Solution Space Of Problem Research Articles

Fathom: a technology-enhanced learning environment for teaching-learning of expand-reduce skills in software design

Software design problems are often characterized as ill-structured because the requirements are not clearly defined. These problems offer various solution paths and the criteria used to select the solution may not be known initially. The expertise and experience of designers play a crucial role in determining the quality of software design. Less experienced designers often tend to prematurely narrow down their options to a single solution without fully exploring the problem-solution space. This tendency has a negative impact on the overall quality of software design. Studies have shown that systematically expanding the problem space before reducing to problem formulation and exploring the solution space before reducing it to a single solution (expand-reduce skills) improves the quality of the design. We have designed and developed a technology-enhanced learning environment (TELE) named Fathom for scaffolding expand-reduce (ER) skills in software design problems in a data structures course. In this paper, we present three cycles of design, development, and evaluation of Fathom based on the design-based research (DBR) approach. In the first DBR cycle, we identified and evaluated Fathom’s pedagogical features in learning ER skills. In the second cycle of DBR, the aim was to improve the design of Fathom for the learning and transfer of ER skills. Fathom was revised in the third cycle of the DBR to scaffold metacognitive skills. The main contribution of this research is pedagogical design for facilitating the learning of expand-reduce skills in solving software design problems.

Careless product use in access-based services: A rebound effect and how to address it

The sharing economy promises to alleviate environmental pressures by optimizing the distribution and use of resources, for example, shifting from ownership to access-based services. Services that grant users access for a long period only improve sustainability if they extend product lifetimes. However, careless use of accessed products reduces product lifetimes. We investigate the problem–solution space of product care in access-based services. A user survey reveals significantly lower product care for accessed than owned washing machines. We then systematically develop solutions to stimulate product care in access-based services, resulting in ten strategies and 23 design solutions. Levers and barriers to promoting product care are uncovered through interviews with access-based service providers; the range of products impacts providers’ ability to implement product-specific care strategies, the service can be designed to deter or attract user groups, and providers’ control over the supply chain determines which product care strategies they can implement.

Transformative public procurement for innovation: ordinary, dynamic and functional capabilities

ABSTRACT Public Procurement for Innovation (PPI) requires multiple roles from the public administration according to the problem-solution space, delineated by the level of consensus around the definition of the needs of the contracting authority and the potential solutions to address them. Each role involves a distinct mix of capabilities, which we categorize as ordinary (i.e. doing things right), dynamic (i.e. doing the right things) and functional (i.e. addressing functional challenges). Following a Mutual Learning Exercise with procurement officials of 15 European countries, we identify 45 capabilities for contracting authorities to embrace the different facets of PPI as a transformative policy instrument.

Do Creativity Metrics from Design Research Correlate with Those from Psychology?

ABSTRACT In this paper, we explore measurements of design creativity through metrics related to the processes used in designing and relate them to the metrics used in psychology for idea creativity, ie, novelty and fluency. Our goal was to test the reliability of psychometric measures of creativity to assess creativity in team design. We studied 19 teams of 3 professional engineers that engaged in a one hour-long design task. Design tasks have a greater ecological validity than single repetitive tasks like the AUT and the RAT. Engaging in a design task involves a wide range of cognitive activities, which contribute to creative ideation and to expanding the design space. This study focused on the relationship between the teams’ design idea creativity and design behaviors during the task. We explored to what extent design collaboration between teammates, design evaluation and the co-evolution of the problem-solution space relate to the psychometric measures of idea creativity. Results suggest no specific trend in the correlation between collaboration and idea creativity as measured by the metrics used in psychology, while more cognitive focus on problem-solution co-evolution negatively correlates with these measures of idea creativity. The paper concludes with potential explanations for this lack of correlation.

Educating for complexity in Inclusive Design: From products to service systems

Inclusivity is the explicit and ultimate aim of Inclusive Design. Although the field continues to develop, it remains heavily influenced by product design logic and practices. Drawing on current discussions on how to educate designers for real-world challenges, we argue that Service Design offers a complementary knowledge set that supports novice inclusive designers in engaging with increasingly complex design problems. To this end, we describe how a Service Design workshop was introduced into an ongoing inclusive design project. The participants’ self-reported experiences were captured throughout and results show the workshop’s impact in five key aspects: re-framing the problem-solution space; encouraging a new design logic; challenging a heuristic approach to designing by systematizing the process; shifting views on disability from individual to structural and systemic levels; its effectiveness for student learning. We discuss how these findings have direct implications for building capacity to address increasingly complex design problems and for the future of Inclusive Design education.

Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.

Operationalizing contested problem-solution spaces: The case of Dutch circular construction

In shaping collective responses to societal challenges, we currently lack an understanding of how to grasp and navigate conflicting ideas on societal problems and potential solutions. The problem-solution space is an increasingly popular framework for conceptualizing the extent to which problem-oriented and solution-oriented views are divergent. However, this reflexive framework needs an operationalization to become useful in practice. We contribute to this debate by demonstrating how Q-methodology can be used to systematically identify, describe, and compare collectively held visions in relation to problems and solutions. We use the case of Dutch circular construction, and identify three conflicting imaginaries that inform us about disagreement and common ground. We conclude by discussing how policymakers can use different approaches to navigate contestation, presumably mobilizing actors for a collective response.

Multispace Evolutionary Search for Large-Scale Optimization With Applications to Recommender Systems

Large-scale optimization is vital in today’s artificial intelligence (AI) applications for extracting essential knowledge from huge volumes of data. In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This article, thus, proposes a new search paradigm, namely the multispace evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to five state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems as well as an AI application in e-commerce, i.e., movie recommendation.

Solution space exploration of low-thrust minimum-time trajectory optimization by combining two homotopies

Solving the low-thrust trajectory optimization problem with indirect methods has two main challenges including initial guessing and global optimum searching. To overcome these difficulties, two homotopies, including the thrust-homotopy and the longitude-homotopy, are combined to investigate the solution space of the low-thrust minimum-time trajectory optimization problem. The thrust-homotopy can be interpreted as a continuation on the maximum thrust magnitude, and the longitude-homotopy is proposed via a continuation on the final cumulative true longitude. Afterwards, the structure of the solution space is revealed by simulating a transfer scenario from the geostationary transfer orbit to the geostationary orbit. A bounded and smooth solution surface, where each point represents a solution with specific thrust and final cumulative true longitude, is formed by intersecting longitude-homotopy paths and thrust-homotopy paths. Based on the solution surface, the thrust-longitude-combined homotopic approach and a new hybrid homotopy are proposed to obtain local solutions by tracking homotopy paths from a solution of an easy problem, avoiding initial guessing. Finally, the longitude-homotopy is applied to search the global minimum-time solution from an obtained local solution.

Scheduling Disjoint Setups in a Single-Server Permutation Flow Shop Manufacturing Process

In this paper, a manufacturing process for a single-server permutation Flow Shop Scheduling Problem with sequence dependant, disjoint setups and makespan minimization is considered. The full problem is divided into two levels, and the lower level, aimed at finding an optimal order of setups for a given fixed order of jobs, is tackled. The mathematical model of the problem is presented along with a solution representation. Several problem properties pertaining to the problem solution space are formulated. The connection between the number of feasible solutions and the Catalan numbers is demonstrated and a Dynamic Programming-based algorithm for counting feasible solution is proposed. An elimination property is proved, which allows one to disregard up 99.99% of the solution space for instances with 10 jobs and 4 machines. A refinement procedure allowing us to improve the solution in the time required to evaluate it is shown. To illustrate how the properties can be used, two solving methods were proposed: a Mixed-Integer Linear Programming formulation and Tabu Search metaheuristic. The proposed methods were then tested in a computer experiment using a set instance based on Taillard’s benchmark; the results demonstrated their effectiveness even under a short time limit, proving that they could be used to build algorithms for the full problem.

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand, this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem. Guided by the idea of intuitive reasoning, we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions. First, Selective Attention Mechanism (SAM) is introduced to reduce the search space by adaptively focusing on the important information in the features. Then, Quantum Annealing (QA) algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution. Experiments on the parking lot location dataset in Luohu District, Shenzhen, show that the proposed method has improved the accuracy and running speed of the solution, and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.

A novel nomad migration-inspired algorithm for global optimization

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behavior of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability.

