Multiple Alternative Solutions Research Articles

Profile of Teacher Decision-Making in Designing Mathematical Tasks Based on Teaching Experience

Objective: This research aims to describe the decision-making profile of mathematics teachers in designing mathematical assignments based on teaching experience. Method: The subjects of this study were mathematics teachers in junior high school education units with a bachelor's degree in mathematics education, including one senior teacher (ST) and one novice teacher (NT). The researcher used a task sheet for making a mathematical task design (TSMTD) and an interview guide as instruments for data collection. Results: The results showed that both novice and senior teachers identified problems by mentioning known information, but novice teachers tended to modify textbook assignments as materials for designing questions. In collecting information, beginner and senior teachers compared ideas from previous experiences, but senior teachers considered the context of the questions to be designed. Beginner teachers submitted problems by raising the theme of comparing values and transforming values with different resolution processes, while senior teachers considered several contexts and chose multiple alternative solutions. Both beginner and senior teachers checked all the questions designed and assessed the feasibility of the solutions generated from the task. Novelty: This research contributes to the understanding of the decision-making profile of mathematics teachers in designing assignments based on their teaching experience, highlighting the differences between novice and senior teachers in terms of problem identification, information collection, problem submission, and checking of designed questions.

The Diffusion of Problem and Solutions and Heterogeneity in Organizational Fields

Although research on diffusion processes has made significant progress, its focus on institutional practices and technological innovations implies an emphasis on solutions to organizational problems rather than on whether organizations recognize problems to begin with. Problem recognition is a precondition for adopting a solution, and behaviorally it is a diffusion process that is interdependent with solution diffusion. Moreover, a given problem typically has multiple alternative solutions that are in competition, while also providing legitimacy to each other by legitimizing the problem they solve. Diffusion analyses of 221 innovations that address eight problems in online newspaper publishing show that the adoption and abandonment of solutions by peers alter the likelihood of recognizing the problem and adopting the specific solution, as well as alternative solutions. This theory and empirical support have important implication for the study of diffusion processes of institutions and technological innovations alike, as it questions the assumption that practices have independent diffusion trajectories. Analysis of a solution without attention to problem recognition is problematic, especially if multiple solutions to the same problem exist.

An Interval-Valued Intuitionistic Fuzzy Model Based on Extended VIKOR and MARCOS for Sustainable Supplier Selection in Organ Transplantation Networks for Healthcare Devices

The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.

BIM–LCCA Integration for Road Pavement Maintenance

The use of building information modeling (BIM) for infrastructures is on the rise but the use of the life cycle cost analysis (LCCA) integrated within the BIM environment itself to analyze multiple design alternatives and set up a proper maintenance plan has not yet been developed. It is possible to integrate evaluation of maintenance and waste recovery strategies, minimizing costs and the use of raw materials. A visual programming tool was exploited to set up an application that applies the LCCA methodology to a road pavement project in the early design phase, returning the total maintenance cost updated to the year of construction, the number of maintenance interventions, and the residual value of accumulated fatigue damage. The execution of the LCCA tool dynamically updates the properties of the BIM model with the resulting pavement management plan and returns multiple outputs, giving to the user the possibility of making further assessments and store the data as needed. The developed application shows that BIM is a promising tool to implement more complex and useful analyses that can reduce the maintenance planning time and allow the designer to choose between multiple alternative solutions already in the early design phase, bringing consistent cost savings through the possibility of finding the minimum cost solution based on the specific project needs in compliance with the quality and safety criteria to be guaranteed for the road category.

