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The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

The purpose of this study was to examine if team composition based on adaption-innovation (A-I) problem-solving styles is related to the teamwork quality and team effectiveness (TE) of student project teams participating in a [state-gifted program (SGP)].

A correlational design was conducted with a sample of 72 (SGP) participants, consisting of 15 project teams (n = 15), which formed three groups: (1) the homogeneous adaptive group, which consists of five homogeneous adaptive teams (n = 5); (2) the homogeneous innovative group, which consists of five homogeneous innovative teams (n = 5), and (3) the heterogeneous group (i.e. a mix of innovative and adaptive individuals), which consists of five heterogeneous teams (n = 5).

A one-way ANOVA and post-hoc test revealed that team composition based on problem-solving styles is related to teamwork quality and TE. Regarding TE, both homogeneous groups (i.e. all adaptive or all innovative individuals) were more effective than the heterogeneous group. However, regarding teamwork quality, only the adaptive group had significantly higher teamwork quality than the heterogeneous group.

We recommend that leadership educators utilize Kirton’s adaption-innovation inventory (KAI) as a tool for building effective student project teams. KAI can be used by leadership educators in two major ways: to assign students to groups (as done in the current study) or for team building, where team members share their KAI scores to better understand their problem-solving preferences.

The findings add to the literature by specifying the type of homogeneous groups (i.e. homogeneous adaptive), which may offer an advantage over heterogeneous groups regarding teamwork quality.

This paper deals with two problems in transport network planning and control: trip matrix estimation and traffic signal optimisation. These two problems have both been formulated as bi-level programming problems with the User Equilibrium assignment as the second-level programming problem. One currently used method for solving the two problems consists of alternate optimisation of the two sub-problems until mutually consistent solutions are found. However, this alternate procedure does not converge to the solution of the bi-level programming problem. In this paper, a new algorithm will be developed and will be applied to two road networks.

Customers can participate in new product development (NPD) in many ways. Drawing on the knowledge-based view (KBV) and innovation literature, this study aims to contrast two main product development activity types, i.e. problem-solving and decision-making. It proposes customers play distinct roles if they get involved in these activities, which influence NPD outcomes differently. It also explores customer need specificity as a boundary condition for the above-mentioned relationships.

The authors collected survey data from 308 managers in the innovation domain.

Customer involvement in problem-solving and decision-making distinctively influences new product innovativeness and development speed. Customer need specificity interacts with the two co-development types differently to impact these NPD outcomes further.

This research extends the KBV and addresses the inconsistent findings in the literature regarding customer involvement as co-developers in innovation. It also provides novel insights into how knowledge characteristics like customer need specificity can direct co-developing activities to generate distinct NPD results.

This paper offers practical implications for firms on how to involve customers in developing innovative new products while managing development speed.

Prior research has yet to distinguish customer responsibilities related to co-development activities. This research fills this gap and offers novel insights that problem-solving and decision-making have opposite impacts on different NPD outcomes. This research demonstrates that finer knowledge about customer involvement responsibilities is needed for critical NPD outcomes.

This paper aims to examine the double-edged sword effects of work-related rumination on employees’ innovative performance. Drawing upon Conservation of Resources (COR) theory, this study developed a model to reveal the mechanism through which work-related rumination affects employees’ innovative performance.

Data were collected from a sample of employees and their managers at an information technology services firm in China. A two-wave data collection method with a one-month interval was employed. The analysis was conducted using structural equation modeling on 482 paired manager-subordinate responses.

Our findings highlight the complex interplay between work-related rumination and innovative performance. Specifically, affective rumination was found to detract from employees’ innovative performance, whereas problem-solving pondering had a positive effect. Notably, creative self-efficacy emerged as a key mediator in these relationships. Furthermore, employees’ perceived organizational support moderated the impact of rumination on creative self-efficacy and, consequently, on innovative performance, buffering the negative effects of affective rumination and enhancing the positive effects of problem-solving pondering.

Firstly, it enriches the existing literature on work-related rumination by exploring its nuanced influence on employees’ innovative performance. Secondly, it illuminates the underlying mechanism through which work-related rumination affects innovative performance, mediated by creative self-efficacy. Lastly, it highlights the crucial role of perceived organizational support in moderating these relationships, offering valuable insights for practitioners seeking to foster a more innovative work environment.

The purpose of this study is threefold: Determine recent trends in several mental health problems in the USA, identify risk factors that may be responsible for the trends and evaluate intervention policies to reduce the consequences of these problems.

This study used data from the National Survey of Children's Health (NSCH), a nationally representative survey of children under the age of 17 that was conducted between 2016 and 2022. Prevalence rates in the data take into account the probability of selection and nonresponse. Because of the possible correlation in the longitudinal responses in the data, an appropriate extension of the generalized linear models (the marginal models) was used. Marginal models, also known as population-average models, do not require distributional assumptions for the observations, only a regression model for the mean response. The avoidance of distributional assumptions leads to the use of the generalized estimating equations (GEE) method.

The author found that the odds of children and adolescents experiencing mental health problems in the USA changed over a seven-year period, from 2016 to 2022. Anxiety and depression, in particular, have both increased, with anxiety increasing faster than depression; however, behavioral issues and attention deficit disorder/attention deficit hyperactivity disoder (ADD/ADHD) remained stable until 2020 (the start of COVID-19), when they began to rise. This paper also found a link between increased social media use and increased mental health problems, and bullying has a negative impact on the mental health of children and adolescents.