A residual-based message passing algorithm for constraint satisfaction problems

Message passing algorithms, whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages, provide a powerful toolkit in tackling hard computational tasks in optimization, inference, and learning problems. In the context of constraint satisfaction problems (CSPs), when a control parameter (such as constraint density) is tuned, multiple threshold phenomena emerge, signaling fundamental structural transitions in their solution space. Finding solutions around these transition points is exceedingly challenging for algorithm design, where message passing algorithms suffer from a large message fluctuation far from convergence. Here we introduce a residual-based updating step into message passing algorithms, in which messages with large variation between consecutive steps are given high priority in the updating process. For the specific example of model RB (revised B), a typical prototype of random CSPs with growing domains, we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational cost. Our approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.

A particle swarm optimizer with multi-level population sampling and dynamic p-learning mechanisms for large-scale optimization

Large-scale optimization, which has received much attention in recent years, is inherently a challenging problem. This paper proposes a particle swarm optimizer with multi-level population sampling and dynamic p-learning mechanisms to address the problem. The multi-level sampling mechanism in the proposed method is developed for supporting a balanced evolutionary search. The mechanism works by partitioning the particles of swarm into multi-levels based on their fitness before each generation of evolution. A subset of swarm is then dynamically sampled from the particles at various levels for evolution such that encouraging exploration at the beginning of evolution while exploitation towards the end of evolution, thus appropriately searching the space. The dynamic p-learning mechanism, on the other hand, is introduced to allow efficient particle learning while preserving the swarm diversity during evolution. In this mechanism, each particle is devised to learn from one of the top 100p% particles of the sub-swarm and the value of p associated with each particle is dynamically adjusted during evolution. By employing the above two mechanisms, the resulting method aims to appropriate search the solution space of large-scale global optimization problem for identifying the optimal or near-optimal solution. The performance of the proposed method has been evaluated on CEC’2010 and CEC’2013 benchmark suites for large-scale optimization and compared with related methods. Our results confirm the merits of the devised mechanisms in the proposed method. The results also show that our method can achieve a superior performance and outperform related methods.

A new green revolution or agribusiness as usual? Uncovering alignment issues and potential transition complications in agri-food system transitions

Agri-food system transitions are a considerable challenge requiring stakeholder alignment on what changes need to be made and how. When stakeholders do not agree on the goals or methods of a transition, this can be a serious obstacle to success. This paper analyzes 42 vision documents for the future of Dutch agriculture from a broad range of stakeholders to determine stakeholder alignment using an inductive coding approach. We identified 23 issues as the main challenges for the transition in these documents. We are the first to categorize them according to a recently proposed problem-solution space for wicked problems. Stakeholders were fully aligned in recognizing the problem for the majority of issues, but showed agreement on solutions for less than a quarter. For the issues of international orientation, sector size, and farm business models, we found a lack of consensus on the problem, indicating fundamental disagreement about the type of agricultural sector desired by stakeholders. The apparent consensus on environmental and social issues provides clear societal expectations for agronomic development and innovation, while the divergence on economic issues highlights the rift between growth-oriented paradigms and more holistic paradigms like agroecology. The crucial empirical novelty of this paper is that progress on environmental and social matters is restricted by divergent views on the economic characteristics of a future agri-food system, adding further complexity to mission-oriented transition and innovation policies.

The role of regional innovation systems in mission-oriented innovation policy: exploring the problem-solution space in electrification of maritime transport

ABSTRACT The innovation literature increasingly addresses grand challenges and transformative change. However, the issue of to what extent transformative change can build upon the resources, actors and institutions of existing innovation systems has not received sufficient attention. Against this background this paper aims to advance our understanding of the geographies and continuities of transformative change, by exploring the role of regional innovation systems in mission-oriented innovation. Based on an in-depth case study of electrification of ferries in Western Norway, the paper finds that the accomplishment of the mission was in large part due to the fact that it created new regional economic opportunities and built upon and mobilized existing regional resources, actors and structures. This mission re-orientation of an existing regional innovation system was characterized by (a) limited contestation, low complexity and low uncertainty about the technological battery-driven solutions pointed at, (b) multi-actor and multi-scalar agency and finally (c) asset modification of strong and pre-existing RIS structures, institutions and regulatory context.

