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Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

This study aims to portray the growth–energy–environment relationship in Luxembourg by performing a yearly data covering the years from 1970 to 2021.

Economic growth, population growth and disaggregated energy use into renewable and non-renewable sources are employed to investigate the impact of each on environmental quality as measured by CO2 emissions. ARDL Bounds co-integration test was established to explore the long run relationship among the variables and the ARDL approach was employed to estimate the long run impact of the determinants on CO2 emissions. The results validated the presence of the EKC in Luxembourg. Moreover, the causal link between variables is investigated employing a novel approach to time-varying causality which was recently developed by Shi et al. (2018, 2020) highlighting the main contribution of this study.

Overall results of the study lead to conclude that economic growth and sustainable environmental quality can both coexist if feasible anti-pollution laws are enacted, fossil fuels are reduced and further research is made to increase the capacity of renewable energy sources and raise their percentage contribution to energy supply. The time-varying causality test results verify that causal link between environmental degradation and its main determinants is apparent over different time spans. Thus, time-varying causality test results suggest to design dynamic policies to improve environmental quality in Luxembourg.

This study contributes to the literature since the use of the variables is the first of its kind for a time-series empirical analysis on Luxembourg as the study seeks to investigate the growth–energy–environment relationship in the country. Furthermore, to the best of the author’s knowledge, there is no previous study investigating the time-varying causality link between environmental degradation and its main determinants by the application of the test developed by Shi et al. (2018, 2020) considering the indicators focused in this study and for the case of Luxembourg.

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

Despite high aspirations to pursue personal development, self-sustaining employment, socio-economic integration, and stable futures in their host, origin, or resettlement countries through higher education, intersecting legal, economic, linguistic, and sociocultural barriers severely constrain refugees’ options. There is limited research on how refugee students overcome these barriers to access higher education, particularly in displacement settings like Turkey, which perpetuates a deficit view of these learners. This chapter seeks to address this gap and challenge deficit ideologies through an asset-focused perspective on the stories of 10 Syrian young adults accessing higher education during forced displacement in Turkey using a composite narrative portrait crafted based on common experiences running across the participants’ individual narratives. The narrative illustrates the importance of equitable policies, quality language instruction, inclusive pedagogies, and supportive interpersonal relationships for young people aspiring to invest in their futures during displacement as well as the resourceful and dynamic strategies they devise.

The purpose of this paper is to assist a manufacturer for determining the principal supplier development impediments (SDIs) that are affecting the supplier development programs (SDPs) and yield a basis for drawing the appropriate mitigation strategies.

The proposed approach starts with the application of fuzzy analytic hierarchy process and Pareto analysis to obtain the principal SDIs. Subsequently, the interpretive structural modeling and Fuzzy-Matrice d’Impacts Croisés-Multiplication Appliquée à un Classement analysis are applied on the principal SDIs to explore the root causes inducing ineffectiveness in the SDPs.

The outcomes of the proposed approach were demonstrated through a case study conducted in an Indian automotive components manufacturing company. The principal SDIs were identified, ranked, classified and structurally related for the said case company’s manufacturing environment. It was found that the lack of competent workforce, level of nascent relationship between manufacturer and suppliers and poor devolution of authority were the main SDIs that the case company has to primarily focus to make its SDPs effective.

Although the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other case situations. However, the proposed approach can be applied for analyzing the SDIs in any manufacturing environment once the set of SDIs relevant to that environment are carefully chosen.

The methodology would assist a manufacturer to proactively identify the SDIs that it has to primarily focus and subsequently devise the mitigation strategies for smooth running of its SDPs.

By utilizing the identified SDIs and applying the proposed methodology a manufacturer can find the main SDIs that are making the SDPs ineffective and accordingly fix the hindrances through certain mitigation strategies designed.

The Covid-19 pandemic spread rapidly around the world and required strict restriction plans and policies. In most countries around the world, the outbreak of the disease has been serious and has greatly affected the health system and the economy. The factors such as the number of patients with chronic diseases, the number of people over 65 years old, hospital facilities, the number of confirmed Covid-19 cases, the recovering Covid-19 cases and the number of deaths affect the rate of spread of Covid-19. This study aims to evaluate the performances of 21 Organisation for Economic Co-operation and Development (OECD) countries against the Covid-19 outbreak using three data envelopment analysis (DEA) models.

