Design of a cooperative problem-solving system for enroute flight planning: An empirical study of its use by airline dispatchers

In a previous report, an empirical study of 30 pilots using the Flight Planning Testbed was reported. An identical experiment using the Flight Planning Testbed (FPT), except that 27 airline dispatchers were studied, is described. Five general questions were addressed in this study: (1) under what circumstances do the introduction of computer-generated suggestions (flight plans) influence the planning behavior of dispatchers (either in a beneficial or adverse manner); (2) what is the nature of such influences (i.e., how are the person's cognitive processes changed); (3) how beneficial are the general design concepts underlying FPT (use of a graphical interface, embedding graphics in a spreadsheet, etc.); (4) how effective are the specific implementation decisions made in realizing these general design concepts; and (5) how effectively do dispatchers evaluate situations requiring replanning, and how effectively do they identify appropriate solutions to these situations

End-To-End Strategic Planning Model Proposal with Artificial Intelligence

Organizations want to manage the future by being positioned correctly in the future with strategic planning, which is a part of strategic management. Strategic planning, which is a proactive approach, is a long-term type of planning and a case of forecasting based on data. It also contains many data that are complex, uncertain and linguistic, without statistical history. Real-life challenges, like strategic planning, are dynamic, unpredictable, and multi-criteria. It is known that the use of artificial intelligence techniques in such problems gives optimum results and minimizes human errors. At the same time, the use of multi-criteria-decision-making methods together with artificial intelligence techniques provides accurate and effective results to the decision maker. Artificial intelligence (AI) applications, which is the phenomenon of today, continue to enter all areas of our lives and its usage area is rapidly spreading. AI techniques are also used in the stages of strategic planning. There are applications of artificial intelligence (fuzzy logic) with SWOT analysis, which is a very common method to determine the current situation in strategic planning. Moreover, the number of models offering solutions with artificial intelligence techniques at all stages of planning from the very beginning to the end is low. In this study, a model proposal is presented for the preparation of end-to-end strategic planning with AI techniques. In the proposed model, strategic planning was examined in four stages as analyses of current situation, strategic concepts, assessment and evaluation. (Strategic planning was examined in four stages in the proposed model: current situation analyses, strategic concepts, assessment, and evaluation.) AI techniques are suggested for each stage. The proposed AI techniques have been chosen from among the most used techniques in the literature. The methods used are of 3 types. The first type is fuzzy logic (FL) which is one of the AI techniques, expert systems (ES), artificial neural networks (ANN), and genetic algorithms (GA). The second type is the combination of delphi technique used in data collection and multi-criteria decision-making methods, which is the decision-making methods, and FL. Fuzzy delphi and fuzzy multi-criteria-decision-making methods. The third type is the use of other AI techniques with FL. Fuzzy ES, fuzzy ANN. The aim of this study is to provide a perspective to organizations and experts that make strategic planning

Diagnosing Problems with the User Interface for a Strategic Planning Fuzzy DSS

Stratassist, a prototype fuzzy decision-support system (DSS) for strategic planning, was tested using the research hypothesis that subjects performing a task with the help of a DSS would produce better strategy statements than the control groups. Results supported this hypothesis. Two-factor analysis of variance revealed interaction effects between the treatment levels and the subject work experience levels that merited further study. Since the fuzzy DSS is meant to be used by a variety of planning analysts and executives, it was important to understand why these reactions occurred. Discriminant analysis of demographic information and followup questionnaire responses identified characteristic profiles with which to diagnose and correct problems in the user interface. These results suggested that a color graphics module under development may enhance user satisfaction