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This case study was undertaken to characterise the interventions made by a kindergarten teacher during a multiplicative structure problem-solving lesson. The study findings show that when she realised the children were having difficulty, she provided assistance to enable them to find the right answer. When the children gave an incorrect response, she did not prompt them to reason their ideas, but either ignored their response or guided them to the right answer with manipulatives. Moreover, less varied and simpler problem-solving strategies were employed when the teacher gave instructions than when she gave help or clues. From this, we conclude that the intervention determined the type of strategy used by the children and the teacher varied their discourse depending on the children's performance. [ABSTRACT FROM AUTHOR]

Computer cooking is a task-related phenomenon where students (end-users) must blindly follow a long list of orders without any connection to the content of the problem, if there is any. Despite its low efficacy, this method is widely used and accepted in informatics both in the learning-teaching process and testing. The National Base Curriculum 2020 in Hungary is in complete accordance with the 'Informatics Reference Framework for Schools', but the course books hardly use the latest results of computer education research. The present paper provides examples of how the results of computer education research can be integrated into teaching-learning materials and classroom practices and discusses the effectiveness and consequences of the different solutions, where tool-centred approaches are compared to problem-focused solutions. [ABSTRACT FROM AUTHOR]

This article presents the educational product generated from the research entitled “Teaching Exponential Function with Problem Solving Methodology: Report of a Practice”, developed within the scope of the Professional Master's Degree in Science, Mathematics and Technology Teaching at the State University of Santa Catarina (UDESC). The educational product is a Facebook page composed of applied activities, activity suggestions, activity reports, videos of activity resolutions, reports of practices in the application of problem situations on the exponential function through problem solving, activity resolutions, videos and problem suggestions for applications and video suggestions, the objective is to share experiences and exchange information, motivating and encouraging educators in their practices. The design of the educational product aimed to contribute to the teaching practice of in-service teachers, both in mathematics classes and in their professional life. On the “Facebook” page, the activities that were applied in the classroom are described using the Problem Solving Methodology methodology, based on studies by researchers Lourdes de la Rosa and Norma Suely Gomes Allevato, which were applied in the classroom together with Geogebra objects. [ABSTRACT FROM AUTHOR]

BACKGROUND: The COVID-19 pandemic has been challenging for all and has had a particular effect on university-level educators. Although the use of technologies in education is recognized as critical in developing transversal skills and preparing students for the needs of modern society, the COVID-19 pandemic crisis stressed the relevance of the digital medium. At the same time, the emergency hastened significant changes to universities' main roles - teaching, learning, and evaluation. AIMS: In this article, we present research conducted by a Problem-Solving Group, a virtual community of practice formed by the International Consortium of Universities for Drug Demand Reduction (ICUDDR) to support educators in addressing the challenges of the pandemic crisis. METHODS: Based on a problem-solving methodology, the virtual community of practice (Problem-Solving Group) provided synchronous group sessions and asynchronous individual support. The resulting analysis and discussion are based on the problemsolving methodology. PARTICIPANTS: Twenty-two educators, including two facilitators, attended six virtual problem-solving sessions between September and December 2020. RESULTS: Participants were committed educators who shared their experiences, challenges, and best practices. The problem-solving methodology was effective in identifying critical areas in remote emergency university-level teaching. CONCLUSIONS: The results stress the importance of creating a common space where educators with similar problems and difficulties can share ideas, experiences, and best practices. The virtual community of practice was effective, although it requires more extensive development and research. [ABSTRACT FROM AUTHOR]

Academic works have indicated which didactic methods to teaching engineers fail in the professional preparation, despite the international effort to create competences for engineering students. Studies about teaching in engineering in general, and in civil engineering, specifically, are scarce, or is in the incipient period, at least in relation to and compared to the material dedicated to the same subject by mathematicians, then, the aim of this paper is to discuss if some aspects of the modern technique of mathematics could be applied in engineering teaching, in specific case for the problem solving methodology.

The article argues how effective altruism concern with the happiness of others. Topics include ethics of altruism like being with others serving people for best results; moral requirements of conditional obligations ways of difference with hypothetical imperatives; and the ways to rescue the human beings who stuck any kind of problem or situation.

