Search results

The vehicle routing problem (VRP) is critical for the successful execution of logistics activities. However, there is strong evidence that efficiently solving the VRP is often complicated and requires more powerful – and possibly intelligent – support tools. In accordance with this necessity, the present study proposes a decision support system (DSS) applicable to the VRP, which includes both initial planning and replanning phases to support the real-time operations.

The proposed DSS lies at the basis of resilience thinking to provide a capacity to absorb and withstand the impact of disruptions, where resilience is connected with the factors of preparedness, flexibility and redundancy. These factors are approached in this study through a number of operational strategies in the reactive and proactive modes. The DSS includes a multi-layer perceptron neural network to predict changes that may arise in dynamic contexts, a modified k-means clustering algorithm to group customers with both static and dynamic attributes and two mixed-integer programming models to produce primary and alternate routing plans.

The research is motivated by the operational challenges faced by a collaborative networked clinical laboratory, which seeks to enhance efficiency and productivity in the daily management of medical sample collection and delivery through the implementation of increased automation. The findings reveal that centralized planning leads to heightened vulnerability in route planning and increased costs for replanning. Furthermore, the effectiveness of resilience-enhancement strategies varies based on the source and level of uncertainty.

The contributions of this paper are as follows: incorporating resilience thinking into the operational planning of logistics services, managing the decision-making of transport and collection companies through a DSS framework to ensure proper support to real-time operations, addressing the clustered VRP in a dynamic setting and adopting forecasting approaches to cover possible sources of dynamism.

The objectives of this study are as follows: RO1: to determine the key enablers that encourage the adoption of sustainable practices; RO2: to create a structural relationship model between the enablers that have been discovered and RO3: to use dependence and driving power to classify and analyse these factors.

In total, 11 variables were extracted through systematic literature review and experts opinion, and a group interview with more than 15 experts who have an experience of more than 10 years in academics was conducted. Generally odd in numbers, they presented each pair of variables and took their opinion. Using “Interpretative Structural Modelling” and “Cross-Impact Multiplication Applied to Classification” (ISM MICMAC) analysis, a model describing the structural relationship between these factors has been formulated to know the conceptual framework.

The study shows that the digital payment and waste treatment as the linkage or mediating variables. Top management commitment (TMC), regulatory framework and transparency are the drivers or independent variables. Green logistics, sustainable packaging and production are the dependent variables. Further, the resulting hierarchy and contextual linkages among variables shed light on key dimensions for decision-making. Green logistics, sustainable packaging and production are interconnected, playing a significant role, in the framework and influenced by the lower level variables. Transparency should be given due consideration to ensure sustainable competitive advantage.

(1) Owing to a lack of time and resources, the researchers have been able to explore only limited variables; more factors or enablers can be taken in future for further research. (2) Money constraint is also there. (3) One major limitation is that this research includes opinion from different experts which are dynamic in nature and keep on changing from time to time. (4) Primary research can also be done with the help of different software like partial least squares and structural equation modelling (PLS-SEM), covariance-based structural equation modelling (CB-SEM) and Statistical Package for the Social Sciences (SPSS) by taking same variables or merging new variables with the given variables of this paper. (5) With the help of this paper, the author tried to give an overview of some important enablers which are helpful in sustainability. Given a time in future, more research can be done by taking different sectors or a specific sector.

Findings are helpful for policymakers about effective strategies for promoting sustainability. They provide evidence-based insights into the potential impact of policy interventions and help shape the development of environmental regulations and initiatives. Sustainability is a global issue. Thus, this research paper contributes to international discussions and collaborations on environmental conservation and sustainable development goals (SDGs). They may influence diplomatic efforts to address transnational environmental challenges. Companies can use research findings to improve their sustainability practices and develop environmentally friendly products and services. Understanding sustainability enablers can help businesses reduce their ecological footprint while remaining economically viable. Research on sustainability enablers often involves exploring new technologies and innovations. Practical implications include the development and adoption of renewable energy sources, waste reduction techniques and sustainable agricultural practices. Sustainable communities rely on a range of enablers, from renewable energy infrastructure to local governance structures. Research informs community development efforts by identifying best practices and effective strategies for building resilient, environmentally friendly neighbourhood.

This research paper contributes to raising awareness about sustainability issues among the public. It also provides insights into the importance of sustainable practices and their impacts on society. Sustainability enablers can influence individual and collective behaviour, and they may encourage people to adopt more sustainable lifestyles and consumption habits. Sustainability research often addresses issues of equity and social justice. Understanding the enablers of sustainability can help identify strategies to ensure that sustainable practices benefit all segments of society, including marginalized communities.

This research paper endeavour to identify key sustainability development enablers (SDEs) by consolidating numerous factors within a single study. While several research papers have explored various sustainability enablers separately, no previous study has delved into their interactions. Furthermore, there is a dearth of research on classifying enablers based on the fuzzy MICMAC analysis. The ISM-based model presented here serves as a valuable resource for both practitioners and academics, aiding in their comprehension of the framework of relationships.

