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To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

Open innovation (OI) literature suggests that firms can improve their innovation performance by learning from a large set of actors in the innovation process. However, the extant literature has overlooked the “who” question: which partners should be included in the different phases of the innovation funnel? How should they be selected? This paper aims to offer, while focusing in the early phases of the innovation process, a list of possible criteria for partner evaluation and suggests a structured methodology for their selection.

An empirical test of both the criteria and the methodology is presented with reference to a company operating in the Advanced Underwater Systems sector. The authors propose a peer-based modification of intuitionistic fuzzy (IF) multi-criteria group decision-making with TOPSIS method (peer IF-TOPSIS). IF-TOPSIS allows coping with subjectivity, imprecision and vagueness in group decision-making problem under multiple criteria.

The paper proposes an innovative application of a peer-modified version of IF-TOPSIS to a challenging and complex decision problem – partner selection for OI – which is usually subjected to uncertainty and evaluation from multiple experts.

This work contributes to the extant literature advancing the criteria that could be used in the selection process, avoiding focusing on single specific aspects of the collaboration phases or on specific types of partners (suppliers).

Pressure on business to direct their activities responsibly has been increased during the last years to extent their suitability performance in all economic, social and environmental dimensions. This has motivated businesses and researchers to identify ways to implement sustainable and resilient operations. In the era of economic globalisation, small and medium enterprises (SMEs) are recognised as an engine of sustainable economic development in both the developed and developing world. Their competitiveness drives the economy, both nationally and internationally. SMEs have faced challenges in developing, internationalisation and achieving competitive advantage. Purpose of current study is to identify and analyse the sustainability and resiliency (SR) barriers to SME internationalisation and prioritise the practices to overcome the negative influence of barriers. In this regard, first, barriers and innovative practices have been identified through the literature review. Second, the essential barriers will be selected through reduction steps by the intuitionistic fuzzy Delphi (IF-Delphi) method. After computing the weight of barriers through the IF-DEMATEL method, the practices were prioritised using four multiple attribute decision-making (MADM) methods in an IF environment. Finally, the scores were aggregated by correlation coefficient and standard deviation (CCSD) technique. Results present that ‘Lack of economical resources to global exports’ and ‘Complications in acclimatizing export product design’ are the top priority barriers and ‘Knowledge of global market opportunities’ and ‘Networking with business incubator institutions’ have been recognised as the essential SMEs internationalisation practices. This study contributes to creating a more focussed approach towards the growth of SMEs. The study results would be helpful for industry, policymakers and academia.

The purpose of this paper is to attempt supplier selection considering economic, environmental and social sustainability issues.

Subjective human judgment bears some kind of vagueness and ambiguity; fuzzy set theory has immense potential to overcome this. Owing to the advantage of intuitionistic fuzzy numbers set over classical fuzzy numbers set; three decision-making approaches have been applied here in intuitionistic fuzzy setting (namely, intuitionistic-TOPSIS, intuitionistic-MOORA and intuitionistic-GRA) to facilitate supplier selection in sustainable supply chain.

The stated objective of this research “to verify application potential of different decision support systems (in intuitionistic fuzzy setting) in the context of sustainable supplier selection” has been carried out successfully. A case empirical research has been conducted by applying three different decision-making approaches: intuitionistic fuzzy-TOPSIS, intuitionistic fuzzy-MOORA and intuitionistic fuzzy-GRA to an empirical data set of sustainable supplier selection problem. The ranking orders thus obtained through exploration of aforesaid three approaches have been explored and compared.

As compared to generalized fuzzy numbers, intuitionistic fuzzy numbers exhibit a membership degree, a non-membership degree and the extent of hesitation; a better way to capture inconsistency, incompleteness and imprecision of human judgment. Application potential of aforesaid three decision support approaches has been demonstrated in this reporting for a case sustainable supplier selection.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

The purpose of this paper is to provide a framework for assessing the overall innovativeness of manufacturing firms using a multi-attribute group decision-making methodology.

This study identifies the indicators of firms’ innovativeness from the literature. The concept of neutrosophic numbers has been used to assign different importance weights to individual decision makers to account for the differences in their educational backgrounds and practical experience. An intuitionistic fuzzy based TOPSIS procedure is adapted for ranking the candidate firms based on their performance on identified criteria. The implementation of the proposed methodology is demonstrated through an explanatory example. Sensitivity analysis is carried out to judge the robustness of the proposed framework.

The proposed framework provides an efficient and reliable tool to subjectively evaluate and compare the innovativeness of manufacturing firms. The sensitivity analysis shows that the methodology is robust enough to absorb the noise factors/errors/variations, etc.

Motivated by this work, future studies can consider developing an integrated innovativeness index for evaluation of innovativeness of manufacturing firms. The concept of interval valued intuitionistic fuzzy and neutrosophic sets can be utilized to reduce the margin of perceptual errors even further.

The study will provide the firms with a framework for benchmarking their innovative performance. The firms can analyze their current performance and reconfigure their resources and capabilities suitably to improve their competitive position.

This study is one of the few attempts that have been made to articulate a firm level innovativeness assessment tool for manufacturing firms operating in an industry sector. Advanced concepts of fuzzy and neutrosophic sets have been utilized to eliminate the chances of bias/perceptual errors that most often affect the quality of decisions in today’s dynamic and uncertain decision-making environment.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Due to stern management policies and increased community attentiveness, sustainable supply chain management (SSCM) performs a vast component in endeavor operation and production management. Sustainable circular supplier selection (SCSS) and evaluation presented the environmental and social concerns in the fields of circular economy and sustainable supplier selection. Choosing the optimal SCSS is vital for organizations to persuade SSCM, as specified in various researches. Based on the subjectivity of human behavior, the selection of ideal SCSS often involves uncertain information, and the Pythagorean fuzzy sets (PFSs) have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the multi-criteria decision-making (MCDM) procedure. Here, a framework is developed to assess and establish suitable suppliers in the SSCM and the circular economy.

This paper introduced an extended framework using the evaluation based on distance from average solution (EDAS) with PFSs and implemented it to solve the SCSS in the manufacturing sector. Firstly, the PFSs to handle the uncertain information of decision experts (DEs) is employed. Secondly, a novel divergence measure and parametric score function for calculating the criteria weights are proposed. Thirdly, an extended decision-making approach, known as PF-EDAS, is introduced.

The outcomes and comparative discussion show that the developed method is efficient and capable of facilitating the DEs to choose desirable SCSS. Therefore, the proposed framework can be used by organizations to assess and establish suitable suppliers in the SCSS process in the circular economy.

Selecting the optimal sustainable circular supplier (SCS) in the manufacturing sector is important for organizations to persuade SSCM, as specified in various research. However, corresponding to the subjectivity of human behavior, the selection of the best SCS often involves uncertain information, and the PFSs have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the MCDM procedure. Hence, manufacturing companies' administrators can implement the developed method to assess and establish suitable suppliers in the SCSS process in the circular economy.