Identification of multiple alternative solutions in structural model updating using multiple subswarm particle swarm optimization

The global optimal solution of structural model updating may not represent the real physical structure owing to modelling simplifications, measurement errors and uncertainties. This study proposes a multiple subswarm particle swarm optimization (MSPSO) to identify multiple alternative solutions in structural model updating. The MSPSO introduces two additional operators, a double angle operator and a multiple subswarm operator, into the standard particle swarm optimization (PSO). The former aims to identify the search range of possible solutions and the latter facilitates search efficiency after the search range has been located. Numerical and engineering applications, including two benchmark functions, a space truss structure and a large-span arch bridge, are used to examine the feasibility of the proposed method. The results indicate that MSPSO can identify multiple alternative solutions effectively while PSO only finds the global optimal solution, and these solutions provide more options for the selection of the best physical representation of the structure.

Clustering Documents Along Multiple Dimensions

Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author's gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the problem of identifying alternative clustering solutions by developing a Probabilistic Multi-Clustering (PMC) model that discovers multiple, maximally different clusterings of a data sample. Empirical results on six datasets representative of real-world applications show that our PMC model exhibits superior performance to comparable multi-clustering algorithms.

Solving distributed FMS scheduling problems with/ without breakdowns: Simulation optimization approach

As the survival rate in this competitive market goes very narrow, multiple alternative solutions are needed to be examined in their company’s logistics network. This process not only allows gaining insights about the implications of cost or other factors of available alternatives, it also allows us to think about many factors such as market openings, labour oriented manufacturing companies, high- tech products assisted labour force, transportation and communication costs too. As the companies shift their industries to different places where they are benefitted for their individual business as well as competitive forefront in international economic market. This may include one or multi-site manufacturing facilities or a vast network of manufacturing industries which are composed of factories which work on a sequential or a co-operative manner. Since it is always uneconomical to transfer a work to another factory from a factory which had faced unforeseen or technical issues, this method may help in providing justification in re-allocation of the job to factories in practical scenarios. This research work aims at solving distributed Scheduling problems using discrete event simulation approach.

Flexibilidade cognitiva e autoeficácia empreendedora dos líderes da Young Entrepreneurs’ Alliance e da federação Ibero-Americana de Jovens Empresários

Objective: To evaluate the relationship between Cognitive Flexibility, with its subscales, and Entrepreneurial Self-efficacy, with its dimensions, of leaders representing young entrepreneurs in the G20 Young Entrepreneurs Alliance and the Ibero-American Federation of Young Entrepreneurs. Methodology: The study has qualitative and quantitative approaches, as well as descriptive nature. The analytical and statistical processing were Qualitative Comparative Fuzzy Set Analysis (fsQCA); Correlation and Linear Regression. Results: The results pointed to significant positive correlation in hypotheses H2, H3, and H4, and rejected hypothesis H1, thus concluding that said leaders maintain dexterity in perceiving and generating multiple explanations and alternative solutions to uncertain, difficult, and/or new occurrences (cognitive flexibility), thus positively influencing the belief that their abilities are effective in carrying out courses of action necessary to create a new business or the development of an existing business (entrepreneurial self-efficacy). Theoretical/methodological contributions: The study contributes to the literature by expanding the knowledge about the relationship between cognitive flexibility and entrepreneurial self-efficacy, as well as, the research sample consisting of world leaders and the use of the fsQCA technique. Relevance/Originality: The research deepens the discussion on cognitive flexibility and entrepreneurial self-efficacy of leaders of young entrepreneurs, since how entrepreneurs think and act has become an indispensable issue to support entrepreneurial activities carried out independently or within organizations. Social contributions: The contribution lies in the evidence that there is a significant positive relationship between cognitive flexibility, which is an important problem-solving skill, along with entrepreneurial self-efficacy for understanding the strategies that entrepreneurs share with each other.

Metabolic flux configuration determination using information entropy.

Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli’s glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.

