Decision Making in Systems Engineering and Management (eBook)

Patrick J. Driscoll, Dale L. Henderson, Gregory S. Parnell (Herausgeber)

eBook Download: EPUB

2022 | 3. Auflage
576 Seiten
Wiley (Verlag)
978-1-119-90142-6 (ISBN)

Infos

DECISION MAKING IN SYSTEMS ENGINEERING AND MANAGEMENT

A thoroughly updated overview of systems engineering management and decision making

In the newly revised third edition of Decision Making in Systems Engineering and Management, the authors deliver a comprehensive and authoritative overview of the systems decision process, systems thinking, and qualitative and quantitative multi-criteria value modeling directly supporting decision making throughout the system lifecycle. This book offers readers major new updates that cover recently developed system modeling and analysis techniques and quantitative and qualitative approaches in the field, including effective techniques for addressing uncertainty. In addition to Excel, six new open-source software applications have been added to illustrate key topics, including SIPmath Modeler Tools, Cambridge Advanced Modeller, SystemiTool2.0, and Gephi 0.9.2.

The authors have reshaped the book's organization and presentation to better support educators engaged in remote learning. New appendices have been added to present extensions for a new realization analysis technique and getting started steps for each of the major software applications. Updated illustrative examples support modern system decision making skills and highlight applications in hardware, organizations, policy, logistic supply chains, and architecture.

Readers will also find:

• Thorough introductions to working with systems, the systems engineering perspective, and systems thinking
• In-depth presentations of applied systems thinking, including holism, element dependencies, expansive and contractive thinking, and concepts of structure, classification, and boundaries
• Comprehensive explorations of system representations leading to analysis
• In-depth discussions of supporting system decisions, including the system decision process (SDP), tradespace methods, multi-criteria value modeling, working with stakeholders, and the system environment

Perfect for undergraduate and graduate students studying systems engineering and systems engineering management, Decision Making in Systems Engineering and Management will also earn a place in the libraries of practicing system engineers and researchers with an interest in the topic.

Patrick J. Driscoll, PhD, is a Professor Emeritus of Operations Research and in the Department of Systems Engineering at the United States Military Academy. He was lead author and editor for the 3^rd edition of Decision Making in Systems Engineering and Management. He has over 30 years' experience teaching systems engineering, mathematics, and operational topics and is the former USMA Transformation Chair, Deputy Department Head, and Program Director for Systems Engineering.

Gregory S. Parnell, PhD, is a Professor of Practice in the Department of Industrial Engineering at the University of Arkansas and Director, System Design and Analytics Laboratory (SyDL), and Director of the M.S. in Operations Management and M.S. In Engineering Management programs. He previously taught at the United States Military Academy, the U.S. Air Force Academy, the Virginia Commonwealth University, and the Air Force Institute of Technology.

Dale L. Henderson, PhD, is a Principal Research Scientist at Amazon and former Assistant Professor in the Department of Systems Engineering at the United States Military Academy.

Textauszug

List of Figures

Figure 1.1 The stages of a system's life cycle.

Figure 1.2 Waterfall system life cycle model.

Figure 1.3 Spiral life cycle model with embedded risk assessment. Source: Boehm  [38].

Figure 1.4 Life cycle assessment of environmental costs of a washing machine [41].

Figure 1.5 Systems decision process used throughout a system life cycle. Source: D/SE, 2010.

Figure 1.6 Simplified risk management cycle affecting systems decisions.

Figure 1.7 Elements of decision quality (the corresponding SDP phases are annotated in the diagram).

Figure 1.8 Systems decision process.

Figure 1.9 Modeling and analysis flow for typical SDP application.

Figure 1.10 Spectrum of modeling purposes. Source: Pidd  [18]/with permission of Springer Nature.

Figure 1.11 Three required ingredients for proper problem definition.

Figure 1.12 Stakeholder salience types. Source: Matty  [48].

Figure 2.1 Ordered dependency tracing example.

Figure 2.2 Major capability acquisition chart [9].

Figure 2.3 Two abstract system models: graphical and mathematical.

Figure 2.4 A system representation with external feedback.

Figure 2.5 Three possible qualitative structures of a system kernel function.

Figure 2.6 Conceptualization of engineering management system.

Figure 2.7 Landmark® under‐counter wine refrigerators. Source: Landmark.

Figure 2.8 Two boundary options for the same BEV system of interest.

Figure 2.9 Degrees of internal understanding of a system.

Figure 2.10 Multilateral friendship systems in a social network. Source: Pidd  [17]/ John Wiley & Sons.

Figure 2.11 Three hierarchy levels of system spatial placement. (Courtesy of Kevin Hulsey Illustration Inc.).

Figure 2.12 Structural organization of a system with boundaries.

Figure 2.13 Alternative choice set evolution during a systems project.

Figure 3.1 The tradeoff for solution design goals.

Figure 3.2 The modeling process.

Figure 3.3 Rocket launch discrete event model.

Figure 3.4 Area under the function f(x) = x2.

Figure 3.5 Model characteristics.

Figure 3.6 Types of electric vehicles and their major components [8].

Figure 3.7 Battery electric vehicle (BEV) systemigram mainstay.

Figure 3.8 Battery electric vehicle (BEV) initial systemigram.

Figure 3.9 Directional dependency diagram relationships.

Figure 3.10 diagram and its adjacency matrix.

Figure 3.11 Example diagram for logistic system with associated mathematics.

Figure 3.12 The four domains of axiomatic design.

