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ISyE 8851: Topics in Manufacturing Modeling in Discrete Event Logistics Systems: Concepts, Methods, and Tools

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Goal for Seminar Comprehensive view Basic understanding Platform for further use or development Identify opportunities

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Rule #1 This is a discussion, not a lecture. The assumption is that everyone participating will have something worthwhile to contribute to the conversation What’s worthwhile? –A question –An insight –A source –An answer

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Rule #2 I will try to provide a framework, some ideas, some suggestions, some sources But…I am perfectly willing to change directions if that seems like the right thing to do in order to accomplish the goal So…I will depend on you to take ownership in the process and the outcome

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Questions for today What is modeling? What are discrete event logistics systems? How can we organize our exploration of modeling?

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What is modeling?

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Paradigm.....example, pattern (from Webster’s 7th New Collegiate) an overall concept accepted by most people in an intellectual community, as those in one of the natural sciences, because of its effectiveness in explaining a complex process, idea, or set of data (from Webster’s New World Collegiate)

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You can see two images in this picture: 1. A beautiful young woman, whose face is turned away from you, or 2. An old hag who has a wart on her nose Which did you see?

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The Lesson Is... That we are programmed to interpret stimuli in a particular way--by our paradigms or modeling archetypes In modeling, we depend upon particular modeling archetypes, and upon our ability to apply these archetypes appropriately and effectively; and we depend on our ability to learn new archetypes or develop new archetypes as appropriate

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Modeling in OR Formal Abstract Mathematical Computational

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How big is it? How round is it? Stator rotor

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What can we say? About the process that creates “diameters”? About the motors that we assemble?

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Turning Process Raw Rotors Machined Rotors “Assignable” variation “Random” variation

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Time Diameter

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Observations If I can estimate the variance (which I can, from the sample data) And if I can set the mean (which I can, by adjusting the process) Then I can “control” the probability that a randomly selected rotor and stator will not “fit”

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How many models have I used?

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One view of the modeling process

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REALITY OBSERVE INTERVENE NARRATIVE MODEL SYMBOLIC MODEL COMPUTATIONAL MODEL SYNTHETIC MODEL NORMATIVE MODEL PLANNING MODEL Directly through sight, feeling, hearing, tasting, smelling; Indirectly through verbal, visual, tactile, or textual representations A “story” describing a perception and/or an understanding of “reality”, and including a “question” An abstraction from the “story” using symbols, to which specific “meanings” are assigned, including operational transformations Reduction of the symbolic model to a form that permits computation, by assigning specific values to parameters, and performing indicated operations Application of the computational model to explore “what if” questions, by varying parameter values, seeking “optimum” values, or otherwise exploring alternatives A “design” for changing “reality” based on the synthesis of available data and models A description of the steps that must be/will be taken to implement the normative model Executing the planning model

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REALITY INTERVENE COMPUTATIONAL MODEL SYNTHETIC MODEL NORMATIVE MODEL PLANNING MODEL OBSERVE NARRATIVE MODEL SYMBOLIC MODEL The “modeling cycle”

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Sample Narrative Rotors and stators for electric motors are assembled and then machined to produce a finished diameter. The machining processes are sufficiently precise and repeatable that circularity of the turned rotors and stators is not a concern. However, due to a variety of unspecified conditions, there is meaningful variation in the diameters of the machined rotors and stators relative to their nominal dimensions. Each machining process (rotors or stators) has its own characteristics. In the final assembly of an electric motor, it is critical that there be adequate clearance between the rotor and the stator. The problem is to determine the nominal values for the diameters of rotors and stators, so that the number of failed assemblies is suitably small. It also maybe important to understand how large a gap can be expected between the rotor and stator. Costs of assembly failures and performance losses from excessive gaps should be considered.

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Symbolic Model For rotor R i the observed diameter is d( R i ) For stator S i the observed diameter is D( S i ) Assume d() and D() are Gaussian random variables

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Other Models Computational –cdf, probability statements Synthetic –Varying nominal, looking at estimated failures Normative –Applying criteria to select nominal Planning –How to implement results

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Important Observations The modeling process is iterative--you may repeat steps when you realize that something was omitted in a prior step The modeling process often employs specialized terminology

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Important Observations--cont’d Categories of terminology –Specific to a modeling technique, e.g., graphs & networks –specific to an analytic method, e.g., waiting line analysis, linear programming, network flow optimization, simulation –specific to an application domain, e.g., logistics, manufacturing, health systems, finance

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Important Observations--cont’d There is a huge repository of “standard” models that very often can be customized to a particular problem--so you need to be familiar with and conversant with these models –These standard models become the “paradigms” that affect how we “see” the problem (narrative OR real world)

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Important Observations--cont’d Modeling requires assumptions –restrictions, narrowing down from the narrative –elaborations, adding to the narrative Must always test the assumptions as part of validating the conclusions

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Important Observations--cont’d For a given symbolic model, there may be many possible computational models-- knowing which ones work well is important! –Also, for a given model, there may be a variety of applicable software packages; selecting and using them appropriately is important, as well.

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Important Observations--cont’d Normative models always require the application of criteria for selecting among alternatives –cost minimization, profit maximization, quality, speed, etc.

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Important Observations--cont’d Planning models require a keen understanding of the pragmatic considerations--organizational and operational –time and cost to implement change –difficulty of enlisting people

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REALITY INTERVENE COMPUTATIONAL MODEL SYNTHETIC MODEL NORMATIVE MODEL PLANNING MODEL OBSERVE NARRATIVE MODEL SYMBOLIC MODEL Experience Domain Knowledge Analytical Modeling Knowledge Business & Organizational Knowledge Decision Theory Knowledge

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What is a discrete event logistics system?

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Discrete Event Logistics Systems Robotic cell Warehousing automation Warehouse Factory Global supply network

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Robotic Workcell

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Warehousing Automation

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Warehouse

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300 mm wafer fab 1K tools 1K process steps 40K wspm 30 day MCT 25 wafers per lot 1600 lots WIP 10-20 km travel per lot $2-3 billion investment

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Global Supply Net

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Similarities Units of flow Conversion processes Transactions between entities Asynchronous Multi-product, multi-process, multi-route Dynamic input & output transport conversion delay

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Differences Scale and scope Dwell time Decision latency Response time

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Claim: We look at reality through our “methodology goggles” Linear programming: resources allocated among activities, subject to constraints Simulation: queues, resources, dispatch rules, seize/hold/release Factory Physics: bottleneck rate, critical WIP, congestion factor It’s easy to lose sight of the importance of, and difficulty of, modeling, as distinct from analysis

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How can we organize our exploration of modeling? Use a “systems engineering approach” to discrete event logistics systems.

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Rouse Construction of Systems Engineering Description –Of phenomena and their causes Prediction –Of phenomena Control –Of phenomena Design –Of systems to achieve desired phenomena

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Design is the “Holy Grail” If you can design it, then you must understand how to control it If you can control it, then you must understand how to predict it If you can predict it, then you must understand how to describe it Description is the “foundation”

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Plan Catalog all the modeling methods and tools that might be applied to each of these four phases of systems engineering Understand the modeling methods Characterize modeling methods with respect to what they do and don’t represent Identify opportunities—apply old methods in new ways; develop new methods

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Why? Models are how we think about DELS The more comprehensive our “model base” the more clearly we can think about DELS Clarity of thinking drives quality of results Your degree will be doctor of philosophy

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