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The age of industrialization and modernization has increased energy demands globally. Solar energy has been recognized as an inexhaustible source of energy and has been applied for desalination and electricity generation. Among different non-conventional energy resources, Solar Energy (SE) is one of the main contributors to the global energy system. A photovoltaic system (PS) is applied to produce SE using photovoltaic cells. The selection of a solar panel includes many intricate factors involving both subjective and quantifiable parameters; therefore, it can be regarded as a complex Multi-Criteria Decision-Making (MCDM) problem. As the uncertainty commonly occurs in the selection of an ideal solar panel, the theory of Pythagorean fuzzy sets has been proven as one of the flexible and superior tools to deal with the uncertainty and ambiguity that arise in real-life applications. The aim of the study is to present an MCDM framework for solving the Solar Panel Selection (SPS) problem within the Pythagorean fuzzy (PF) environment. For this, first, a new integrated method is proposed based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) approaches in the Pythagorean fuzzy sets (PFSs) context. In the proposed approach, subjective weights of the evaluation criteria are calculated by the SWARA method, and the preference order of alternatives is decided by the VIKOR method in the PF context. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. The computational procedure of the proposed methodology is established through a case study of the SPS problem under PF environment, which proves the applicability and efficiency of the proposed method. Furthermore, this study performs sensitivity analysis to reveal the stability of the developed framework. This analysis signifies that the solar panel option R4 constantly secures its highest ranking despite how the parameter values vary. In addition, a comparative study is discussed to analyze the validity of the obtained result. The results show that the proposed approach is more efficient and applicable with previously developed methods in the PFS environment.

Abstract Nowadays, health-care waste management is a challenging issue for both the public and government sectors because it contains infectious, radioactive, or hazardous waste. Selecting a suitable method for health-care waste disposal is known as a complex decision-making problem due to the existence of several multiple criteria that may in conflict with each other. In this paper, we propose a novel method called evaluation based on distance from average solution framework based on parametric divergence measures with the context of intuitionistic fuzzy sets to evaluate and rank the health-care waste disposal alternative. To do this, first, novel parametric intuitionistic fuzzy divergence measures are developed, and various desired properties have also been examined. Second, the decision experts are evaluated, and the criteria weights are computed by developed parametric divergence measure method. Third, the preference order of the alternatives is illustrated by the developed framework. The proposed framework is based on the positive and negative distances from the average solution. Moreover, the health-care waste disposal alternative selection problem is considered to elucidate the applicability of the proposed framework. Four health-care waste disposal alternatives, including incineration, steam sterilization, microwave and landfill disposal, are considered in this study. The health-care waste disposal alternatives considered in this study include. The outcome illustrates that, the steam sterilization was the highest appraisal score (0.7025) and therefore, it should be chosen as the most suitable health-care waste disposal method in this study. Also, a comparison with existing approaches is shown to illustrate the validity and practicability of the developed framework.

In recent years, the assessment of desirable renewable energy alternative has been an extremely important concern that could change the environment and economic growth. To tackle the circumstances, some authors have paid attention to selecting the desirable renewable energy option by employing the decision-making assessment and linguistic term sets. With a fast-growing interest in multi-criteria group decision-making (MCGDM) problems, researchers are tirelessly working towards new techniques for better decision-making. Decision makers (DMs) generally rate alternatives linguistically with different probabilities occurring for each term. Previous studies on linguistic decision-making have either ignored this idea or have used an only a single value for representing the weight of the linguistic term. Since expression of the complete probability distribution is hard and implicit hesitation exists, representation of weights of the linguistic terms using a single value becomes imprecise and unreasonable. To avoid this challenge, an interval-valued probabilistic linguistic term set (IVPLTS) is used, which is a generalization of (probabilistic linguistic term set) PLTS. Inspired by the usefulness of IVPLTS concept, we develop a decision framework for rational decision making. Initially, some operational laws and axioms are presented. Further, a novel aggregation operator known as interval-valued probabilistic linguistic simple weighted geometry (IVPLSWG) is developed for aggregating DMs’ preferences. Also, criteria weights are determined using the newly developed interval-valued probabilistic linguistic standard variance (IVPLSV) approach and alternatives are ranked using the extended VIKOR (VlseKriterijumskaOptimizacijaKompromisnoResenje) method under IVPLTS environment. Finally, a numerical example of renewable energy assessment is demonstrated to show the practicality of the developed decision framework. Also, the strengths and weaknesses of the developed decision framework are illustrated by comparison with existing ones.