The NSCH, an annual representative survey, was used in this study to assess mental health problems among children and adolescents in the USA. Marginal models, which enable the capture of potential correlations among observations of the same subject, were used in conjunction with the GEE method. This study differs from previous research, which used other surveys, pre-COVID-19 data points and logistic regressions that assumed independence in repeated observations.

A fleet of mobile robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of task allocation and motion planning problem for a fleet of mobile robots which are requested to operate in an intelligent industry. More specifically, the robots are requested to serve a set of inspection points within given service time windows. In comparison with the conventional time windows, our problem considers fuzzy time windows to express the decision maker’s satisfaction for visiting an inspection point.

The paper develops a unified approach to the combined problem of task allocation and motion planning for a fleet of mobile robots with three objectives: (a) minimizing the total travel cost considering all robots and tasks, (b) balancing fairly the workloads among robots and (c) maximizing the satisfaction grade of the decision maker for receiving the services. The optimization problem is solved by using a novel combination of a Genetic Algorithm with pareto solutions and fuzzy set theory.

The computational results illustrate the efficiency and effectiveness of the proposed approach. The experimental analysis leverages the potential for using fuzzy time windows to reflect real situations and respond to demanding situations.

This paper provides trade-off solutions to a realistic combinatorial multi-objective optimization problem considering concurrently the motion and path planning problem for a fleet of mobile robots with fuzzy time windows.

How an entrepreneurial organisation sets the problem space determines the future venturing mode. However, the factors that contribute to differences in problem space are still not well-developed. Based on sensemaking theory, this study explores the influence mechanism and boundary conditions of organisational sensemaking on effectual problem setting.

The study is based on survey data from 162 entrepreneurial organisations in China. Hierarchical regression is used for analysing the data and testing the hypotheses.

Organisational sensemaking has a negative effect on effectual problem setting and a positive impact on outside-in entrepreneurial marketing capability; outside-in entrepreneurial marketing capability has a negative effect on effectual problem setting; outside-in entrepreneurial marketing capability plays a partially mediating role in the relationship between organisational sensemaking and effectual problem setting. Organisational size negatively moderates the relationship between organisational sensemaking and outside-in entrepreneurial marketing capability. It also negatively moderates the indirect effect of organisational sensemaking on effectual problem setting through outside-in entrepreneurial marketing capability.

First, this study contributes to the research on effectual problem setting by introducing organisational sensemaking as an antecedent. Second, this study extends the literature on organisational sensemaking by showing how organisational sensemaking affects entrepreneurial uncertainty, instead of treating uncertainty as an exogenous shock. Third, this study introduced the outside-in entrepreneurial marketing capability to reveal the mediating mechanism between organisational sensemaking and effectual problem setting.

This research aims to elucidate the complex relationships among internship learning performance, problem-solving efficacy, and their determinants by simultaneously examining a key variable that may moderate these relationships.

This study carried out its empirical examination by surveying graduate students at a prestigious university in Taiwan. The investigation selected two programs from the College of Management and another two from the College of Engineering within the university through a random sampling approach. The selection of research participants from the domains of management and engineering is well-suited to this study’s objectives, given the pronounced prevalence of internships in these fields. Of the 280 questionnaires, 234 usable questionnaires were finally collected for a response rate of 83.57%.

The findings of this study show that learning goal orientation and communication skill learning both represent critical motivations that directly dominate the growth of problem-solving efficacy to ultimately boost learning performance. At the same time, the positive moderation of learning climate suggests the learning climate as an accelerator for learning autonomy that boosts interns’ problem-solving efficacy more strongly.

This study presents an expansion of the social learning theory’s conventional focus on general self-efficacy beliefs by delving into the realm of internships as a unique research context to explore problem-solving efficacy as a distinct facet of self-efficacy. Within this context, the study integrates the mediating role of problem-solving efficacy into the learning dynamics of internships, where interns engage in both learning goal orientation and communication skill learning. By concentrating on the internship setting within the workplace, this study effectively bridges the domains of management education and vocational psychology, extending insights beyond the classroom to comprehensively grasp the impact of learning goal orientation and communication skill learning.

This study investigated the problem-solving strategies used in solving problems in the learning domain of numbers and operations in mathematics textbooks for Turkish middle schools.

To this end, four middle school mathematics textbooks published by the Ministry of National Education of Turkey (MEB in Turkish), one from each grade level (Grades 5–8), were examined. The data in this document analysis study were analyzed using semantic content analysis.

The findings revealed that mathematics textbooks' most used problem-solving strategies for each level were drawing a figure (diagram), writing equality and inequality, making a table and making a systematic list. Drawing a diagram (figure) strategy was the most used strategy at each grade level, while working backwards was the least used one. Another finding was that finding a pattern and solving a simpler analogous problem strategies were rarely used at each grade level.

This study points out how problem-solving strategies used Grades 5-8 mathematics textbooks in a different culture like Türkiye. So, it may also give some important clues for applying problem-solving strategies in mathematics classrooms in a different culture.

This study may draw the attention of educational stakeholders and textbook authors who want to understand and implement problem-solving strategies in mathematics classrooms by considering a different cultural perspective.

This study may point to the importance of using problem-solving strategies in mathematics and daily and social learning environments due to the nature of mathematical problem-solving and problem-solving strategies.

This document review study examined the problem-solving strategies used in Turkish middle school mathematics textbooks, and the data were analyzed using semantic content analysis. The findings revealed that mathematics textbooks' most used problem solving strategies for each level were drawing a figure (diagram), writing equality and inequality, making a table and making a systematic list. Drawing a diagram (figure) strategy was the most used strategy at each grade level, while working backwards was the least used one. Another finding was that finding a pattern and solving a simpler analogous problem strategies were rarely used at each grade level.