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This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision-makers aim to design an efficient assembly line while satisfying a set of constraints.

An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP to optimize the number of stations and the workload smoothness.

To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.

The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is used in the IGA to enhance its local search capability.

The purpose of this study is to first investigate the efficiency of the most commonly used performance measures for minimizing the number of workstations (NWs) in approaches addressing simple assembly line balancing problem (SALBP) for both straight and U-shaped line, and second to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1.

A total of 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics.

Minimizing the smoothness index is not necessarily equivalent to minimizing the NWs; therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency and the idle time are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced.

Results are expected to help scholars and industrial practitioners to better design effective solution methods for having the most balanced assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration.

There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.

This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.

In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.

Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.

The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.

To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

The purpose of this paper is to demonstrate how small manufacturing firms can leverage their Information Technology (IT) resources to develop the lean-digitized manufacturing system that offers sustained competitiveness in the Industry 4.0 era.

The study performs an in-depth five years case study of a manufacturing firm, and reports its journey from failure in the implementation of enterprise resource planning to its success in integrating IT-based technology trends of Industry 4.0 with the firm’s core capabilities and competencies while pursuing manufacturing digitization.

Industry 4.0 transition requires the organizational integration of many IT-based modern technologies and the digitization of entire value chains. However, Industry 4.0 transition for smaller manufacturers can begin with digitization of certain areas of operations in support of organizational core strategies. The development of lean-digitized manufacturing system is a viable business strategy for corporate survivability in the Industry 4.0 setting.

Although the implementation of lean-digitized manufacturing system is costly and challenging, this manufacturing strategy offers superior corporate competitiveness in the long run. Since this finding is rather limited to the present case study, assessing the business value of lean-digitized manufacturing system in a larger scale research context would be an interesting avenue for future research.

Industry 4.0 transition for typical manufacturers should commensurate with their organizational, operational and technical particularities. Digitization of certain operations and processes, when aligned with the firm’s core strategies, capabilities and procedures, can offer superior competitiveness even in Industry 4.0 era, meaning that the strategic plan for successful Industry 4.0 transition is idiosyncratic to each particular manufacturer.

Manufacturing digitization can have deep social implications as it alters inter- and intra-organizational relationships, causes unemployment among low-skilled workforce, and raises data security and privacy concerns. Manufacturers should take responsibility for their digitization process and steer it in a direction that simultaneously safeguards economic, social and environmental sustainability.

The strategic roadmap devised and employed by the case company for managing its digitization process can better reveal what manufacturing digitization, mandated by Industry 4.0, might require of typical manufacturers, and further enable them to better facilitate their digital transformation process.

The purpose of this paper is to contribute to the existing knowledge about the relationships between information technology (IT), lean manufacturing (LM), organizational environmental issues and business performance.

A questionnaire-based survey was conducted to collect data from 122 elite manufacturers, and the hypothesized relationships were tested using partial least squares structural equation modeling.

IT competence in LM acts as a lower-order organizational capability, and its business value should be recognized through the intermediate roles of LM effectiveness and environmental management capability. Findings recommend that the net benefits of LM are mainly materialized through waste and pollution reduction and simplified implementation of proactive environmental practices.

Among other limitations, relying on a rather small sample size and cross-sectional data of this research, and lack of generalizability of findings, tends to have certain limitations. An interesting direction for future research would be to extend this research by assessing interaction of other types of IT resources with LM and organizational environmental issues.

Both LM and proactive environmental management are information-intensive. Investment in both technological and human aspects of IT resource aimed at increasing the effectiveness of LM activities and proactive environmental practices is imperative for contemporary manufacturers.

This study introduces the IT capability of IT competence in LM and two organizational capabilities of LM effectiveness and environmental management capability. By doing so, the study highlights the significant role of organizational environmental issues in devising firms’ IT and advanced manufacturing technology investment strategies in LM context.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

The purpose of this study is to contribute to the existing knowledge about the process of achieving Lean Manufacturing (LM) success.

This study uses interpretive structural modeling and captures the opinions of a group of LM experts from a world-class Japanese automobile manufacturer, to map the interrelationships among potential determinants of LM success. This study further uses the data from a survey of 122 leading automobile part manufacturers by performing structural equation modeling to empirically test the research model proposed.

Management support and commitment, financial resources availability, information technology competence for LM, human resources management, production process simplicity, supportive culture and supply chain-wide integration are the key determinants that directly or indirectly determine the level of achievement of LM success.

The determinants of LM success as experienced by Asian automobile manufacturers might be different from determinants of LM success as experienced by Western automobile manufacturers. An interesting direction for future research would be to capture the experts’ inputs from Western automobile manufacturers to complement the findings of this study.

The practical contribution of this study lays in the development of linkages among various LM success determinants. Utility of the proposed interpretive structural modeling and structural equation modeling methodologies imposing order, direction and significance of the relationships among elements of LM success assumes considerable value to the decision-makers and LM practitioners.

Building on opinions of a group of LM experts and a case study of leading auto part manufacturers, the present study strives to model the success of LM, a topic that has received little attention to date.

This study examines the importance of English Mobile Learning research as a foundation for lifelong and sustainable education from different points of view, including those of technology innovation experts, psychologists and educators. It aims to explore the current status and relevant research trends through the application of bibliometric mapping and bibliometric analysis.

For this study, all Web of Science records (in total 5,343) from 2000 to 2020 in the field of English Mobile Learning were analyzed using the VOSviewer and CiteSpace software tools. The WoS built-in functions, including “Refine” and “Analyze,” were employed to perform the bibliometric analysis. The study further analyzed a sample of the five most-cited articles to identify the previous studies with the highest quality or impact.

The results showed that research in English Mobile Learning is growing quickly and steadily with a noticeable emphasis on various device-based technologies and applications. The study also discusses the key implications for research institutions, education policymakers and academicians, and identifies the most prominent avenues for future research on English Mobile Learning. Moreover, the results shared in this review highlight the most important and emerging areas of research in the field.

This article is the most recent bibliographic review of literature that particularly addresses the English Mobile Learning research during the past two decades.

The present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to empower smaller businesses to embrace Industry 4.0.

The study conducted a systematic review of the literature and drew on the technology-organization-environment framework to identify various technological, organizational and environmental determinants of Industry 4.0 technology adoption and their underlying components. The study applied the textual narrative synthesis to extract findings from the eligible articles and interpret them into the Industry 4.0 technology adoption roadmap.

Industry 4.0 is a vital strategic option to SMEs, enabling them to keep up with the digitalization race. SMEs significantly lag behind large organizations in benefiting from disruptive Industry 4.0 technologies. SMEs are still struggling with the initial adoption decisions regarding the digital transformation under Industry 4.0. Results identified various determinants that might explain this condition. The study developed a digitalization roadmap that describes the necessary conditions for facilitating SMEs’ digitalization under Industry 4.0.

Various technological, organizational and environmental factors might determine the current positioning of SMEs against Industry 4.0. These determinants can act as barriers or drivers depending on their properties. The roadmap describes determinants indispensable to promoting Industry 4.0 technology adoption among SMEs, such as knowledge competencies or value chain digitalization readiness.

Exclusively focusing on empirical research that reported applied insights into Industry 4.0 technology adoption, the study offers unique implications for promoting Industry 4.0 digital transformation among SMEs.