations for green adoption and implementation of solar energy-driven refrigeration technologies are the two most important discoveries of the study that might help in improving CSC's environmental performance.Research limitations/implicationsFrom the implications side, the study enriches and extends the current literature content on CSC sustainability. In addition, it offers sound managerial implications by identifying the challenges that create threats among the management for sustainability adoption and suggesting the most suitable sustainable strategies, which may help the management to raise the environmental performance of their CSC. Besides having various important theoretical and managerial implications for the study, contemplation of only environmental sustainability traits as a broader perspective limits the scope of the study.Originality/valueThe study's main contribution is the exploration of the most crucial challenges imparting obstructions in sustainable development and sustainable strategies, which may get the interest of the CSC players, market leaders, and industrial and academic practitioners working in the domain of CSC sustainability. In addition, this study offers structured theoretical and empirical evidence for CSC's environmental sustainability, thus playing a bridging role between theoretical sustainability concepts and its practical implications in CSC industries.