There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1–S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, flipped learning has received tremendous attention from educational practitioners and researchers. However, this study argues that existing e‐learning systems mainly serve for learning management and content delivery purposes, although they lack support for flipped learning. As an innovative educational approach, flipped learning requires more pedagogical elements, such as integrated instructional design and adaptive content delivery, to achieve effective direct instruction. This study aims to create a learning adaptivity design to support effective learning in the flipped individual learning space in which the teacher is absent. Because teaching involves various pedagogical and content knowledge sources, we propose a conceptual model of teaching as a function of the knowledge triad of Guideline (G), Teaching Activity (T), and Material (M). To realize this conceptualization, an ontological problem‐solving approach is used for knowledge‐based systems development to integrate the relevant knowledge sources. The knowledge model is created using the Protégé platform to develop OWL‐based domain ontology, task ontology, and SWRL‐based semantic rules to enable inference in the GTM triad for learning adaptivity. The case illustration shows that the knowledge‐based system prototype can adaptively guide student learning in the flipped individual learning space with the knowledge sources integrated. [ABSTRACT FROM AUTHOR]

Within family stress theories, the way in which families communicate about their hardships and issues is crucial for the family’s stress management and functioning. In the current study, we sought to validate the Portuguese version of the Family Problem Solving Communication (FPSC) Index and examine its psychometric properties. Developed within the context of the resiliency model of family stress, adjustment and adaptation, the FPSC is a ten-item self-report measure that assesses the family communication patterns that are likely to play a role in the family’s coping with hardships and difficulties. The participants were 332 individuals between 18 and 76 years who completed the FPSC and self-report measures of family hardiness and family functioning. A confirmatory factor analysis supported the original FPSC bifactorial structure, indicating the presence of two dimensions, Affirmative Communication and Incendiary Communication. Correlations between the scale’s total score and its dimensions with the measures of family hardiness and family functioning attested to its convergent validity. Furthermore, the index demonstrated respectable to very good internal consistency and temporal stability. In conclusion, the Portuguese version of the FPSC is a valid and reliable instrument that can support the development of future empirical studies focused on family problem solving communication, especially with regard to family adaptation in different contexts of adversity. [ABSTRACT FROM AUTHOR]

The concept of stratification (CST) has recently been proposed as an innovative approach in problem solving. CST takes a recursive approach to solve problems. It considers a system which has to transition through states until it arrives to a state which belongs to a desired set of states, namely a target set. The states can be stratified by enlarging the target (absorbing adjacent states). Incremental enlargement is a means to identify possible paths to achieve the target. Such an enlargement can also be used to degrade the target when the original target is not reachable. Although the characteristics of the concept, such as incremental enlargement, enhance its potential application in robotics, artificial intelligence, and planning and monitoring, there is a major shortcoming in the approach, namely its inability to consider targets of unequal importance. This study considers two targets of unequal importance for the system in CST, labelled Bi-Objective CST model (BOCST). In comparison with the original proposed CST model in this research, a version of CST with finite states which is much easier to apply than the original CST is proposed, labelled fuzzy CST. Following that, a combination of Fuzzy CST and BOCST (FBO-CST) is proposed. The model is then employed to address a restaurant selection problem using data from Google. The example illustrates how the model should be applied in a big data environment. By defining finite state CST and considering targets of unequal importance, this study is expected to facilitate future applications of CST. [ABSTRACT FROM AUTHOR]

The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose “black-box” solver. In practice, however, many problems cannot be solved completely automatically, but require a “human-in-the-loop” to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem. [ABSTRACT FROM PUBLISHER]

The article presents an argument that the existence of a physical process that can solve a problem in a polynomial number of macroscopic steps does not entail the existence of a Turing machine, physical or abstract, that can solve the same problem in a polynomial number of read/write tape moves, even if we grant digital physics. The argument is supported by Barcan Formula.

Worrying is a key concept in describing the complex relationship between anxiety and cognitive control. On the one hand, cognitive control processes might underlie the specific tendency to engage in worrying (i.e., trait worry), conceptualized as a future-oriented mental problem-solving activity. On the other hand, the general tendency to experience the signs and symptoms of anxiety (i.e., trait anxiety) is suggested to impair cognitive control because worrisome thoughts interfere with task-relevant processing. Based on these opposing tendencies, we predicted that the effect of the two related constructs, trait anxiety and trait worry, might cancel out one another. In statistics, such instances have been termed suppressor situations. In four experiments, we found evidence for such a suppressor situation: When their shared variance was controlled, trait worry was positively whereas trait anxiety was negatively related to performance in a memory task requiring strategic, effortful retrieval. We also showed that these opposing effects are related to temporal context reinstatement. Our results suggest that trait worry and trait anxiety possess unique sources of variance, which differently relate to performance in memory tasks requiring cognitive control. [ABSTRACT FROM PUBLISHER]

