1,720,970 research outputs found

    SOLVING SCHEDULING PROBLEMS AS THE PUZZLE GAMES USING CONSTRAINT PROGRAMMING

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    Constraint programming (CP) is one of the most effective techniques for solving practical operational problems. The outstanding feature of the method is a set of constraints affecting a solution of a problem can be imposed without a need to explicitly defining a linear relation among variables, i.e. an equation. Nevertheless, the challenge of paramount importance in using this technique is how to present the operational problem in a solvable Constraint Satisfaction Problem (CSP) model. The problem modelling is problem independent and could be an exhaustive task at the beginning stage of problem solving, particularly when the problem is a real-world practical problem. This paper investigates the application of a simple grid puzzle game when a player attempts to solve practical scheduling problems. The examination scheduling and logistic fleet scheduling are presented as operational games. The game‘s rules are set up based on the operational practice. CP is then applied to solve the defined puzzle and the results show the success of the proposed method. The benefit of using a grid puzzle as the model is that the method can amplify the simplicity of CP in solving practical problems

    Solving Scheduling Problems as the Puzzle Games Using Constraint Programming

    No full text
    Constraint programming (CP) is one of the most effective techniques for solving practical operational problems. The outstanding feature of the method is a set of constraints affecting a solution of a problem can be imposed without a need to explicitly defining a linear relation among variables, i.e. an equation. Nevertheless, the challenge of paramount importance in using this technique is how to present the operational problem in a solvable Constraint Satisfaction Problem (CSP) model. The problem modelling is problem independent and could be an exhaustive task at the beginning stage of problem solving, particularly when the problem is a real-world practical problem. This paper investigates the application of a simple grid puzzle game when a player attempts to solve practical scheduling problems. The examination scheduling and logistic fleet scheduling are presented as operational games. The game‘s rules are set up based on the operational practice. CP is then applied to solve the defined puzzle and the results show the success of the proposed method. The benefit of using a grid puzzle as the model is that the method can amplify the simplicity of CP in solving practical problems

    Feasibility study of using mobile application to support triage and diagnosis clinical decisions for pediatricians: User-centered design approach

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    Background While there is some evidence in the literature demonstrating success in using a triage software application in ED, none of the solution was developed specifically to support a holistic decision of pediatricians in triage and diagnosis purposes to initiate the first treatment properly. To explore the usefulness and possibility of employing a digital-based solution to enhance clinician performance, the mobile application was developed and then assessed in different perspectives. Objective The primary objective of this study is to contribute implementation practice of an application to support pediatric triage and diagnoses. The secondary objective is to present the results of the preliminary evaluation of the application. Methods The application called Pedicmeter was developed. Formative tests with revisions were applied throughout the development phase. A number of summative extensive evaluations were also conducted to investigate the efficacy of the proposed method. The evaluation focused on measuring the ability of an application to support a pediatric staff’s decision to determine an overall severity level and disease diagnosis. Finally, the user’s (clinician's) satisfaction of using the application was measured. Results The application Pedicmeter enables clinicians to make more accurate decisions in determining emergency level of pediatric patients by 6.66%. The application accurately diagnosed a disease with 73.08% accuracy and 66.67% accuracy for respiratory and infectious diseases, respectively. The diagnostic information that the application suggested shows that it does have an influence on a clinician’s diagnosis. Using the app showed improvements in diagnostic accuracy for asthma, croup, sepsis, but it showed a decrease in the accuracy of a clinician's decision for pneumonia. The benefit of the application that satisfies the pediatricians the most is the helpfulness of the features of the application (86%), while the least satisfying factor was the required number of inputs (63%). Conclusion The developed application conceptually shows a promising opportunity to enhance clinicians’ decisions from the pilot study. However, the study also reveals further tweaks are required and unveils challenging issues and the concerns of clinician users when use the application. Further research will be conducted to investigate and determine the limiting factors and specific issues revealed by this study. Longitudinal data collection and analysis also need to be conducted to investigate the clinical implications
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