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A Systems Scientist’s Thoughts on Model-Based Systems Engineering Wayne Wakeland, PhD Systems Science Program Portland State University.

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Presentation on theme: "A Systems Scientist’s Thoughts on Model-Based Systems Engineering Wayne Wakeland, PhD Systems Science Program Portland State University."— Presentation transcript:

1 A Systems Scientist’s Thoughts on Model-Based Systems Engineering Wayne Wakeland, PhD Systems Science Program Portland State University

2 The Nature of Systems Dynamic Connected/coupled Governed by feedback Boundaries are artificial (often permeable) Complex & non-linear History dependent Edge of chaos Self-organizing Self-replicating (living systems) Adaptive/evolutionary Characterized by trade- offs Counterintuitive Policy resistant Emergent

3 In complex systems, cause and effect are often distant in time and space We may act to produce short-term benefits and long- term costs; we forget about delay

4 The solution to one problem may cause another problem (unintended results) Artist Gary Larson Example: The ”Green Revolution” agricultural technologies were introduced into Asia in the late 1960s as a solution for food insecurity. Decades later, they have proved detrimental in terms of biodiversity loss, increased use of agro-chemical based pest and weed control, water logging, salinization and land degradation. Slide adapted from LEAD International and Sustainability Institute

5 Events Patterns of Behavior Systemic Structure Mental Models The Iceberg: EVENTS – PATTERNS – STRUCTURE REACT ANTICIPATE DESIGN Increased leverage and opportunities for learning What happened? What has been happening? Why has this been happening? How can I improve the performance of the system?

6 Systems Science Methods Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results

7 Key Concepts and Principles A system consists of elements & relationships, with specific purpose/goal/function Whole > sum of the parts Structure causes behavior Circular causality – Outputs influence inputs; cannot separate cause and effect Mental models (often hidden) shape our thinking Systems Archetypes (common structures & behaviors) – E.g., Fixes that fail, Shifting the Burden, Success to the Successful, Limits to Growth, Tragedy of the Commons And much more (far too much to cover this morning) – Complex adaptive systems, living systems, open systems, structural coupling, autopoiesis, adaptation, resilience, evolution, …


9 Systems Thinking Seeing the forest and the trees Interconnectedness Thinking dynamically – Behavior over time – Delayed impacts/consequences Thinking closed loop (vs. linear causality) Endogenous thinking (system as cause) Thinking operationally – How things actually actually work

10 Useful Communications Tools: Causal Loop Diagrams

11 Example: Fixes that Fail Archetype The story: due to budget problems, spending on maintenance decreases, which balances the budget…BUT, over time, breakdowns increase, forcing more spending, which stresses the budget even worse than before!

12 Iteration in Systems Design/Intervention Processes

13 Specific Modeling Methods System dynamics – Focuses on modeling the underlying feedback structures with differential equations – Equation are solved to simulate behavior over time Discrete system simulation – Uses a Monte Carlo approach to analyze how the variety/randomness impacts system performance – Often emphasizing business operations and processes, especially in manufacturing and supply chain logistics Agent based simulation – Used to study how low-level interactions between individual agents influences overall system behavior/performance

14 System Dynamics Example Project Management – Brooks’ Law: “Adding manpower to a late software project makes it later” SD has been used to simulate complex projects and evaluate potential decisions, actions, policies

15 Typical Project “Disasters” The Channel Tunnel -- original estimate, $3 billion; final cost, $10 billion Boston’s “Big Dig” -- original mid- 1980’s estimate, $2.5 billion; latest estimate, $14.5 billion (9/2001) Aircraft development -- nearly double initial estimate New Car Development -- original plan, 400 person-years of effort; final cost, 800 person-years

16 Project Behavior Over Time





21 Discrete System Simulation Detailed, step-by-step emulation of the flow of entities through the system – With uncertain arrivals, processing times, and/or "routing" (branching) The computer monitors each simulated entity as simulated time proceeds – Enter system, move thru, according to the various probability fns. governing timing and sequence of events The computer also records pertinent data regarding the simulated entities and servers – wait times, throughput, queue lengths, process times, utilization… Creates a synthetic "sample" of system performance data Sample data is then analyzed statistically

22 Types of Problems DSS can Address Performance issues in existing systems – Long waits, high inventory, poor utilization of resources, low throughput Need to estimate performance of a system under design What Might One Learn? – Where the bottlenecks are and how they might be alleviated – How to improve flow, reduce queues and wait times, and increase utilization & throughput – The optimal number of servers, queues, buffers, etc. – Effective operating rules or policies

23 Examples of Discrete System Simulation Manufacturing facilities Bank operations Airport operations (passengers, security, planes, crews, baggage) Transportation/logistics/ distribution operations Hospital facilities ( emergency room, operating room, admissions) Computer network Freeway system Business process (e.g., insurance office) Criminal justice system Chemical plant Fast-food restaurant Supermarket Theme park Emergency Response system

24 Project for Systems Science 527 Class, Spring, 2011

25 Manufacturing Process Design Drawing

26 DSS model animation (closely mimics the actual system) The model contains complex logic regarding: A) Different fault occurrences, B) Part filling requirements, and C) Realistic variations seen in complex assembly processes

27 Model Results The simulation showed the behavior of the proposed new automated system It suggested that given expected faults, operator utilization will be 45-55% Thus, if the operator must load parts, do audits, and perform fault correction, they could not handle two machines, as would be needed to achieve the cost targets

28 Agent Based Simulation More of a stretch for systems engineering than the others… Key Features – Agents – Environment – Rules – Spatial aspects – Can reflect heterogeneity of individuals

29 Key ABS Concepts Decentralized control – Bottom up as opposed to top down Emergence Self-organization Evolutionary considerations Examples – Spread of Forest Fires – Flocking – Crowd behavior – Ants (and how ants can find optima) – Network effects

30 Crowd Crush Model The problem: crowd panics Sheffield, England 1986, 96 dead Phnom Phen, Cambodia, November 23, 2010: 347 dead Duisburg, Germany, July 25, 2010,19 dead This model was developed as class project Alexandra Nielsen, Systems Science 525, Fall 2010

31 Crowd Crush: defined Die of asphyxiation, not blunt force trauma Can die standing Warning of a crush Surrounded on all sides More than 4 people per square meter Force to kill The force of 5 people pushing on one person can break a rib, collapse a lung, smash a child's head

32 Simulation purpose Discover why some crowds are lethal and others not Can crowd deaths occur in non-aggressive crowds? Does aggression or reactivity (jostling) have a greater impact on crowd deaths? Is there some combination of factors that is reliably lethal? (So we can avoid it) What interventions can prevent deaths? Is there a critical density after which nothing will work? Netlogo model…

33 Model Testing Interventions Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead Validate vs. anecdotal evidence Wal-Mart door rush Cambodia see a “body” → panic Opening door in a crowd → death (Barnsley Public Hall disaster)

34 Applicability and Limitations Large crowds, single doorway Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd dynamics Limited by simplifying assumptions (extremely simple) No falling Only forward motion No groups, altruism, variability in agents Forces not vectors, not true physical force

35 Findings Don't allow a huge build up, then open a door Closing the gates before clearing the corridor helps, but not much Do anything you can to prevent panic

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