Although research suggests a compelling connection between juvenile delinquency and minors with learning disabilities, rarely any action is taken to thwart or prevent the continuation of this trend. This leaves minors with learning disabilities predisposed to a future with a criminal record, and left at the mercy of the juvenile justice system that fails to accommodate them. This Note proposes the nationwide adoption of problem-solving courts that implement screening procedures alongside treatment programs for children with learning disabilities. Without preventative measures and proper handling of these minors, the rise of recidivism rates, stigmatization, and the deficiency of treatment are inevitable. [ABSTRACT FROM AUTHOR]

Organizations are faced with solving increasingly complex problems. Addressing these issues requires effective leadership that can facilitate a collaborative problem solving approach where multiple perspectives are leveraged. In this conceptual paper, we critique the effectiveness of earlier leadership models in tackling complex organizational issues. We then examine one promising model, adaptive leadership, in detail and propose that this model provides a leadership approach for addressing current organizational realities. The model, proposed and developed over the last two decades, fundamentally supports the assumption of leadership by multiple stakeholders, with the formulation of the leadership dependent on the emergent problem. Adaptive leadership, with its focus on collaborative problem-solving utilizing multiple perspectives, is especially applicable to large organizations faced with solving complex problems involving many stakeholders. [ABSTRACT FROM AUTHOR]

Depth First Search (DFS) pseudo-tree is popularly used as the communication structure in complete algorithms for solving Distributed Constraint Optimization Problems (DCOPs) from multiagent systems. The advantage of a DFS pseudo-tree lies in its parallelism derived from pseudo-tree branches because the nodes in different branches are relatively independent and can compute concurrently. However, the constructed DFS pseudo-trees in experiments often come to be chain-like and greatly impair the performances of solving algorithms. Therefore, we propose a new DPOP algorithm using a Breadth First Search (BFS) pseudo-tree as the communication structure, named BFSDPOP. Compared with a DFS pseudo-tree, a BFS pseudo-tree is more excellent on the parallelism as it has much more branches. Another notable advantage is that the height of a BFS pseudo-tree is much lower than that of a DFS pseudo-tree, which gives rise to the shorter communication paths and less communication time. The method of Cluster Removing is also presented to allocate cross-edge constraints to reduce the size of the largest message in BFSDPOP. In the experiment, BFSDPOP with a BFS pseudo-tree and original DPOP with a DFS pseudo-tree are compared on three types of problems - graph coloring problems, meeting scheduling problems and random DCOPs. The results show that BFSDPOP outperforms original DPOP in most cases, which proves the excellent attributes of BFS pseudo-tree over DFS pseudo-tree. [ABSTRACT FROM AUTHOR]

In this paper we present and theoretically study an Approximate Policy Iteration (API) method called A P I − B R M using a very effective implementation of incremental Support Vector Regression (SVR) to approximate the value function able to generalize Reinforcement Learning (RL) problems with continuous (or large) state space. A P I − B R M is presented as a non-parametric regularization method based on an outcome of the Bellman Residual Minimization (BRM) able to minimize the variance of the problem. The proposed method can be cast as incremental and may be applied to the on-line agent interaction framework of RL. Being also based on SVR which are based on convex optimization, is able to find the global solution of the problem. A P I − B R M using SVR can be seen as a regularization problem using 휖−insensitive loss. Compared to standard squared loss also used in regularization, this allows to naturally build a sparse solution for the approximation function. We extensively analyze the statistical properties of A P I − B R M founding a bound which controls the performance loss of the algorithm under some assumptions on the kernel and assuming that the collected samples are not-i.i.d. following a β−mixing process. Some experimental evidence and performance for well known RL benchmarks are also presented. [ABSTRACT FROM AUTHOR]

This article aims to test the effectiveness of using the Problem Solving Methodology in science teachers training courses. The problem situations produced by teachers in the contexts of initial and in-service training are categorized and analyzed. The methodology involved conducting a literature review, conducting teacher training experiences and analysis of the body of information gathered during the courses, as well as problem-situations produced in these contexts. The results show aspects of teacher training for working with teaching strategies as the role of the teacher in the authoring and development problems, pedagogical reasoning, epistemological, psychological preparation for a more structured conceptual basis and strengthening of the teachers’ practical knowledge.

