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The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

The purpose of this paper is twofold: to identify and map contemporary research on advanced technology implementations for problem-solving purposes in the manufacturing industry, and to further understand the organizational learning possibilities of advanced technology problem-solving in the manufacturing industry.

This paper outlines a scoping review of contemporary research on the subject. The findings of the review are discussed in the light of theories of contradicting learning logics.

This paper shows that contemporary research on the subject is characterized by technological determinism and strong solution-focus. A discussion on the manufacturing industries’ contextual reasons for this in relation to contradicting learning logics shows that a Mode-2 problem-solving approach could facilitate further learning and expand knowledge on advanced technology problem-solving in the manufacturing industry. A research agenda with six propositions is provided.

The introduction of advanced technology implies complex effects on the manufacturing industry in general, while previous research shows a clear focus on technological aspects of this transformation. This paper provides value by providing novel knowledge on the relationship between advanced technology, problem-solving and organizational learning in the manufacturing industry.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

Social entrepreneurs engage in collective action to adapt and solve social problems in complex environments. Through collective action, they mobilise and access resources to create positive social change in local communities. While previous studies explain the role of social identity in promoting cooperation, this paper aims to examine shared meaning as a predictor of collective action in social entrepreneurial ventures (SEVs). This study was conducted among founders of SEVs focusing on their engagement in collective action.

The study adopted a cross-sectional survey to achieve its objectives. The population comprised 558 SEVs registered with the Kampala Capital City Authority in Uganda. A sample size of 226 social ventures was determined using Krecjie and Morgan and participants were selected using the simple random sampling technique. The questionnaires were distributed by two research assistants, and 210 completed questionnaires were returned. Structural equation modelling was used to analyse survey data and test the study hypotheses.

The study findings show that shared meaning in the form of teamwork and group efficacy predict collective action in SEVs.

The results have implications for social entrepreneurship researchers, practitioners and policymakers. Firstly, creating social ties by belonging to different groups in the community creates common understanding among social entrepreneurs and other actors fostering cooperation to solve problems in the local community. Secondly, understanding each other’s perspective well enough facilitates a shared view of social problems for combined action.

SEVs should provide relevant information using the right channels in local communities to promote collaboration. Failure to use the right communication channels may prevent collective action. Managers in social ventures should allow for open information sharing between themselves and the partners that they work with to address social problems. This enables them to share both the good and bad feedback. It also enables the growth of teams and improves how they work. The teams should be developed with specific responsibilities so that everyone is clear on what they should do while addressing social problems.

The researchers argue that shared meaning develops when social entrepreneurs interact with the local community and other stakeholders prompting joint action to address social problems. This study extends knowledge on collective action using the activity domain theory.

This paper aims to examine the impact of problem-solving activities, emotional experiences and contextual and personal factors on learning from dealing with software-related problems in everyday office work.

To measure the use of problem-solving activities, emotional experiences and the contextual factors of problem characteristics and learning in situ, a research diary was used. To measure team psychological safety (contextual factor) and personal factors, including the Big Five personality traits, occupational self-efficacy and technology self-efficacy, the authors administered a self-report questionnaire. In sum, 48 students from a software company in Germany recorded 240 diary entries during five working days. The data was analysed using multilevel analysis.

Results revealed that asking others and using information from the internet are positive predictors of self-perceived learning from a software-related problem, while experimenting, which was the most common activity, had a negative effect on learning. Guilt about the problem was positively related to learning while working in the office (as opposed to remote work), and feeling irritated/annoyed/angry showed a negative effect. Surprisingly, psychological safety had a negative effect on perceived learning.

Major limitations of the study concern the convenience sample and the disregard for the sequence of the activities.

This study contributes to the limited empirical evidence on employees’ problem-solving activities and informal workplace learning in the software context. To overcome the shortcomings of previous studies using retrospective assessments and in-lab observations, this study uses the diary method to investigate in situ.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

The main operations of the powder-coating process are staggered along a closed-loop conveyor. Given the volatile market demands, using a fixed level of staffing may result in significant productivity losses. The present study aims to capture stochastic behavior and optimize operator assignment problems in a practical powder-coating process. By using the proposed methodology, when demand changes, the optimal operator assignment configuration can be provided, ensuring high labor productivity.

The powder-coating process is an important industrial application and is often a labor-intensive system. The present study adopts a practical case to optimize its staffing level. Because of its operational complexity, the problem is solved by a proposed simulation-optimization approach. The results are promising, and the proposed methodology is shown to be an effective approach.

The proposed methodology was tested for various demand levels. The optimized operator assignment configuration always improves on the performance of other staffing levels. Given the same daily throughput, the optimized operator assignment configuration can improve performance by as much as 19%. In scenarios where there is increased demand, the resulting reduction in overtime work improves performance by between 20.33% and 56.72%. In scenarios where there is reduced demand, the optimized staffing level produces improvements between 3.13% and 50%. Compared with the fixed staffing policy of the case company, the flexible staffing policy of the proposed methodology can maintain high labor productivity across demand variations. The results are consistent with the Shojinka philosophy of the Toyota Production System.

This study proposes a solution to the operator assignment decision in a labor-intensive manufacturing system – a powder-coating processing system. Powder coating provides a solid powder coating without any solvent. Because of its excellent application performance and environmental protection, it is widely used in the field of metal coating, especially appliances for offices and homes. Most of the existing literature has solved the problem by making unrealistic assumptions. The present study proposes a simulation-optimization method to solve a practical problem in powder-coating processing. The effectiveness of the proposed methodology is illustrated by a practical application. According to the experimental results, five operators can be saved for the same daily throughput. An average of 35 and 19 min of overtimes can be saved when demand increases by 10% and 20% with one less operator; between 2 and 16 operators can be saved when demand falls by 10%–60%.

The purpose of this article is to phenomenologically explore the reflective practices of leaders in public organisations amidst a complex societal context in combination with rapid changes. In this article, the authors specifically explore the lived experiences of public leaders to generate new hypotheses concerning their reflective practices.

The phenomenological methodology consists of analysing the lived experiences of 13 public leaders, collected in an in-depth interview and written reflections.

The thick data offer new and up-to-date insights into the daily experiences of public leaders concerning their challenges, the effect of the addictive and alienating forces, their reflex to withdrawal when facing emotional incidents and the effects of their contradictory mindsets.

The practical implication is a critical approach towards reflective practices of public leaders. The risk is that reflectivity is approached as a socially desirable instrumental ritual. Considering the needs and desires the public leaders shared, the authors wonder: Is there a growing importance of reflective time and space – or, above all, meaningful relations and resonant moments amidst the alienation forces?

The phenomenological exploration offers concrete insights into the daily experience of public leaders', as opposed to the often-abstract theory. The new hypotheses provide a new starting point for further critical phenomenological research.

This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe.

After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged.

Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures – institutions and technology –and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them.

The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.