Creative Problem Solving Preferences: Their Identification and Implications

Creative problem solving has been one of the most widely used and researched creative process models. The purpose of this study was to explore individuals’ preferences for different aspects of this model. The main premise to this research is that the creative problem solving process involves a series of distinct mental operations (i.e, collecting information, defining problems, generating ideas, developing solutions, and taking action) and people will express different degrees of preference for these various operations within the model. This study describes the development of the Buffalo Creative Process Inventory, a measure designed to identify preferences in terms of the major operations within Creative Problem Solving. Factor analysis, and correlations with two established style measures, indicate much promise for this new measure.     

MorphoTRIZ – Solving Technical Problems with a Demand for Multi‐Smart Solutions

Using two approved creativity techniques, technical problem solving can be supported in a systematic and analytical way: morphological and contradiction‐oriented problem solving (the latter as part of the Theory of Inventive Problem Solving [Russian acronym: TRIZ]). In these techniques different anchor points for creativity are used: morphological‐oriented problem solving is characterized by structuring a system into independent partial systems and finding answers by combining the different solutions of these subsystems; contradiction‐oriented problem solving is focused on finding fundamental contradictions within a system, representing the core problems, and solving them by applying the accumulated experiential knowledge of previous inventors. In this paper a combination of both techniques is developed and deployed, using network‐oriented function analysis as a connecting concept. We refer to this combination as MorphoTRIZ. It is particularly helpful in technical problem solving, when there is a demand for a multitude of smart solutions, which has become quite common with regard to design‐oriented products and individualized mass production as well as various other purposes. Seen from the conceptional perspective of creative problem solving, MorphoTRIZ combines process elements: (i) for producing a multitude of ideas, (ii) for producing many categories of ideas, and (iii) for producing highly original ideas. It provides connections to further creativity techniques, to open innovation and also to the field of management research as application area.     

The Creativity Continuum: Towards a Classification of Creative Problem Solving Techniques

It can be argued that companies may innovate on a more regular basis if groups and individuals can be encouraged to think more creatively. One method of encouraging novel ideas is to undertake creative problem solving (CPS). There are a number of different techniques that can enhance creative thinking. Some of these techniques require less imagination and produce less novel results than others. Individuals and group facilitators must choose which creative problem solving technique(s) to use for their specific situation. Some techniques, for example, may leave an inexperienced group feeling uncomfortable (e.g. if they are asked to draw pictures or develop fantasies) although they are often more effective than the more analytical methods. It may be helpful, therefore, to classify creative problem solving techniques so that individuals can easily choose an appropriate technique for their specific situation. This paper offers such a classification, which categorises the creative problem solving techniques into paradigm preserving, paradigm stretching and paradigm breaking. Some implications and future research are also presented.     

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.     

Conflict and Conflict Management in Innovation Processes in the Life Sciences

Research on social conflicts in innovation processes has been rare, leaving an important research gap, as conflicts often arise in innovation processes and are even part of a researcher's everyday life. This study examines innovation and the variables of innovation success in the life sciences; it tests a conflict management model and examines the impact of both conflict type and conflict management style on innovation performance. This study surveyed 152 basic and applied researchers on their conflict management style through a multi‐method approach incorporating both survey and qualitative methods. The substantive aspects and relational effectiveness of conflict management styles were compared, considering their number of publications and patents, problem solution quality, project newness, conflict de‐escalation and communication. Applied researchers showed significantly more domination than did basic researchers, and a dominating conflict management style was significantly related to project newness. Moreover, problem solving was not always the most successful conflict management style. These findings have important practical implications for conflict management training and can help managers and researchers strengthen their co‐operation and improve productivity.     

Different Styles for Different Needs – The Effect of Cognitive Styles on Idea Generation

Researchers are engaged in finding the precursors for innovation. Drawing on Kirton's Adaption‐Innovation (KAI) Inventory, we explicitly test Kirton's central premise that cognitive styles differentiate between preferences for producing ideas in a certain way. We argue that the generation of either a magnitude or original ideas is governed by different underlying cognitive styles. In a study with 191 individuals, we find that the cognitive style originality associates with ideational fluency whereas the rule governance style associates with the generation of original ideas. By providing a cognitive explanation for how ideas are generated, we deepen the understanding of the idea generation process. This is particularly important for the future use of the KAI and for organizations that strive to be innovative.     

New Developments in Computer Assisted Creative Problem Solving

A new kind of computer assisted creative problem solving method is examined in which visual images are provided to support the process. Until now, much attention has been given to developing programmes which help to structure problems and ideas so that they become more meaningful to the people who are concerned with them. Emphasis has been placed almost entirely on the written word as form of expression. It is argued that a combination of pictorial imagery and written words may be more effective in helping users to gain insights into problems and to come up with ideas or solutions to problems.     

