# Chapter 7 Entity Transfer and Steady-State Statistical Analysis

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1 Chapter 7 Entity Transfer and Steady-State Statistical Analysis

2 What We ll Do... Types of Entity Transfers Resource-Constrained Transfers Transporters (Model 7.1) Conveyors Non-accumulating (Model 7.2) Accumulating (Model 7.3) Statistical analysis of steady-state simulations Warm up and run length (Model 7.4) Truncated replications (Model 7.5) Batching in a single run (Model 7.6) Automatic run-time confidence intervals via batch means 7-2

3 Types of Entity Transfers Connect Zero-delay Connection graphic vs. module Labels (no graphic) Route Non-zero-delay constant, r.v., expression Stations, animated Routes Fixed routes vs. entity-dependent Sequences Connect and Route both assume: No limit on number in transit at a time Entities have their own feet 7-3

4 Resource-Constrained Transfers Limit total number of entities in transit at a time Telecommunications (number of packets) Logistics (number of vehicles) Entities still have their own feet Model via existing constructs think creatively Model space on the road as a Resource Limit number of Units of this Resource Entity must Seize unit of space resource before beginning trip, Release it at end of trip The space resource must be defined via a Resources module if > 1 unit is required 7-4

5 Resource-Constrained Transfers (cont d.) Leave Data area of many logic modules Tran Out button Seize a unit of the space resource Priority (1 is highest, ) If no units are free, entity must stay where it is Option for Load Time Enter Data area of many logic modules Tran In button Release the unit of the space resource Option for Unload Time 7-5

6 Transporter Concepts Carts, fork lifts, trucks, wheelchairs, people, When entity is ready to go somewhere, it needs to be picked up and moved Use Transporters moveable resources Activities: Request, Transport, and Free Two types of Transporters Free-Path (we ll do) Travel time depends only on velocity, distance Ignore traffic jams and their resulting delays Guided (won t do) AGVs, intersections, etc. 7-6

7 Creating Transporters Transporter data module (Transfer panel) Transporter name (Cart in our model) Number of Units (2) Free Path vs. Guided (accept Free Path default) Distance Set (accept default name Cart_Dst) Velocity (50 feet/min.) Consistency of time and distance units across model Initial Position (accept Arbitrary default) Options... When Freed... (Remain Where Freed) Can have Schedules, Failures, Statistics Picture for animation, including Ride Point 7-7

8 Requesting a Transporter Entity is done at a station, wants to move on Leave Data area, Tran Out button Transfer Type: Request Transporter name (Cart) Rule (Smallest Distance) Applies when > 1 transporter is available Other options include Cyclical, Preferred Order, Random Priority of this request (1) Applies when demand for transporters exceeds supply Load Time (0.25 min.) 7-8

9 Entity Animation During the Transporter Request Process Use an Arena Storage (similar to a queue) Place to store entity to make it show in animation Get at via animate subdialog in most logic modules Leave for Next Station area Select Storage radio button Storage name (accept default) Check Unstore box to decrement Storage statistics Produces animation object that looks like an animated queue, except for default color (green) Can edit the storage animation just like a queue 7-9

10 Freeing a Transporter Entity has arrived at its next station on a Transporter Enter Data area of this station, Tran In button Select Free Transporter radio button Arena keeps track of which one, unit number Unload Time (0.25 min.) Transporter is now available, will position itself according to Options... When Freed in Transporter data module 7-10

11 Distances for Transporters Define contents of the Distance Set Cart_Dst Distances (in feet) moved by parts: F Order Release Blank cells: part movements that don t occur Place 11 Distance modules (Transfer panel) Beginning, Ending Stations Member of what Distance Set? Numerical Distance Units!! Direction is implied need not be symmetric To Cell 1 Cell 2 Cell 3 Cell 4 Exit System r Cell o Cell m Cell Cell Units!! 7-11

12 Animating Transporter Movement Part of Distance module Beginning, Ending stations First, move module handle to appropriate physical location (organize Distance modules maybe many) Then, drag animation (stations, line) where they belong Dragging module handle also moves animation Use Shift+Drag on handle to leave animation where it is 7-12

13 Animating Transporter Movement (cont d.) Line characteristics double-click on line # Points then drag to correct animation position Maybe Rotate/Flip entity as it moves, line Colors Parking Areas for Transporters Double-click on double circle, Points, Add, Like a queue Points vs. Line, Color, Rotate, Flip Drag around for realistic positioning There could be many animation stations for a single logical station, but you should have only one parking area per Station (delete others) 7-13

