Operational Decision Making

2015 ◽  
pp. 257-271
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Theory ◽  
Energy Management ◽  
Large Scale ◽  
Additional Material ◽  
Time Compression ◽  
Complex Process ◽  
Operational Systems ◽  
Operational Decision Making

Additional material on decision making in operational systems is presented here. This material would be most useful for researchers engaged in the conceptual design of onboard decision support systems. Decision making is a complex process. Over the years much has been written about decision theory but very little attention has been paid to decision making under increased time compression. Also, additional complexity is introduced by having to deal with large-scale dynamic systems and their attendant trajectory and energy management demands. We discuss DODAR and FORDEC and their limitations. Operational decision making is a risk-driven model that triggers pilots' responses, actions, and decisions by changing the aircraft's position within the risk envelope. This material can form the basis of a more complete picture of the state-of-the-art decision theory and what useful aspects and insights we can use operationally.

Mission Critical Events

2015 ◽  
pp. 38-49
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
Large Scale ◽  
Problem Definition ◽  
Analytic Structure ◽  
Critical Events ◽  
Operational Conditions ◽  
Time Compression ◽  
Operational Decision Making ◽  
Mission Critical

Mission critical events are changing operational conditions that will have a significant impact on the mission. If they are specified correctly, one can begin to design meaningful crew station responses. This chapter is about how to make decisions that are appropriate for the environment; in this case, under increased time compression. The theoretical focus of decisions shifts the conceptual design of the decision analytic structure forward to the problem definition stage. In large-scale dynamic systems, getting the problem right is often the most difficult task of the operator and operational manager. Operational decision making (ODM) stands in visible contrast to conventional decision making, and conventional decision theory, in that among all classes of decisions, an operational decision is singular, and contains a number of unique components.

Operational Decision Making in Aviation

2015 ◽  
pp. 15-37
Keyword(s):  
Decision Making ◽  
Critical Thinking ◽  
Theory And Practice ◽  
Time Compression ◽  
Conflicting Information ◽  
Body Of Knowledge ◽  
Operational Systems ◽  
Operational Decision Making

Operational decision making, sometimes referred to as decision making in operational systems, is singular among all other classes of decisions. The type of decision used in operational systems is known as an operational decision and is addressed by the theory and practice of operational decision making (ODM). ODM is a body of knowledge and a system of thought, similar in many respects to critical thinking, but with some important differences. They are that a decision must often be made under increased time compression, it must be made with incomplete of conflicting information, and the consequences of a poor decision could be catastrophic. This chapter provides a brief overview of this important subject. More in-depth treatments follow in later chapters.

scholarly journals State-of-the-Art Prescriptive Criteria Weight Elicitation

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Mona Riabacke ◽  
Mats Danielson ◽  
Love Ekenberg
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Theory ◽  
Decision Analysis ◽  
Analytical Methods ◽  
State Of The Art ◽  
Criteria Weights ◽  
Decision Making Processes ◽  
Method Of Assessment ◽  
Descriptive Theories

Comparatively few of the vast amounts of decision analytical methods suggested have been widely spread in actual practice. Some approaches have nevertheless been more successful in this respect than others. Quantitative decision making has moved from the study of decision theory founded on a single criterion towards decision support for more realistic decision-making situations with multiple, often conflicting, criteria. Furthermore, the identified gap between normative and descriptive theories seems to suggest a shift to more prescriptive approaches. However, when decision analysis applications are used to aid prescriptive decision-making processes, additional demands are put on these applications to adapt to the users and the context. In particular, the issue of weight elicitation is crucial. There are several techniques for deriving criteria weights from preference statements. This is a cognitively demanding task, subject to different biases, and the elicited values can be heavily dependent on the method of assessment. There have been a number of methods suggested for assessing criteria weights, but these methods have properties which impact their applicability in practice. This paper provides a survey of state-of-the-art weight elicitation methods in a prescriptive setting.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

Pragmatism in Decision Support System Research

2021 ◽  
pp. 1471-1493
Author(s):  
Mohammad Tafiqur Rahman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Humanitarian Logistics ◽  
Knowledge Based ◽  
Relief Distribution ◽  
Distribution Process ◽  
Complex Knowledge

Decision making on relief distribution is a complex multidisciplinary task in humanitarian logistics. It incorporates decision makers from different but related problem areas. The failure to perform assigned decision-making tasks in any area makes the entire system unstable and delays the relief distribution process. An organized, well-planned, and practical decision support system (DSS) can assist practitioners in making rapid decisions on delivering relief items. Hence, DSS researchers in humanitarian logistics require rigorous thinking, close and critical analysis, and the identification of challenges to conduct research or validate the generated knowledge properly. To perform such complex knowledge-based tasks, the philosophical understanding of DSS in the humanitarian context is necessary. After analyzing the commonly used philosophical paradigms, this research identifies the pragmatic approach as the adequate support for solving decision-making problems in relief distribution during large-scale disasters.