How do you frame ill-defined problems? A study on creative logics in action

Problem framing is pivotal to fostering knowledge and innovation, especially in the modern environment where problems are often ill defined. However, the managerial literature has thus far mainly addressed problem framing from an outcome perspective, overlooking the processes that lead to the outcomes. A common view is that the complexity, ambiguity and uncertainty of ill-defined problems call for a creative process. Therefore, through ethnographically observing six design thinking workshops, this study adopts a qualitative approach to explore the problem framing creative process. Specifically, we unpack three thinking modalities involved in the creative process (i.e. creative logics) of problem framing: analogical reasoning, associative thinking and abductive reasoning. We suggest that individuals enact these through seven creative operations. In addition, we link these creative operations to two types of problem framing outcomes: referenced frames and crafted frames. From a practitioner perspective, this study casts new light on the importance of problem framing for creativity and innovation, highlighting the ways in which individuals operationalize the creative logics to frame ill-defined problems as original problems worth solving.     

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.     

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.      

Regular solutions of linear differential systems with power series coefficients

The following problem is considered: given a system of linear ordinary differential equations of arbitrary order with power series coefficients, to recognize whether it has regular solutions at point 0 and, if it does, to find them. An algorithm for solving this problem is proposed. Each power series that is a coefficient of the original system is specified by a procedure that computes the series coefficient by the index of this coefficient. The original system is assumed to have full rank; i.e., the equations of the system are independent. The algorithm is implemented in Maple.     

Reinforcement learning: Architectures and algorithms

This article is related to the research effort of constructing an intelligent agent, i.e., a computer system that is able to sense its environment (world), reason utilizing its internal knowledge and execute actions upon the world (act). the specific part of this effor presented in this article is reinforcement learning, i.e., the process of acquiring new knowledge based upon an evaluative feedback, called reinforcement, received by tht agent through interactions with the world. This article has two objectives: (1) to give a compact overview of reinforcement learning, and (2) to show that the evolution of the reinforcement learning paradigm has been driven by the need for more efficient learning through the addition of more structure to the learning agent. Therefore, both main ideas of reinforcement learning are introduced, and structural solutions to reinforcemen learning are reviewed. Several architectural enhancements of the RL paradigm are discussed. These include incorporation of state information in the learning process, architectural solutions to learning with delayed reinforcement, dealing with structurally changing worlds through utilization of multiple models of the world, and focusing attention of the learning agent through active perception. the paper closes with an overview of directions for applications and for future research in this area. © 1993 John Wiley & Sons, Inc.     

Older Adults Talk Technology: Technology Usage and Attitudes

Older adults (n = 113) participated in focus groups discussing their use of and attitudes about technology in the context of their home, work, and healthcare. Participants reported using a wide variety of technology items, particularly in their homes. Positive attitudes (i.e., likes) outnumbered negative attitudes (i.e., dislikes), suggesting that older adults perceive the benefits of technology use to outweigh the costs of such use. Positive attitudes were most frequently related to how the technology supported activities, enhanced convenience, and contained useful features. Negative attitudes were most frequently associated with technology creating inconveniences, unhelpful features, as well as security and reliability concerns. Given that older adults reported more positive than negative attitudes about the technologies they use, these results contradict stereotypes that older adults are afraid or unwilling to use technology. These findings also highlight the importance of perceived benefits of use and ease of use for models of technology acceptance. Emphasizing the benefits of technology in education and training programs may increase future technology adoption.     

Self-efficacy beliefs, computer training and psychological well-being among information and communication technology workers

This study examines the factorial structure of self-efficacy and the relationship among different levels of self-efficacy (i.e., generalised, professional and computer) and psychological well-being and training among Information and Communication Technology (ICT) workers. The sample was made up of 496 workers from different occupational sectors using ICT in their jobs and a subsample of these workers who were trained in ICT. Results show that (1) the generalised-specific structure is maintained in the three levels as expected but in fours factors: i.e., generalised self-efficacy, professional self-confidence, achieving professional objectives and computer self-efficacy. (2) There is a negative and significant relationship between self-efficacy and different psychological well-being indicators. (3) Computer attitudes moderated the relationship between computer training (i.e., number of training hours) and professional self-confidence. So far, workers with high positive attitude towards ICT, when number of hours are high, their levels of professional self-confidence increase, but it depends on the number of training hours (i.e. more training hours, more self-confidence). However, workers with low levels of positive attitude towards ICT experimented a decrease in professional self-confidence. In conclusion, training has not a direct influence on self-efficacy but moderated by type of training. Limitations of the study and practical implications of these findings are discussed.     