The A3 method is a systematic problem solving and continuous improvement approach, which was first employed at the company Toyota. The paper presents possibilities of using the method for searching and effective implementing green innovations in an industrial company transport. Advantages and potential problems related to the method utilization are identified on the basis of a case study focused on technological innovations in ŠKODA AUTO internal transport. [ABSTRACT FROM AUTHOR]

This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. In this article, ATC21S collaborative assessments focus on the project's 'ICT Literacy-Learning in digital networks' learning progression. The article includes a description of the development of the learning progression, as well as examples and the logic behind the instrument construction. Assessments took place in random pairs of students in a demonstration digital environment. Modeling of results employed unidimensional and multidimensional item response models, with and without random effects for groups. The results indicated that, based on this data set, the models that take group into consideration in both the unidimensional and the multidimensional analyses fit better. However, the group-level variances were substantially higher than the individual-level variances. This indicates that a total individual estimate of group plus individual is likely a more informative estimate than individual alone but also that the performances of the pairs dominated the performances of the individuals. Implications are discussed in the results and conclusions. [ABSTRACT FROM AUTHOR]

Collaborative problem solving (CPS) assessment is a new academic research field with a number of educational implications. In 2015, the Programme for International Student Assessment (PISA) assessed CPS with a computer-simulated human-agent (H-A) approach that claimed to measure 12 individual CPS skills for the first time. After reviewing the approach, we conceptually embedded a computer-based collaborative behavior assessment (COLBAS) into the overarching PISA 2015 CPS approach. COLBAS is an H-A CPS assessment instrument that can be used to measure certain aspects of CPS. In addition, we applied a model-based cluster analysis to the embedded COLBAS aspects. The analysis revealed three types of student collaborator profiles that differed in cognitive performance and motivation: (a) passive low-performing (non-)collaborators, (b) active high-performing collaborators, and (c) compensating collaborators. [ABSTRACT FROM AUTHOR]

Although it is widely believed that inducing a change in mental perspective through personal identification with an object facilitates problem solving, empirical evidence that supports this thesis is limited. The present study aimed at recognition of factors determining the effectiveness of personal identification by verifying the efficiency of a new technique of creative problem solving called Revolution. Forty-six subjects participating in the naturalistic study were randomly assigned to five experimental groups testing several versions of Revolution. The following factors were manipulated: personal identification (presence versus lack of identification), problem type (abstract versus specific) and time lag between the preparation stage (preparing the project) and the execution stage. Solutions generated by different groups were evaluated by a team of eight competent judges using the Creative Product Semantic Scale. There was no difference in the overall quality of the solutions when problem type variable was under consideration. Surprisingly, identification appeared to have a negative impact on product creativity (as evaluated by the judges). Detailed analyses revealed that the negative influence of personal identification was limited to specific task conditions, implying that it can be neutralized by separating the preparation from execution stage. The implications of the present results are discussed. [ABSTRACT FROM AUTHOR]

This paper aims to approach the conception of the discipline Solving Mathematical Problems (RPM) of the State Bureau of Education of Rio de Janeiro (SEEDUC), both in view of the Bureau and of a group belonging to the state network teachers. For this, it was analyzed the Bureau document that guides the implementation of RPM discipline and it was made a profile and opinion brief research with this group of teachers. [ABSTRACT FROM AUTHOR]

This paper aims to test whether the use of contraries can facilitate spatial problem solving. Specifically, we examined whether a training session which included explicit guidance on thinking in contraries would improve problem solving abilities. In our study, the participants in the experimental condition were exposed to a brief training session before being presented with seven visuo-spatial problems to solve. During training it was suggested that it would help them to find the solution to the problems if they systematically transformed the spatial features of each problem into their contraries. Their performance was compared to that of a control group (who had no training). Two participation conditions were considered: small groups and individuals. Higher success rates were found in the groups exposed to training as compared to the individuals (in both the training and no training conditions), even though the time required to find a solution was longer . In general, participants made more attempts (i.e., drawings) when participating in groups than individually. The number of drawings done while the participants were trying to solve the problems did not increase after training. In order to explore if the quality (if not the number) of drawings was modified, we sampled one problem out of the seven we had used in the experiment (the "pigs in a pen" problem) and examined the drawings in detail. Differences between the training and no training conditions emerged in terms of properties focused on and transformed in the drawings. Based on these results, in the final discussion possible explanations are suggested as to why training had positive effects specifically in the group condition. [ABSTRACT FROM AUTHOR]