A cooperative system for metaheuristic algorithms

Optimization problems are defined as the functions whereby the target is to find the optimum state depending on the parameters that have certain limitations. In the field of optimization, the aim is to find from among multiple alternative solutions the optimal solution or approximate solution that provides all the restrictions. Metaheuristic is an extremely effective method to find approximate solutions to optimization problems. However, when metaheuristics are used, there occurs an algorithm selection problem. This problem involves decision-making about which algorithm is to be used to solve the existing optimization problem with maximum performance. The objective of this study is to use a cooperative system that combines different metaheuristics to successfully deal with algorithm selection problems. An intelligent combination of different metaheuristics is expected to provide more flexible, more efficient and more robust approaches. However, such a combination requires less precision. The combination is generated through a methodology designed with soft computing. In addition to the algorithm selection problem, the adjustment of algorithm parameters has significant importance in obtaining good results. For this reason, the cooperative system proposed in this study offers fine-tuning of parameters based on soft computing techniques.

Diverse discrete material optimization for multi-patch laminates under vibration environment

In practical engineering, the design scheme is generally a compromise solution that meets various requirements. In most cases, the traditional optimization method provides a single optimized solution, which may be prone to fail during the subsequent design stage because some unpredictable requirements may be not considered in the preliminary optimization process. For example, maximizing fundamental frequency is generally regarded as the optimization objective for aerospace structures under the vibration environment in the preliminary design stage. However, the optimized solution may fail for strength, buckling, or other requirements during the subsequent detailed design stage. Therefore, it is crucial to provide multiple alternative solutions for insurance. In this paper, a diverse discrete material optimization (DDMO) framework is proposed for multi-patch laminates. It can optimize the material topology layout and fiber orientations of composite structures simultaneously and provide multiple alternative solutions that have diversity in design space and different potential performance. In this paper, a diversity index for discrete variables is proposed and the discrete material optimization (DMO) method with the diversity index constraint is employed to perform the DDMO. Two illustrative examples are used to verify the effectiveness of the proposed optimization framework, including a simple example of a composite plate and a complex engineering example of an S-shaped curved shell. Results indicate that, the proposed method can provide multiple diverse alternative solutions with similar performance in the optimization objective, which are verified to have better potential performance than the single solution by the traditional single design method. Moreover, multiple design options by the diverse optimization method can contribute to reducing the probability of redesign and shortening the design cycle.

Site Selection for Hazardous Waste Using FuzzyLogic Combined With Analytic Hierarchy Process: ACase study in Nahavand, Iran

Hazardous wastes include various types of chemicals and other wastes generated from industrialand other sources. Because of the complexity of waste management systems, the selection of theappropriate solid waste landfill site requires consideration of multiple alternative solutions andevaluation criteria. This paper investigated the utilization of the fuzzy logic in combination withAnalytic Hierarchy Process (AHP) technique by applying GIS and IDRISI programming for choosinghazardous waste landfill site in Nahavand, Iran. Based on the real conditions of the study area, weconsidered economic, biological and topographical factors (12 criteria). The candidate sites weredetermined based on the criteria weights. The best location was determined via the fuzzy logic andthe AHP methods. This study found that the combination of fuzzy logic and the AHP method was thebest option for selecting a site for hazardous waste landfill. In the end, 6 suitable areas were selectedfor a hazardous waste landfill in the city. This study verified that the combination of the AHP methodwith fuzzy logic using GIS in our project is a powerful tool for solid waste landfill site selection.

A roadmap for applying qualitative comparative analysis in supply chain research

PurposeThe purpose of this paper is to introduce qualitative comparative analysis (QCA) to the field of supply chain management and provide a detailed roadmap that supply chain researchers can utilize when applying this methodology.Design/methodology/approachData collection focused on the evaluation of product returns management practices as perceived by business customers who operate in a supplier–customer context. In order to analyze the data using the QCA approach, a multi-step analysis was developed.FindingsThe results indicate five solutions that lead to high levels of customer satisfaction. The existence of multiple sufficient configurations for customer satisfaction indicates equifinality because multiple alternative solutions can lead to the same outcome.Research limitations/implicationsThe authors make a methodological contribution by applying the QCA method to the field of supply chain management and providing a detailed roadmap that supply chain researchers can utilize.Practical implicationsThe authors provide managers five different and novel combinations of antecedents that lead to higher levels of customer satisfaction.Originality/valueThis study offers supply chain researchers a better understanding of when it is appropriate to use QCA and how to apply this methodology. From a theoretical perspective, past studies focused exclusively on the “net effects” of these antecedents, thus, did not capture the complexity of the relationships between these various antecedents and customer satisfaction. This is a noteworthy contribution as it highlights the complexity of the amalgam of relationships and factors that impact customer satisfaction within the context of reverse supply chain.