This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.

The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.

This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.

This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.

To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

Based on studies combining sustainable development competencies and green consumption values, this paper aims to explore the relationship between Generation Z's sustainable development competencies and green consumption values and to determine how sources of sustainable knowledge acquisition moderate this relationship.

The data collected from 1,570 students were analyzed using structural equation modeling.

Our research confirms that sustainable development competencies positively and significantly impact Generation Z’s attitudes toward green consumption values. Furthermore, sources of sustainability knowledge acquisition moderate the relationship between sustainability literacy and Generation Z attitudes toward green consumption values. The findings suggest that the acquisition of sustainability knowledge from multiple sources is critical in shaping Generation Z attitudes toward green consumption values.

The key to increasing attitudes toward green consumption values among Generation Z is to provide access to sources of sustainable development knowledge acquisition. Adequate information support generated by sources of sustainable development knowledge acquisition can help shape attitudes toward green consumption values that translate into appropriate purchasing decisions.

The survey indicates the significance of diversifying sources of sustainable development knowledge. Although the study received results suggest that Generation Z’s sustainable development competencies influence attitudes toward green consumption values, they allow us to note that much greater effects are generated when a variety of sources of sustainable development knowledge acquisition are used. Thus, it is desirable not only to communicate about sustainable development but also to tailor the sources of sustainable development knowledge acquisition to the audience’s expectations are desirable.

This study analyzes the relationships between sustainable development competencies and green consumption attitudes, thus filling the gap in sustainable development competencies research and broadening its scope. The results indicate the need to change attitudes through the development of competencies that should enable active and responsible participation in sustainability. At the same time, the research shows some links between green consumption and purchasing behavior, suggesting the influence of green consumption values and orientations on sustainable and environmentally friendly consumption.

This paper aims to summarize the literature (journal articles and book chapters) on Central and Eastern European (CEE) firms’ outward internationalization failures: definitions and understandings of “failure”, “failed” firms’ internationalization processes, causes of “failed” initial and subsequent foreign activities and consequences of “failed” internationalization.

This systematic-narrative hybrid literature review article focuses on CEE firms’ outward internationalization failures.

The paper demonstrates that different objective and subjective measures were used for defining and measuring “failure”. Consensus regarding which firms (from slow internationalizers to born globals) can be considered “failed” is lacking. In different studies, internal and external causes of CEE firms’ outward internationalization “failure” and internationalization-related and other consequences of “failed” internationalization also vary considerably. Due to the complexity of the “failure” phenomenon, it is impossible to identify the most characteristic type of “failed” internationalization or offer “optimal” advice for avoiding failures.

The author is not aware of any other literature review articles focused on CEE firms’ outward internationalization failures. This article contributes to the (international) business and (international) entrepreneurship literature focused on failures, exits and institutional and other factors affecting them.

Are there smart ways to find heuristics? What are the common principles behind heuristics? We propose an integrative definition of heuristics, based on insights that apply to all heuristics, and put forward meta-heuristics for discovering heuristics.

We employ Herbert Simon’s metaphor that human behavior is shaped by the scissors of the mind and its environment. We present heuristics from different domains and multiple sources, including scholarly literature, practitioner-reports and ancient texts.

Heuristics are simple, actionable principles for behavior that can take different forms, including that of computational algorithms and qualitative rules-of-thumb, cast into proverbs or folk-wisdom. We introduce heuristics for tasks ranging from management to writing and warfare. We report 13 meta-heuristics for discovering new heuristics and identify four principles behind them and all other heuristics: Those principles concern the (1) plurality, (2) correspondence, (3) connectedness of heuristics and environments and (4) the interdisciplinary nature of the scissors’ blades with respect to research fields and methodology.

We take a fresh look at Simon’s scissors-metaphor and employ it to derive an integrative perspective that includes a study of meta-heuristics.

Student open feedback is an essential element to improve the teaching service. Comprehending the feedback collected daily may not be possible especially in a large classroom. There is needed an automated system that processes feedback and helps to recommend focused, precise points to the teacher stating the positives and negatives of a class. Also, the feedback texts are neither going to be grammatically correct nor going to consist only of English. Hence, an automated feedback processing system is essential that processes the mixed-language language text that provides crisp clear insights to the teachers, thus making effective student–teacher interaction.

This research is designed to analyse daily feedback from the students in grammarless English-Tamil mixed feedback and creates a dashboard that displays concise keywords regarding positive and negative aspects of the class. An ML-based system architecture is proposed for processing English-Tamil mixed grammarless feedback texts and validates the same with an experimental prototype and compares the results with other state-of-the-art models. This prototype classifies the text into different categories and provides the concise view with topic modelling techniques. This system is useful in progressive improvement of teaching learning process, subsequently leading to better teaching learning environment.