Legal reasoning is a type of problem solving, and is situated within thinking skills, one of the six threshold learning outcomes established under the auspices of the Australian Learning and Teaching Council's Bachelor of Laws Learning and Teaching Academic Standards Statement. The threshold learning outcomes define what law graduates are 'expected to know, understand and be able to do as a result of learning' (Kiff et al., 2010, p. 9). The assessment of legal reasoning, and thus problem solving, should receive greater attention in legal education discourse (James, 2011, p. 15, James, 2012, p. 88). The dominant approach for problem-based questions in the discipline of law over the last 40 years is IRC (issue, rule, application and conclusion). The acronym IRC is not offensive and potentially instils a positive professional legal identity and is a student-centred approach to problem solving. This journal article documents an incremental approach to IRC in law where first year students answer a problem-based law question using a grid format before preparing a barrister's advice. [ABSTRACT FROM AUTHOR]

The article discusses changes involving legal problem-solving methods in relation to topics such as non-adversarial justice, therapeutic jurisprudence, and the possible role of law as a healing agent, and it mentions compassion, drug courts, and criminal justice in New Zealand.

This study demonstrates how a decision-making trial and evaluation laboratory (DEMATEL) threshold value can be quickly and reasonably determined in the process of combining DEMATEL and decomposed theory of planned behavior (DTPB) models. Models are combined to identify the key factors of a complex problem. This paper presents a case study of a food and beverage information system as an example. The analysis of the example indicates that, given direct and indirect relationships among variables, if a traditional DTPB model only simulates the effects of the variables without considering that the variables will affect the original cause-and-effect relationships among the variables, then the original DTPB model variables cannot represent a complete relationship. For the food and beverage example, a DEMATEL method was employed to reconstruct a DTPB model and, more importantly, to calculate reasonable DEMATEL threshold value for determining additional relationships of variables in the original DTPB model. This study is method-oriented, and the depth of investigation into any individual case is limited. Therefore, the methods proposed in various fields of study should ideally be used to identify deeper and more practical implications. [ABSTRACT FROM AUTHOR]

This paper explores the notion of ‘innovation spaces’ within the UN system, as physical and virtual laboratories for innovation. Using empirical research in a range of innovation labs the authors explore four key questions: what form UN innovation labs have taken, what has motivated their creation, what their aims and objectives are, and what impact they are having. The answers to these questions promote reflection on the future of innovation spaces, particularly an analysis of whether a model of ‘siloed’ innovation spaces will survive in the humanitarian system. The paper demonstrates the important role that innovation labs play in the UN system, as well as grappling with the challenges they face. [ABSTRACT FROM PUBLISHER]

This article attempts to make a map of the research on Problem Solving, from journals: Boletim GEPEM, BOLEMA, Educação Matemática em Revista, Educação Matemática Pesquisa e ZETETIKÉ. The period analyzed was from the creation of the magazine until 2010. The aim was to investigate thematic focus of research in Problem Solving, as well as the distribution of research in these focus. It was also possible to investigate when it launched the first article on Problem Solving in Brazil - 1977, as well as the decades of increased production in the research on the subject. [ABSTRACT FROM AUTHOR]

Creative problem solving (CPS) requires solutions to be useful and original. Typically, its operations span problem finding, idea generation, and critical evaluation. The benefits of training CPS have been extolled in education, industry, and government with evidence showing it can enhance performance. However, although such training schemes work, less is known about the specific tools used. Knowing whether a particular tool works or not would provide practitioners with a valuable resource, leading to more effective training schemes, and a better understanding of the processes involved. A comprehensive review was undertaken examining the empirical support of tools used within CPS. Despite the surprising lack of research focusing on the use and success of specific tools, some evidence exists to support the effectiveness of a small set. Such findings present practitioners with a potential resource that could be used in a stand-alone setting or possibly be combined to create more effective training programs. [ABSTRACT FROM AUTHOR]

The article discusses the development of effective problem solving strategies for students leaning. It mentions that students must learn such strategies to develop expertise in physics that includes conceptual analysis of the problem, planning of the solution, implementation, evaluation and reflection upon the process. It notes that drawing a diagram is considered as one of the effective problem solving strategy.

