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For some specific multi-criteria decision-making (MCDM) problems, especially in emergency situations, because of the feature of criteria and other fuzzy factors, it is more appropriate that values of different criteria are expressed in their correspondingly appropriate value types. The purpose of this paper is to build a multi-criteria group decision-making (MCGDM) model dealing with heterogeneous information based on distance-based VIKOR to solve emergency supplier selection in practice appropriately and flexibly, where a compromise solution is more acceptable and suitable.

This paper extends the classical VIKOR to a generalized distance-based VIKOR to handle heterogeneous information containing crisp number, interval number, intuitionistic fuzzy number and hesitant fuzzy linguistic value, and develops an MCGDM model based on the distance-based VIKOR to handle the multi-criteria heterogeneous information in practice. This paper also introduces a parameter called non-fuzzy degree for each type of heterogeneous value to moderate the computation on aggregating heterogeneous hybrid distances.

The proposed distance-based model can handle the heterogeneous information appropriately and flexibly because the computational process is directly operated on the heterogeneous information based on generalized distance without a transformation process, which can improve the decision-making efficiency and reduce information loss. An example of emergency supplier selection is given to illustrate the proposed method.

This paper develops an MCGDM model based on the distance-based VIKOR to handle heterogeneous information appropriately and flexibly. In emergency supplier selection situations, the proposed decision-making model allows the decision-makers to express their judgments on criteria in their appropriate value types.

The purpose of this paper is to create a numerical technique to tackle the challenge of selecting software reliability growth models (SRGMs).

A real-time case study with five SRGMs tested against a set of four selection indexes were utilised to show the functionality of TOPSIS approach. As a result of the current research, rating of the different SRGMs is generated based on their comparative closeness.

An innovative approach has been developed to generate the current SRGMs selection under TOPSIS environment by blending the entropy technique and the distance-based approach.

In any multi-criteria decision-making process, ambiguity is a crucial issue. To deal with the uncertain environment of decision-making, various devices and methodologies have been explained. Pythagorean fuzzy sets (PFSs) are perhaps the most contemporary device for dealing with ambiguity. This article addresses novel tangent distance-entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and authenticity of the suggested measures.

The present study aims to demonstrate the application of newly developed combinative distance-based assessment (CODAS) approach for grading and selection of Tossa jute fibres, which possesses some unique features uncommon to other variants of multi-criteria decision-making (MCDM) method.

The CODAS method was used in this study to rank/grade ten candidate lots of Tossa fibres on the basis of six apposite jute fibre properties, namely, fibre defect, root content, fineness, strength, colour and density. These six fibre properties were considered as the six decision criteria, here, and they were assigned weights as determined previously by an earlier researcher using analytic hierarchy process. The grading of jute fibres was done based on a comprehensive single index known as the assessment scores (Hi), in descending order of magnitude.

Among the 10 Tossa jute lots, T2 was ranked 1 (top grade) because of the highest assessment score of 6.887, followed by T1 with Rank 2 (assessment score 1.830). Because of the least assessment score of −2.795, the candidate lot T4 was considered as the worst, and hence ranked 10. The overall ranking pattern given by the CODAS method was similar to the TOPSIS approach done by Ghosh and Das (2013). This study was supported by various sensitivity analyses to judge the efficacy of the present approach. No occurrence of rank reversal during the sensitivity analyses obviously corroborates the robustness and stability of the CODAS method.

Jute pricing is fixed solely by the quality for which grading of fibre is prerequisite. The traditional “Hand and Eye” method or Bureau of Indian Standards (BIS) system for jute grading is basically subjective assessment and need domain expertise. MCDM is reported as the most viable solution which gives due importance to the fibre parameters while grading the fibres based on a single index. The present study demonstrates the maiden application of CODAS to address the fibre grading problems for jute industries. This approach can also be extended to solve other decision problems of textile industry, in general.

CODAS is a recently developed exponent of MCDM. Uniqueness of the present study lies in the fact that this is the first ever application of CODAS in the domain of textile industry, in general, and jute industry, in particular. CODAS approach is very simple involving a few simple mathematical equations yet a potent tool of decision-making. This method possesses some features uncommon in other variants of MCDM. Moreover, the efficacy of CODAS method is investigated through various sensitivity analyses, which has been ignored in the earlier approaches.