A New Method for 3-Satisfiability Problem Solving Space Structure on Structural Entropy

Analyzing the solution space structure and evolution of 3-satisfiability (3-SAT) problem is an important way to study the difficulty of the solving satisfiability (SAT) problem. However, there is no unified analysis model for the spatial structure and evolution of solutions under different constraint densities. The analysis of different phase transition points and solution regions is based on different metric analysis models. The solution space of 3-SAT problem is obtained by planting strategy and belief propagation. According to the distribution of the influence of frozen variables on the solution, a label propagation algorithm based on planting strategy is proposed, is used to find the solution cluster, and then the structure entropy is used to measure its structure information. The structure entropy analysis model of 3-SAT problem solution space is established, and the unified analysis framework of solution space evolution and satisfiability phase transition is given. The experimental results show that the model is effective and can accurately analyze the evolution process of solution space and satisfiability phase transition, and verify the accuracy of interference phase transition point threshold predicted by long-range frustration theory.

Understanding climate health adaptation in practice: adaptation to the oak processionary moth in The Netherlands

Abstract Background Understanding of climate health adaptation and its organisation and implementation is limited. This presentation focuses on the adaptation to the oak processionary moth (OPM). The OPM spreads out northwards through Europe, and human contact leads to reactions in skin mucous membranes, conjunctivitis, pharyngitis and respiratory distress. Literature has focused largely on the ecology and epidemiology of the OPM. However, there is limited understanding of the organisation and implementation of climate health adaptation knowledge and coordinating an effective response. Methods In an explorative case study we examined the current OPM adaptation strategies in practice. Semi-structured interviews with 26 actors in The Netherlands were held to unravel the problem and the complexities involved in OPM adaptation. Results The results indicate that the context of OPM adaptation is multidimensional, given the involvement of many interdependent actors. At regional and local level early networks emerge aiming to improve the effectiveness of OPM adaptation. At the national level the Knowledge Platform OPM was set up, in which actors collaborate to collect and share knowledge and experience. Nevertheless, OPM strategies are based on ad hoc approaches with ambiguous tasks and expertise. In addition, actors have different perceptions and values concerning health, sustainability, risks and responsibilities influencing decision-making processes, collaboration and a coordinated approach. Conclusions The generation of knowledge and its translation into practical strategies calls for interdisciplinary cooperation in knowledge development. Climate health adaptation involves more than technical and organisational solutions alone. It also entails the development of a shared problem perception and solution space in which citizens are also engaged. Therefore, implementation of vertical and horizontal coordination climate health adaptation strategies are required.

An Agile Systems Engineering Analysis of Socio‐technical Aspects of a University‐built CubeSat

ABSTRACTSystem development teams often face challenges outside of pure technical problems. There are unpredictable circumstances in many areas including customer needs, personnel changes, and sociotechnical issues that can span across the development organization such as knowledge management and goal alignment. This paper presents the results of an exploratory case study on a university CubeSat team developing an Earth Observation satellite. Formal analysis of agile systems engineering helps improve success throughout the CubeSat lifecycle. We apply the INCOSE Agile systems engineering WG decision guidance method for applying agile system engineering method to identify areas in which the project organization can improve to become more agile in three specific problem spaces: customer problem space, solution space, and product development space. The analysis process led to valuable insights about how the project organization of an academic project differs from that of industry. Additionally, the results indicate that areas such as stakeholder management and support environment could be factors that would benefit more from agile responsiveness.

An Agile Systems Engineering Analysis of a University CubeSat Project Organization

AbstractUniversity CubeSat projects become popular in recent decades, and face challenges that include both technical and sociotechnical aspects. However, these teams often lack the infrastructure and resources for having effective systems engineering or project management which are beneficial for addressing these challenges and developing complex systems, such as satellites. In this paper we present the results of an exploratory case study of a university CubeSat team developing an Earth Observation satellite. The Agile Decision Guidance method was applied to pinpoint parts of the project organization that could benefit from agile methods in three specific areas: customer problem space, solution space, and product development space. The results drew attention to areas such as; stakeholder management, knowledge and information management, and the support environment, that could benefit from an agile approach. We outline some of the plans to move forward and how the team responded to the analysis. We also discuss if the method was appropriate for academic small satellite organizations and adaptations of the method made during the assessment.