Evaluation of Sustainable Site Selection Using Analytical Hierarchy Process for Hospitals: Karbala City as A Case Study

Site selection for a hospital location is one of the pivotal strategy- related decisions taken by the government. The selection of a suitable site for a hospital requires consideration of multiple alternative solutions and assessment factors. The present study aims at determine the optimum site out of three alternative sites to build a new hospital in Kerbala city. The main sustainability factors are; urban factors (including size, accessibility, restrictions, availability), environmental factors (including geomorphology, hydrology, vegetation, climate, other environmental factors) and economic factors (including service and utilities, cost) factors. The Analytical Hierarchy Process (AHP) as a multi criteria decision support system was adopted to find the weights of each factor and reach to select the most suitable site from three alternative sites. The results showed the site number (2) was the most sustainable site to construct the hospital project, where the alternative site records a biggest normal index of 0.419.

Virtual prototyping for maritime crane design and operations

This paper presents the implementation of the virtual prototyping system for maritime crane design and operations. The study is designed to bridge the following gaps in maritime crane system simulations. First, the virtual prototyping system introduces an open and flexible platform oriented to overall product and system design, modeling, simulation and visualization. Second, the virtual simulator for operations based on the proposed framework is reinforced by high fidelity models of physics and dynamics. The paper discusses the challenges in virtual prototyping of complex multi-domain systems from the perspectives of modern complex engineering system design, modeling and simulation of multi-domain dynamic systems, large set of data exchange for communication and visualization. The software architecture of the system is based on the application of the functional mock-up interface standard. This utilizes the current available modeling and simulation tools, and allows for the exchange and reuse of models. Simulations in a virtual environment permit the evaluation of multiple trade-offs and alternative solutions from early design stages. As a case study and verification, the knuckle boom crane systems were implemented. The virtual crane simulator proved the effectiveness of the proposed virtual prototyping system in solving the long-existing challenges in simulations of complex multi-domain systems.

Widespread neural oscillations in the delta band dissociate rule convergence from rule divergence during creative idea generation.

Critical to creative cognition and performance is both the generation of multiple alternative solutions in response to open-ended problems (divergent thinking) and a series of cognitive operations that converges on the correct or best possible answer (convergent thinking). Although the neural underpinnings of divergent and convergent thinking are still poorly understood, several electroencephalography (EEG) studies point to differences in alpha-band oscillations between these thinking modes. We reason that, because most previous studies employed typical block designs, these pioneering findings may mainly reflect the more sustained aspects of creative processes that extend over longer time periods, and that still much is unknown about the faster-acting neural mechanisms that dissociate divergent from convergent thinking during idea generation. To this end, we developed a new event-related paradigm, in which we measured participants' tendency to implicitly follow a rule set by examples, versus breaking that rule, during the generation of novel names for specific categories (e.g., pasta, planets). This approach allowed us to compare the oscillatory dynamics of rule convergent and rule divergent idea generation and at the same time enabled us to measure spontaneous switching between these thinking modes on a trial-to-trial basis. We found that, relative to more systematic, rule convergent thinking, rule divergent thinking was associated with widespread decreases in delta band activity. Therefore, this study contributes to advancing our understanding of the neural underpinnings of creativity by addressing some methodological challenges that neuroscientific creativity research faces.