Due to liberalization, privatization and globalization, the need of competent technical manpower at an economical cost is increasing rapidly. Many foreign multinationals are focusing on India for employable talents. Many technical institutions with cutting edge technologies and leading edge techniques are being set up by foreign collaboration, national and private initiatives. The objective of this study is to propose a model for performance evaluation and benchmarking of Indian technical institutions from perspective of all stakeholders.

For the proposed framework, a multiple criteria decision‐making tool, distance‐based approach (DBA) methodology is applied for performance evaluation of seven Indian technical institutions taking into account some selected criteria like, faculty strength (FS), student intake (SI), number of PhD awarded (PhD), number of patents applied for (patent), the campus area in acres (CA) and tuition fee per semester in rupees (TF). Consulting the experts in various fields with the help of certain questionnaire and aggregating their views by conducting ameliorated nominal group technique session, we select these evaluation criteria. The subjective weights of the criteria are determined using analytic hierarchy process (AHP). For the analysis, the required data are collected from annual report published by Ministry of Human Resource and Development (MHRD) for the year of 2007‐08.

In this paper, we have chosen seven centrally funded technical institutions for study and the institutions are coded as A, B, C, D, E, F and G. The result of the study reveals that A is the best and F is the worst. The ranking we get is in the order of A≽B≽E≽C≽G≽D≽F. From the result it is understood that A can be considered as benchmark for B, C and E (which form the second group) and this second group can be considered as an improvement target for the rest. At the end a holistic technical education system model (HTESM) is proposed.

This paper is one of the few studies that evaluate the performance of technical institutions in India. The novelty in the approach is that DBA and AHP are being used as a benchmarking technique in a simple methodology which is generic in nature.

Mathematical modeling of innovation diffusion is a constantly evolving field within marketing science. The diffusion process explains the dispersion of an innovation among potential buyers. Prior research on innovation diffusion has been based on modeling varied aspects of real life situations in marketing. One such aspect is studying the adoption process depending on the awareness and motivation level among the customers. Awareness is having knowledge of an innovation, whereas motivation is about the perception of an individual. In line with these aspects, the purpose of this paper is to propose a unified modeling framework for the adoption process based on the awareness and motivation about the product.

When the market is well informed about the product, there are some people who are motivated and some, who have adopted the product earlier and shall now influence others in their buying behavior. It is very much similar to queuing system in which some units are waiting in a queue for the service, service for some units are being processed and some units have already been served. This analogous behavior between two approaches has motivated the use of infinite server queuing theory in modeling adoption of the product. Thereafter, the authors have proposed a unification scheme to model different market scenarios.

From analyzing the values of comparison criteria, it was not clear that which among them is performing best. Thus there was a need for an approach which can judiciously find the optimal model. For this very purpose the authors applied distance-based approach which was capable of computing the optimal model based on the distance of attribute value from the optimal. The analysis performed on two real life sales data sets depict that model in which awareness is following logistic pattern and motivation and adoption are following a constant pattern is ranked one.

The idea has been validated on product. It would be interesting to know how the methodology works on service.

The modeling framework discussed in this paper can be helpful to know from the available set of alternative, which among them is performing better in capturing the spread of the product in the market. The proposed framework offer some managerial guidance by highlighting the unusual aspects of diffusion process and also present an approach to judge the best among a set of different models.

Recently, much research about the effects of simulation on an organization’s operational performance and efficiency has been carried out. But still, there is a need for lean-based simulations rather than just modelling the system. However, these studies still lack systematization. Consequently, the purpose of this paper is to systematize the literature on lean simulations in the area of production and operations management.

A systematic analysis was performed on 93 articles extracted from the Clarivate Analytics Web of Science (WoS) Core Collection database. The analysis was limited to only articles that come under the “Engineering industrial,” “Engineering Manufacturing” and “Operations Research and Management” subject category in WoS Core Collection database. Later, a bibliometric analysis was integrated with the literature review for deeper literature categorization.

A bibliometric analysis displayed the relevance of selected literature (keywords, authors and sources). The systematic literature review helped to clarify each clusters’ content and purpose.

This study portrays the gaps in the lean simulation integration methods and techniques through a detailed systematic and bibliometric review.