Established in 1923, Turkey has been a republic without a dominant republican conception of liberty. A chance to install such a conception was missed in the early republican period and never recaptured. The republic was unable to get rid of vestiges of the authoritarian tradition of the past. Centuries-old authoritarian tradition persisted well into the recent and the contemporary periods. Presenting ample evidence, the article underlines the weight of history and the legacy of authoritarian mentality that promoted the use of authority, not liberty, in political problem-solving. The initial failure to abandon an authoritarian problem-solving approach proved fateful for the chances of the deepening of democracy in Turkey. [ABSTRACT FROM AUTHOR]

The article discusses the use of the search, translate, answer, review (STAR) strategy to improve mathematical problem solving abilities of students with emotional and behavioral disorders (EBD). Topics mentioned include the components of the STAR strategy which engage students with cognitive processes required to solve mathematical problem, the strategy's use of mnemonic devices to manage word problems, and the aspects to consider when applying the STAR strategy for students with EBD.

Insightful problem solving is a vital part of human thinking, yet very difficult to grasp. Traditionally, insight has been investigated by using a set of established "insight tasks," assuming that insight has taken place if these problems are solved. Instead of assuming that insight takes place during every solution of the 9 Dot, 8 Coin, and Matchstick Arithmetic Problems, this study explored the likelihood that solutions evoked the "Aha! experience," which is often regarded as the defining characteristic of insight. It was predicted that the rates of selfreported Aha! experiences might vary based on the necessary degree of constraint relaxation. The main assumption was that the likelihood of experiencing an Aha! would decrease with increasing numbers of constraints that must be relaxed, because several steps are needed to achieve a representational change and solve the problem, and thus, the main feature of suddenness of a solution might be lacking. The results supported this prediction, and demonstrated that in many cases participants do solve these classical insight problems without any Aha! experience. These results show the importance of obtaining insight ratings from participants to determine whether any given problem is solved with insight or not. [ABSTRACT FROM AUTHOR]

The financial challenges of recent times places a premium on national statistical institutes (NSIs) to creatively examine how to 'do more with less'. Faced with significant staffing reductions and demands for quality improvements, CSO introduced the lean six sigma problem solving methodology. In this paper we explore: * Why CSO introduced lean and how we introduced it * Examples of successful lean projects: how we did it and the benefits achieved in terms of cost reduction and quantifiable quality improvement * Examples of where we had difficulties with lean projects - the learning lessons/implementation issues national statistical institutes should be aware of, including the importance of placing lean within a strategic context. [ABSTRACT FROM AUTHOR]

Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an opposition-based learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions. [ABSTRACT FROM AUTHOR]

In this contribution, we propose an interactive multicriteria optimisation framework for the time and space assembly line balancing problem. The framework allows decision maker interaction by means of reference points to obtain the most interesting non-dominated solutions. The principal components of the framework are the $$g$$ -dominance preference scheme and a state-of-the-art memetic multiobjective ant colony optimisation approach. In addition, the framework includes a novel adaptive multi-colony mechanism to be able to handle the preferences in an interactive way. Results show how the multiobjective framework can interactively obtain the most useful solutions with higher convergence than previous a priori methods. The experimentation also makes use of original data of the Nissan Pathfinder engine and practical bounds to define industrially feasible solutions in a set of scenarios. By solving the problem in these scenarios, we show the search guidance advantages of using an interactive multiobjective ant colony optimisation method. [ABSTRACT FROM AUTHOR]

Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems. We aim at designing advanced optimization algorithms, with inspiration from human's creative problem-solving strategies. In this paper, we proposed an advanced discussion mechanism-based brain storm optimization (ADMBSO) algorithm, pushing forward our study in the incorporation of inter- and intra-cluster discussions into the brain storm optimization algorithm (BSO) to control global and local searching ability, respectively. In the advanced discussion mechanism, elaborately designed inter- and intra-cluster discussions were alternatively performed throughout the optimization process, with the ratio controlled by a linearly adjusted probability. We further introduced a differential step strategy into the workflow, making ADMBSO a more efficient and more adaptive algorithm. Empirical studies on different function optimization problems illustrated the effectiveness and efficiency of the ADMBSO algorithm. Comparisons among the ADMBSO, BSO algorithm, closed-loop brain storm optimization algorithm, particle swarm optimization algorithm, and differential evolution algorithm, have also been provided in detail. As one of the first algorithms inspired by human behavior, ADMBSO demonstrates its great potential in dealing with complex optimization problems. [ABSTRACT FROM AUTHOR]