Clustering stability for automated color image segmentation

Abstract Clustering is a well-established technique for segmentation. However, clustering validation is rarely used for this purpose. In this work we adapt a clustering validation method, Clustering Stability (CS), to automatically segment images. CS is not limited by image dimensionality nor by the clustering algorithm. We show clustering and validation acting together as a data-driven process able to find the optimum number of partitions according to our proposed color-texture feature representation. We also describe how to adapt CS to detect the best settings required for feature extraction. The segmentation solutions found by our method are supported by a stability score named STI, which provides an objective quantifiable metric to obtain the final segmentation results. Furthermore, the STI allows to compare multiple alternative solutions and select the most appropriate according to the index meaning. We successfully test our procedure on texture and natural images, and 3D MRI data.

Creating Alternatives for Stochastic Water Resources Management Decision-Making Using a Firefly Algorithm-Driven Simulation-Optimization Approach

Abstract: In solving complex water resources management (WRM) problems, it can prove preferable to create numerous quantifiably good alternatives that provide multiple, disparate perspectives. This is because WRM normally involves multifaceted problems that are riddled with incompatible performance objectives and contain inconsistent design requirements, which are very difficult to quantify and capture when supporting decisions must be constructed. By producing a set of options that are maximally different from each other in terms of their unmodelled variable structures, it is hoped that some of these dissimilar solutions may convey very different perspectives that may serve to address these unmodelled objectives. In environmental planning, this maximally different option production procedure is referred to as modelling-to-generate-alternatives (MGA). In addition, many components of WRM problems possess extensive stochastic uncertainty. This study provides a firefly algorithm-driven simulation-optimization approach for MGA that can be used to efficiently create multiple solution alternatives to problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. This algorithmic approach is both computationally efficient and simultaneously produces a prescribed number of maximally different solution alternatives in a single computational run of the procedure. The effectiveness of this stochastic MGA approach for creating alternatives in “real world”, environmental policy formulation is demonstrated using a WRM case study.

Decision making under uncertainty using PEES–fuzzy AHP–fuzzy TOPSIS methodology for landfill location selection

The location selection for landfill municipal solid waste is an important issue in waste management. Selection of the optimal location requires consideration of multiple alternative solutions and evaluation factors because of system complexity. Further, ranking of the alternative locations and selection of the most optimal and efficient locations for landfill waste are an important multi-criteria decision-making problem. In this study, the candidate locations for landfill are determined based on Political, Economical, Environmental and Social factors are assessed through decision-makers’ opinion and by the methodology that incorporates fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). Specifically, the fuzzy AHP technique is applied to determine the weights of selected criteria impacting the location selection process, and the fuzzy AHP is adapted to model the linguistic vagueness and ambiguity, which can also be expressed as triangular fuzzy numbers. Furthermore, fuzzy TOPSIS technique is employed to rank the alternative locations. The applicability of this methodology is demonstrated by a case study of landfill waste location selection in the region of Casablanca, Morocco, and the results are compared with other techniques. Finally, to complete the treatment, a sensitivity analysis is performed to examine the impact of the preferences given by decision-makers to choose the best location.

Environmental decision-making under uncertainty using a biologically-inspired simulation-optimisation algorithm for generating alternative perspectives

In solving many environmental policy formulation applications, it is generally preferable to formulate several quantifiably good alternatives that provide multiple, disparate approaches to the problem. This is because environmental decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult - if not impossible - to quantify and capture at the time when supporting decision models must be constructed. By generating a set of maximally different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This maximally different solution creation approach is referred to as modelling to generate-alternatives (MGA). This paper provides a biologically-inspired metaheuristic simulation-optimisation MGA method that can efficiently create multiple solution alternatives to environmental problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy of this stochastic MGA approach for environmental policy formulation is demonstrated using a municipal solid waste case study. It is shown that this new computationally efficient algorithmic approach can simultaneously produce the desired number of maximally different solution alternatives in a single computational run of the procedure.