Lean managers can get various insights and ideas on implementing lean and simulation integration methods in real-time process improvement scenarios.

This study demonstrates the best future recommendations for lean based modelling for both consumers and organizations that shall address the social and economic issues.

To the best of the authors’ knowledge, minimal attention has been paid to systematizing the literature on lean simulation in industrial, manufacturing and operations management. This study contributes to close this gap.

In recent years, much has been discussed about new consumer practices based on the sharing economy. In this context, the purpose of this paper is to map out the international scientific production on sharing economy.

The research adopted a descriptive qualitative approach. Based on a sample of 95 documents collected in the Scopus database, analyses of bibliometric and sociometric indicators were carried out, as well as content analyses were conducted to identify the main thematic categories in the field.

The results show that sharing economy is an emerging topic, and of late, the research in this field has grown rapidly. The study provides a mapping of top journals and authors, works of greatest impact and of co-authorship, co-citation and bibliographic coupling networks, which evidence the low intensity of researcher’s interactions and scientific production dispersion in the field. The main subjects found in the sharing economy literature are determinants, motivations and barriers, sharing economy impacts, regulation, models and frameworks, critical approach and entrepreneurship and sharing-based new businesses.

The analyses did not take into account the timing perspective. Further research could undertake a timeline-based approach in order to present direct citation networks and to relate works according to the year when they were published.

The study innovates by identifying the main subjects in the sharing economy literature, as well as by presenting network analysis for some bibliometric indicators, complementing previous research in the field.

The purpose of this study is to present Industry 4.0 technologies for advancing the circular economy (CE) adaption in manufacturing industry’s supply chain (SC) network. To pursue the same, Industry 4.0 technological aspects were recognized as solution measures to overcome the challenges for CE implementation in SC.

A new hierarchical framework containing 13 leading CE challenges and eight promising Industry 4.0 technological aspects had been proposed, representing their mutual relationship. The proposed framework was analysed using a hybrid approach of analytic hierarchy process (AHP) and combinative distance-based assessment (CODAS) under interval-valued intuitionistic fuzzy (IVIF) environment. The IVIF-AHP was used to acquire the priority weights of the CE challenges, whereas the IVIF-CODAS was used to attain the preference order of the proposed technological aspects.

The key findings of the present work indicate that “Information disruptions among the SC members due to multiple channels” and “Manpower inability to handle the toxic materials” are the two most critical challenges hindering the adoption of CE practices in SC. Along with, the results also demonstrate that to overcome these challenges, “Smarter equipment to empower flexibility and mass customization” and “Big data driven decision-making system” are the two most significant Industry 4.0 technological solutions, adoption of which might encourage the organizations to align their operations with CE philosophies.

The sample size of the experts engaged in work was limited; however, big data studies could be conducted in future to capture more insights of the stated topic. In addition to this, to understand the implication of CE on Industry 4.0-based manufacturing, a separate study can be synthesised in future.

The proposed work facilitates a new framework consolidating various perspectives associated with CE implementation into a manufacturing industry considering the scenario of Indian rubber industry. This study enables the decision-makers to recognize the challenging factors for CE implementation into their organizations and up-taking the proposed Industry 4.0 practices as technological measures for improving the organization overall performance.

This paper aims to provide a transaction cost analysis (TCA) perspective to exporting firms' selection of foreign markets and the performance consequences of this international market selection (IMS) decision. This paper proposes a conceptual framework that hypothesizes the relationship between transaction cost factors, IMS and export performance.

This paper tests the proposed framework with a database of Chinese manufacturing firms using regression models and controlling for possible endogeneity. The endogeneity issue may arise due to IMS being influenced by unobserved industrial/firm attributes.

The results show that transaction cost factors are able to explain IMS. Furthermore, firms whose decisions have incorporated transaction cost factors perform significantly better than their rivals.

Understanding transaction costs helps decision-makers formulate more efficient IMS strategy to achieve superior export performance. Future research on IMS may examine “passive exporting”, i.e. exporting initiated by overseas buyers, consider the role of institutional distance and use other approaches toward cultural distance-based IMS.

This study adds a new theoretical underpinning for IMS by developing a framework based on TCA, and thus broadens the applications of TCA into IMS. Our empirical results support this extension.