The proposed web-based architecture is validated with a prototype by comparing the results with other state-of-the-art models. The accuracy of the results is higher (>90%) in the proposed architecture than other models (<60%). The created teacher dashboard is highly recommendable and provides day-to-day recommendation for finetuning teaching and learning process. The web-based dashboard created for teachers enables them to interpret the student feedback with much ease due to the Machine learning algorithms used in implementing the web-based solution.

This system is designed to help the teachers to improve themselves in the teaching learning process with the feedback. The proposed system is a prototype that is initially tested with sample feedback texts obtained in sessions in postgraduate classrooms. The implementation of the prototype and analysis of teacher and student experience are presented as the immediate scope of this research work. This helps the teachers to get an overall view on the best teaching practices and what to improve. This work currently uses Bidirectional Encoder Representations from Transformers (BERT) uncased and in the increase of native language text the system may work with BERT multilingual.

This prototype will be implemented as a web-mobile based application. Students can submit their daily feedback through a mobile app, while teachers will access a dashboard that presents a concise overview generated by the proposed system architecture. The dashboard will also provide trend analysis, highlighting positive and negative aspects of the sessions. The system's effectiveness will be evaluated through a qualitative study, incorporating feedback from teachers and insights from students. This evaluation will help teachers gain a comprehensive understanding of the most effective teaching practices and areas needing improvement, thereby enhancing the teaching-learning process. The web-mobile application aims to Streamline the feedback process, making it easy for students to share their thoughts and for teachers to receive actionable insights. This study offers a clear and concise summary of student feedback and trend analysis from which the teachers can quickly identify patterns and make necessary adjustments to their teaching methods. Ultimately, this approach will foster a more responsive and effective educational environment, supporting continuous improvement and better student–teacher interactions. Further, the proposed system requires lesser technical knowledge and can be used by anyone.

A literature review has identified a critical need for a feedback processing system that functions at short intervals. Such a system is essential for providing teachers with concise, periodic summaries of students' open-ended feedback, which is vital for fostering continuous improvement in the teaching-learning process. The immediate processing of feedback, particularly when it contains English-mixed texts, is crucial for making timely adjustments that enhance both student performance and experience. By swiftly addressing concerns and reinforcing positive feedback, the system will improve student–teacher interactions, provide meaningful insights that contribute to progressive educational growth. This will help implement a feedback system that operates in these short intervals and allows for real-time monitoring and response to students' needs and experiences. Additionally, by highlighting areas of success, teachers can build on effective strategies and practices.

This research paper proposes a system architecture PSFAS: Progressive Student Feedback Analysis System with Multi Level Classification and Clustering that enables effective interaction between the student and the teacher with the findings from feedback and presenting an experimental prototype that can be incorporated into the regular teaching learning process, whether online or offline. It has been found from the literature review, that feedback processing is mostly done in the English language. This work proposes a system architecture that gives higher accuracy than other state-of-the art models for feedback texts having English-mix grammarless sentences.

This study aims to identify the factors influencing the creation of a toxic workplace and to examine how to improve a toxic workplace in the electronics industry.

First, the factors that influence the creation of a toxic work environment were identified by reviewing the research literature. Then, the current status of each identified factor in Iran’s electronics industry was evaluated by developing a questionnaire. Based on the survey data, a relationship map between the factors influencing the toxic workplace was created using the fuzzy cognitive mapping technique. Finally, scenarios were designed to improve the toxic workplace.

The results show that the “workplace bullying” factor is the factor with the highest centrality in relation to other factors. The results of the scenario design indicate the effectiveness of the “unrealistic expectations at work” factor as a scenario trigger.

This study helps reduce the toxic workplace in the organization, which plays an important role in improving the employees’ work performance and the organization’s development.

Industrialized construction has brought about expectations of improved productivity in the construction industry. However, the lack of a commonly accepted definition has created confusion regarding the types of development covered by the industrialized construction umbrella. These inconsistent definitions convoluted the discussion on this phenomenon. This study aims to clarify the definition of industrialized construction through a systematic literature review.

This systematic literature review was conducted according to PRISMA principles. Records were gathered from Scopus and Web of Science. Following the scientometric analysis, content analysis was conducted according to the template analysis approach.

The analysis of 121 articles revealed four main themes related to industrialized construction: 1) the construction concept, 2) construction methodologies, 3) systematization, rationalization and automatization and 4) societal and industrial change processes. Definitions of industrialized construction can be analyzed with seven clusters: 1) prefabrication, 2) standardization, 3) sector, 4) integration, 5) manufacturing practices, 6) technological investment and 7) none. Based on the content analysis, the proposed definition is: industrialized construction is the adoption of practices that minimize project-specific work in construction from the start of the design to the end of the building’s life cycle.

This study proposes a definition for industrialized construction following content analysis of broadly sampled literature. The proposed definition can provide a basis on which developments in the construction industry can be reflected.