Towards a More Realistic Creative Problem Solving Approach

This paper takes a closer look at the existing multi‐step diamond shaped models for creative problem solving (CPS). A case history of a real‐life technical problem in which CPS techniques were used is our source of inspiration for some new ideas about approaching CPS. We propose three concurrent processes: Content finding, Acceptance finding and Information finding. In concrete in‐company projects, these three processes need to be managed simultaneously, which leads to a fourth overarching process: project management. Content finding is concerned with the process the creative session members are going through based on people's own active knowledge and ideas and on sharing their mental models to get new ideas. Acceptance finding is concerned with the co‐creation of new and additional mental systems that are needed for bringing new ideas into good currency within the existing organization and goes beyond agreement on implementation plans. Information finding is concerned with gathering additional knowledge on the ideas that are not readily available during the session. Finally, Project management is concerned with organizing and leading the creative session and in the embedding of the project into the larger organization.     

Resilient employees are creative employees,when the workplace forces them to be

With a basis in conservation of resources theory, this article considers the connection between employees' resilience and disruptive creative behaviour—conceptualized herein as the extent to which they generate radically new ideas for organizational improvement—as well as how this connection might be invigorated by resource‐draining work conditions that stem from excessive workloads and unfavourable decision‐making processes. Data collected through a survey administered to employees in an organization that operates in the distribution sector reveal that employees' resilience levels spur their disruptive creative behaviour, and this process is more prominent among employees who believe they have insufficient time to complete their work tasks (i.e., suffer from high work overload) and operate in organizational climates marked by high rigidity or dysfunctional politics. The findings accordingly inform organizational practitioners that the allocation of employees' personal resource bases to disruptive creative behaviours might be particularly useful among employees who face substantial adversity in their organizational functioning.     

Integrated intelligent techniques for remarshaling and berthing in maritime terminals

Maritime container terminals are facilities where cargo containers are transshipped between ships or between ships and land vehicles (trucks or trains). These terminals involve a large number of complex and combinatorial problems. Two important problems are the container stacking problem and the berth allocation problem. Both problems are generally managed and solved independently but there exist a relationship that must be taken into account to optimize the whole process. The terminal operator normally demands all containers bound for an incoming vessel to be ready in the terminal before its arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. This is particularly important for vessels from priority customers who may have been guaranteed berth-on-arrival service in their contract with the terminal operator. To this end, both problems must be interrelated.In this paper, a set of artificial intelligence based-techniques for solving both problems is presented. We develop a planning technique for solving the container stacking problem and a set of optimized allocation algorithms for solving the berth allocation problem independently. Finally we have developed an architecture to solve both problems in an integrated way. Thus, an algorithm for solving the berth allocation problem generates an optimized order of vessels to be served meanwhile our container stacking problem heuristics calculate the minimum number of reshuffles needed to allocate the containers in the appropriate place for the obtained ordering of vessels. Thus combined optimal solutions can be calculated and the terminal operator could decide which solution is more appropriate in each case. These techniques will minimize disruptions and facilitate planning in container terminals.     

Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach

Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.     

WHAT IS TO BE DONE: LEARNING DEMOCRACY WHILE IMPROVING ORGANIZATIONS

Two-day planning exercises are one way to meet the demand for management training in the postcommunist countries. These exercises serve as problem-solving meetings, help the people who participate to become more accustomed to open discussions, and provide an opportunity for academics and people from related organizations to learn about the problems of an organization and to cooperate in finding solutions. Participatory planning exercises require no prior technical training. Because the method is easy to learn, exercises of this kind can spread rapidly through a society, providing many people with a new kind of experience in organizational problem solving. Because the method is learned by applying it in practice, managers are not removed from their responsibilities as managers. The method is particularly effective at teaching the kinds of skills most needed in countries making the transition to democracy and market economies—individual initiative and bottom-up decision making.     

A columnar competitive model for solving combinatorial optimization problems

The major drawbacks of the Hopfield network when it is applied to some combinatorial problems, e.g., the traveling salesman problem (TSP), are invalidity of the obtained solutions, trial-and-error setting value process of the network parameters and low-computation efficiency. This letter presents a columnar competitive model (CCM) which incorporates winner-takes-all (WTA) learning rule for solving the TSP. Theoretical analysis for the convergence of the CCM shows that the competitive computational neural network guarantees the convergence to valid states and avoids the onerous procedures of determining the penalty parameters. In addition, its intrinsic competitive learning mechanism enables a fast and effective evolving of the network. The simulation results illustrate that the competitive model offers more and better valid solutions as compared to the original Hopfield network.