The Implementation of Large-Scale Decision-Making Support Systems

2010 ◽  
pp. 1071-1083
Author(s):  
Manual Mora ◽  
Ovsei Gelman ◽  
Guisseppi Forgionne ◽  
Francisco Cervantes
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Large Scale ◽  
Support Systems ◽  
Model Based ◽  
Knowledge Based ◽  
Decision Making Support

This article reviews the literature-based issues involved in implementing large-scale decision-making support systems (DMSSs). Unlike previous studies, this review studies holistically three types of DMSSs (model-based decision support systems, executive-oriented decision support systems, and knowledge-based decision support systems) and incorporates recent studies on the simulation of the implementations process. Such an article contributes to the literature by organizing the fragmented knowledge on the DMSS implementation phenomenon and by communicating the factors and stages involved in successful or failed large-scale DMSS implementations to practitioners.

scholarly journals APLIKASI PENDUKUNG KEPUTUSAN PENENTUAN PROMOSI JABATAN MENGGUNAKAN METODE PROFILE MATCHING (STUDI KASUS DI PT. PETROKIMIA GRESIK)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Wimpi Sancaka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Gap Analysis ◽  
Assessment System ◽  
Decision Making Process ◽  
Matching Method ◽  
Profile Matching

Human resources play an essential role in helping companies to achieve their vision and mission. As a large scale company, PT Petrokimia Gresik obviously needs to invest in Employees’ Performance Assessment System. It could act as a decision-making tool or a measure to evaluate and assess employees' performance at work so that theemployees’ promotion would be moreobjective and organized. Decision support system could be used to reduce the subjectivity in decision-making process. The decision support system that uses profile matching method or competency gap analysis was created based on the data which refers to the decree of the board of directors issued by PT Petrokimia Gresik. This system analyzes and assesses employee’s competencies by grouping and calculating core factors and secondary factors in each variable. The output of the calculation is a ranking of the candidates. By implementing decision support system which uses Profile Matching method, it assists company decision-making process in promotion decision based on employees’ competency scores more optimally.

Decision Making in Complex Environments

2017 ◽  
Vol 4 (2) ◽  
pp. 1-14
Author(s):  
Kevin M. Smith
Keyword(s):  
Decision Making ◽  
Performance Optimization ◽  
Large Scale ◽  
Bayesian Probability ◽  
Complex Environments ◽  
Bayesian Theorem ◽  
Skill Sets ◽  
Technology Applications ◽  
Operational Decision Making ◽  
And Performance

Bayesian probability theory, signal detection theory, and operational decision theory are combined to understand how one can operate effectively in complex environments, which requires uncommon skill sets for performance optimization. The analytics of uncertainty in the form of Bayesian theorem applied to a moving object is presented, followed by how operational decision making is applicable to all complex environments. Large-scale dynamic systems have erratic behavior, so there is a need to effectively manage risk. Risk management needs to be addressed from the standpoint of convergent technology applications and performance modeling. The example of an airplane during takeoff shows how a risk continuum needs to be developed. An unambiguous demarcation line for low, moderate, and high risk is made and the decision analytical structure for all operational decisions is developed. Three mission-critical decisions are discussed to optimize performance: to continue or abandon the mission, the approach go-around maneuver, and the takeoff go/no-go decision.

scholarly journals Formula-E race strategy development using artificial neural networks and Monte Carlo tree search

2020 ◽  
Vol 32 (18) ◽  
pp. 15191-15207 ◽  
Author(s):  
Xuze Liu ◽  
Abbas Fotouhi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Energy Management ◽  
Large Scale ◽  
Prediction Models ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Fast Prediction

Abstract Energy management has been one of the most important parts in electric race strategies since the Fédération Internationale de l’Automobile Formula-E championships were launched in 2014. Since that time, a number of unfavorable race finishes have been witnessed due to poor energy management. Previous researches have been focused on managing the power flow between different energy sources or different energy consumers based on a fixed cycle. However, there is no published work in the literature about energy management of a full electric racing car on repeated course but with changeable settings and driving styles. Different from traditional energy management problems, the electric race strategy is more of a multi-stage decision-making problem which has a very large scale. Meanwhile, this is a time-critical task in motorsport where fast prediction tools are needed and decisions have to be made in seconds to benefit the final outcome of the race. In this study, the use of artificial neural networks (ANN) and tree search techniques is investigated as an approach to solve such a large-scale problem. ANN prediction models are developed to replace the traditional lap time simulation as a much faster performance prediction tool. Implementation of Monte Carlo tree search based on the proposed ANN fast prediction models has provided decent capability to generate decision-making solution for both pre-race planning and in-race reaction to unexpected scenarios.

Decision Making in Complex Environments

2021 ◽  
pp. 453-466
Author(s):  
Kevin M. Smith
Keyword(s):  
Decision Making ◽  
Performance Optimization ◽  
Large Scale ◽  
Bayesian Probability ◽  
Complex Environments ◽  
Bayesian Theorem ◽  
Skill Sets ◽  
Technology Applications ◽  
Operational Decision Making ◽  
And Performance

Bayesian probability theory, signal detection theory, and operational decision theory are combined to understand how one can operate effectively in complex environments, which requires uncommon skill sets for performance optimization. The analytics of uncertainty in the form of Bayesian theorem applied to a moving object is presented, followed by how operational decision making is applicable to all complex environments. Large-scale dynamic systems have erratic behavior, so there is a need to effectively manage risk. Risk management needs to be addressed from the standpoint of convergent technology applications and performance modeling. The example of an airplane during takeoff shows how a risk continuum needs to be developed. An unambiguous demarcation line for low, moderate, and high risk is made and the decision analytical structure for all operational decisions is developed. Three mission-critical decisions are discussed to optimize performance: to continue or abandon the mission, the approach go-around maneuver, and the takeoff go/no-go decision.

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