The energy storage is an important character for sustainable energy structures and the prospective future economy. This paper aims to propose a multi-attribute decision analysis (MADA) approach to prioritize and choose the energy storage system (ESS) alternatives in terms of the different technical, economic, environmental and social aspects of them. In this line, an integrated approach is developed with the combination of intuitionistic fuzzy sets (IFSs), a method using the removal effects of criteria (MEREC), rank sum (RS) and the measurement of the alternatives and ranking based on compromise solution (MARCOS) methods for prioritizing the ESSs. The IF-MEREC-RS was used to find the integrated weight by combining the objective and subjective weights of the different indicators for prioritizing the ESSs. The MARCOS method was implemented to rank the various ESSs over several crucial indicators of sustainability. The practical outcome illustrates that the Li-ion battery (LIB) is the best ESS among all of the options, and this is followed by NaSB and NiMHB. A sensitivity investigation with the diverse weights of the indicators shows the impact of the risk preferences on an alternative prioritization. A comparison is discussed with the outcomes of the different presented, extant approaches to certify the superiority of the presented approach.

The research highlights the importance of sustainable supply chain management (SSCM), technology adoption (TA), and performance measurement in promoting sustainability and improving supply chain performance. By incorporating sustainable practices and utilizing digital technologies, organizations can create a more sustainable future and improve their overall performances. This study conducted an in-depth review of the literature to investigate the presence of TA in SSCM with a focus on digital-based supply chains. The review used both bibliometric and content analysis methods to analyze relevant research articles, with the goal of providing a comprehensive understanding of the current state of research in the field, identifying any gaps in the literature, and providing direction for future research. The content analysis of the literature showed the absence of concrete frameworks for SSCM and the need for clearer and more applicable sustainability measurement indices. To address this gap, the study proposed a framework for achieving sustainable development goals through SSCM. In addition, a framework for deploying sustainability indicators was presented. The proposed framework can be used by practitioners to develop practical and comprehensive measures for their respective industries.

<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>

Bioenergy is a kind of renewable energy that can potentially contribute to a broad spectrum of economic, environmental, and societal objectives and aid sustainable development. The assessment, management, and monitoring of the diverse bioenergy production technology alternatives are complex in nature and deliver different benefits due to the lack of precise and comprehensive data. Selection of an optimal bioenergy production technology (BPT) alternative is considered a complex multi-criteria decision-making (MCDM) problem that involves many incompatible tangible and intangible as well as qualitative and quantitative criteria. The procedure of defining and evaluating the weights of the criteria is an important concern for decision experts because the assessment and the final selection of the BPT alternative are carried out on the basis of the defined set of criteria. Intuitionistic fuzzy sets (IFSs) have received considerable attention due to their ability to handle the imprecision and vagueness that can arise in real-life situations. Thus, this study presents an integrated approach, based on stepwise weight assessment ratio analysis (SWARA) and complex proportional assessment (COPRAS) approaches, for the selection of BPT alternatives. In the integrated framework, criteria weights are determined by the SWARA procedure, and the ranking of BPT alternatives is decided by the COPRAS method using IFSs. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. To express the efficiency and applicability of the integrated framework, a BPT selection problem is presented using IFSs. In addition, this study involved sensitivity analysis with respect to various sets of criteria weights to reveal the strength of the developed approach. The sensitivity analysis outcomes indicate that the agricultural and municipal waste of biogas (S3) consistently secures the highest rank, despite how the criteria weights vary. Finally, a comparative study is discussed to analyze the validity of the obtained result. The findings of this study confirm that the proposed framework is more useful than and consistent with previously developed methods using the IFSs environment.

As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.