In this study, the performance of 21 OECD countries to manage the Covid-19 process has been analysed weekly via DEA which is widely used in various practical problems and provides a general framework for efficiency evaluation problems using the inputs and outputs of decision-making units.

The analysis showed that 11 countries out of 21 countries were efficient for selected weeks. According to the DEA results from the 20-week review (09 April 2020–20 August 2020), information about the course of the epidemic prevention and the normalization process for any country can be obtained.

In this study, due to the problem of the discrimination power of DEA, the cross-efficiency model and the super-efficiency model also used. In addition, the output-oriented model was preferred in this study for Covid-19 management efficiency.

The purpose of this article is to examine all the studies carried out within the scope of e-HRM and smart HRM, grouped according to the sub-functions of technical and HRM. The use of technology in HRM has started since the mid-1990s. However, this study focused on the articles published after 2014 in order to keep up to date. Any search strategy should allow for the completeness of the search to be evaluated. The terms “electronic-HRM”, “AI and HRM”, “Industry 4.0 and HRM”, “Society 5.0 and HRM”, “Human Resource Information Systems” and “Digital Technologies and HRM” “Human-robot interaction” has been questioned in IEEE Xplore, ALM digital library, Emerald Insight, SpringerLink, and Science Direct. The Web of Science and Scopus were also queried to double-check the findings and find other relevant articles in lesser-known libraries. Google Scholar was also used for forward and backward searches. These online databases have been chosen because they present the most important peer-reviewed full-text journals, conference proceedings, book chapters. Then, the references of each article were reviewed for additional articles on digital technologies and HRM. Each subsequent article is then reviewed for additional reference.

A total of 5,580 articles have been reviewed. Duplicate items have been removed. The titles and abstracts of 3,500 articles have been scanned to identify potential articles. The full-text evaluation of 2,554 was based on compliance with the inclusion criteria. In addition, 2,458 studies have been excluded. In total, 96 studies have been selected for data extraction. Additionally, questionnaires and reviews have been used to provide comprehensive research on e-HRM and smart HRM. The search terms used are expected to cover most, if not all, of the studies involving e-HRM and smart HRM.

The study carried out in this article is qualitative research. In the article, which methods are used and what has changed in e-HRM and smart HRM are examined. In particular, it has been thought about what can happen with the inclusion of human-machine interaction, AI, chatbots, industry 4.0 and information systems in HRM. Unlike previous studies, this review takes HRM from a broader perspective and groups it by topic, both by technical and HR functions. In addition, the reviewed articles provide brief information about the AI technologies used. In particular, criteria were taken into account according to the field, type and subject of the articles.

This study has the distinction of being the first in the literature in terms of examining all the studies carried out within the scope of e-HRM and smart HRM and grouped according to the sub-functions of technical and HRM in line with its purpose. The article focuses specifically on research published after 2014. It is expected to contribute to the literature in terms of collecting all studies in a single article. Other contributions of this article can be summarized in four main articles: 1) it presents a summary of previous research by grouping the studies on e-HRM and smart HRM according to the interests of researchers. 2) It saves time for the reader as it provides a brief explanation of the studies on the subject. 3) Instead of explaining in detail the general details analyzed in other articles, it offers a practical perspective by focusing on the type, subject and field of the article. 4) With the digitalization of HRM, new, up-to-date research and techniques are introduced.

The advancement of technology and increased sales of service robots have led to discussions about their usage. Recently, the use of service robots in accommodation businesses has become a topic of examination. This study aims to explore the behavioural intentions of accommodation business managers towards using service robots from the perspective of the technology acceptance model. As part of this project-supported study, the ‘Sanbot Elf’ service robot was rented for 14 days and demonstrated in six accommodation businesses in Kastamonu. Semi-structured interviews were conducted with managers and unit supervisors after the demonstration, and the process was also monitored through participant observation. The data were analysed using content analysis and participant observation methods. It was determined that participants' opinions on service robots were generally positive. Participants stated that personalised service presentations could be provided with service robots and that these robots could be used in different departments and marketing processes. The study concluded that service robots could positively impact service quality, are an exciting technology for accommodation businesses and the use of service robots could increase in the future. Participants' attitudes towards using service robots in accommodation businesses were positive in this context.