Figure 3.13 N2 and DSM representations for a four‐element interaction.

Figure 3.14 Activity DSM for a seven‐task process.

Figure 3.15 A binary component DSM showing physical dependencies for the AW101 Helicopter.

Figure 3.16 Clustered AW101 DSM showing potential component modules.

Figure 3.17 Translating the AW101 DSM into an adjacency matrix. (a) DSM with row input dependencies and (b) Adjacency matrix for DSM with row input.

Figure 3.18 AW101 digraph and module clustering. (a) AW101 DSM as an AdjacentGraph and (b) Potential modules via CommunityGraph.

Figure 3.19 Microsoft Excel CSV file and network layout after importing into Gephi. (a) CSV file with labels and (b) Gephi layout with CSV imported.

Figure 3.20 SMI bounding matrices for DSM. (a) Fully integral and (b) fully modular.

Figure 3.21 DSMs and DMMs commonly supporting system development.

Figure 3.22 Example DMM with design defects.

Figure 3.23 COVID causal loop diagram for major factors. Source: Kumar et al. [31]/with permission of Elsevier.

Figure 3.24 COVID stock and flow diagram supporting dynamic simulation.

Figure 3.25 COVID‐19 system behavior over time.

Figure 3.26 Generic IDEF0 model with three functional examples.

Figure 3.27 An A‐0 diagram for the Make Coffee function.

Figure 3.28 Level 2 representation of the Problem Definition phase of the SDP.

Figure 3.29 Level 0 model of a fast food restaurant.

Figure 3.30 Level 1 model of a fast food restaurant.

Figure 3.31 Level 2 model of the order process function for a fast food restaurant.

Figure 3.32 ProModel® anti‐ballistic missile simulation example [40].

Figure 3.33 Simulation types [41].

Figure 3.34 Simulation‐reality relationships.

Figure 4.1 Value‐focused versus alternative‐focused thinking sequences.

Figure 4.2 Flow of value‐focused and alternative‐focused thinking.

Figure 4.3 Dynamic approach to problem structuring. Source: Adapted from Corner et al. [5].

Figure 4.4 The systems decision process (SDP).

Figure 5.1 Concept diagram for Problem definition.

Figure 5.2 Online investigative pattern common to literature reviews.

Figure 5.3 An IDEF conceptualization of stakeholder analysis.

Figure 5.4 Two categories for FCR matrix after BEV research.

Figure 5.5 Hypothetical margin allocation example.

Figure 5.6 Affinity diagramming in action.

Figure 5.7 Example functional hierarchy for a battery electric vehicle (BEV).

Figure 5.8 Functional flow diagram for the top level of a NASA flight mission.

Figure 5.9 Partial functional hierarchy for curriculum management system (CMS).

Figure 5.10 Functional flow diagram for Function 4.0: perform mission operations.

Figure 5.11 IDEF0 Level 1 functional analysis example.

Figure 5.12 U.S. Dept of Defense technology readiness levels (TRL).

Figure 5.13 Integration tracking tool for sensor field experiments.

Figure 5.14 Example risk register used during the SDP.

Figure 5.15 Example of constructed risk outcome range scales.

Figure 5.16 Example P‐I table for 6 risk elements.

Figure 5.17 Specific P‐I table for risk element 3.

Figure 5.18 Example system life cycle cost profile.

Figure 5.19 Estimate of system cost variance over life cycle.

Figure 6.1 Example tradespace involving total value return and risk deviations.

Figure 6.2 Qualitative value model general structure.

Figure 6.3 EV qualitative value model.

Figure 6.4 Simplified value hierarchy for the rocket example.

Figure 6.5 Crosswalk of value functions to objectives.

Figure 6.6 Typical shapes for value functions.

Figure 6.7 Value function for minimizing the logistical footprint value measure for the rocket example.

Figure 6.8 EV example value function and 2D table using the difference method.

Figure 6.9 Value function for the example value increment method.

Figure 6.10 Value functions for the EV example.

Figure 6.11 EV AHP input matrix and normalized matrix with value measure weights.

Figure 6.12 EV AHP consistency ratio matrix and results.

Figure 6.13 Matrix for assigning value measure swing weights.

Figure 6.14 Value measure placement in swing weight matrix: upper left box rule.

Figure 6.15 EV swing weighting results.

Figure 6.16 AHP and swing weighting value measure weight results.

Figure 6.17 Swing weight matrix for CMS concept decision.

Figure 6.18 CMS qualitative value model with tiered weighting.

Figure 6.19 Value functions supporting the CMS concept decision.

Figure 6.20 Rocket design value functions.

Figure 7.1 Define, design, decide loops.

Figure 7.2 Concept map for the Solution design phase.

Figure 7.3 The ideation process.

Figure 7.4 The design thinking process steps. Source: Adapted from Yoshida [13].

Figure 7.5 Zwicky's morphological box. Source: Zwicky [19]/ With permission of Springer Nature.

Figure 7.6 From morphological box to alternatives.

Figure 7.7 Rocket design objectives structure.

Figure 7.8 Feasibility screening.

Figure...

Produkt-Details

• Erscheint lt. Verlag: 25.10.2022
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Control Systems Technology • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Engineering Management • Entscheidungsfindung • Management im Ingenieurwesen • Regelungstechnik • Systems Engineering & Management • Systemtechnik • Systemtechnik u. -management
• ISBN-10: 1-119-90142-1 / 1119901421
• ISBN-13: 978-1-119-90142-6 / 9781119901426