14 More Distances Empty Transporters Above Distances incomplete only for entities Transporters must also move when empty In general, n(n - 1) distances need definition for network with n nodes Some not possible Order Release to Exit System Additional distances to define (grayed): To Order Release Cell 1 Cell 2 Cell 3 Cell 4 Exit System Order Release F Cell r Cell o Cell m Cell Exit System

15 System Improvement (?): Replace Transporters with Conveyor Replace Transporters with a conveyor Loop conveyor to follow main path, clockwise Six entrance/exit points Load, Unload takes 0.25 minute Each part is 4 feet per side, but want 6 feet of conveyor space for clearance on corners Speed = 20 feet/minute Distances: Cell 1 Units!! 39 Cell Order Release Exit System Cell 4 39 Cell

16 Conveyor Concepts Entity to be conveyed must wait for space Conveyor consists of cells Equal size, constantly moving Entities might require multiple contiguous cells Must define cell size; tradeoff involved: Small cells: accurate model but slow execution Large cells: just the opposite! Entities Access space, Convey, and Exit Conveyor = series of linear Segments Each segment starts and ends with a Station Link to form loops, diverge points, converge points 7-16

17 Types of Conveyors Both travel in a single, irreversible direction Nonaccumulating: belt, bucket line, escalator Spacing between entities on it doesn t change Entire conveyor stops for entity Access/Exit if Load/Unload time is > 0 Accumulating: rollers, freeway Conveyor never stops moving If entity on it stops to Exit, other entities behind it are blocked and bunch up (entities ahead of it keep moving) When blockage ends, blocked entities go on but maybe not all at once (spacing requirements) 7-17

18 Change Transporters to Non- Accum. Conveyors (Model 7.2) Modify Transporter model (Model 7.1) Define Variables Load Time, Unload Time Delete Transporter module Add Conveyor module (Transfer panel) Name, Segment Set (define Segments later) Velocity, Cell Size definitions Initial status: Active Max Cells Occupied (for biggest possible entity) Type: Nonaccumulating Entity Size: Cell Size Conveyor statistics (check box) 7-18

19 Change Transporters to Non- Accum. Conveyors (Model 7.2) (cont d.) Every Tran Out: change Request to Access Conveyor = Loop Conveyor # of Cells to Access = 2 Load Time = Load Time (Variable name) Every Tran In: change Free Transporter to Exit Conveyor Unload Time = Unload Time (Variable name) Replace Distances with Segments (need 6) Beginning, Ending Stations, Segment Set Name Define distances for simulation Units = actual length (in feet), not number of cells Place lines for animation 7-19

20 Digression: Manual Change of Variable Values During Run Increase Load Time and Unload Time to 2 during run (see effect on conveyor stopping) Pause the run: Suppose you do this at time TNOW = Use Run Controller: Run/Command menu option or >ASSIGN Load Time = >ASSIGN Unload Time = 2 Resume the run: Close Controller window; OK for demos; questionable for real runs 7-20

21 Change Conveyors to Accumulating Type (Model 7.3) Modify Model 7.2 (with non-accumulating conveyors) Conveyor module Change Type to Accumulating Specify Entity Size to 4 to allow entities to bunch up closer than the 6 feet needed for an entity When a blockage is removed, entities will automatically re-space themselves to consume 6 feet each Verify effect by increasing Load/Unload times 7-21

22 Statistical Analysis of Steady- State Simulations Recall: Difference between terminating, steadystate simulations Which is appropriate depends on model, study Now, assume steady-state is desired Be sure this is so, since running and analysis is a lot harder than for terminating simulations Naturally, simulation run lengths can be long Opportunity for different internal computation order Can change numerical results Underscores need for statistical analysis of output 7-22

23 Warm Up and Run Length Most models start empty and idle Empty: No entities present at time 0 Idle: All resources idle at time 0 In a terminating simulation this is OK if realistic In a steady-state simulation, though, this can bias the output for a while after startup Bias can go either way Usually downward (results are biased low) in queueing-type models that eventually get congested Depending on model, parameters, and run length, the bias can be very severe 7-23

24 Warm Up and Run Length (cont d.) Remedies for initialization bias Better starting state, more typical of steady state Throw some entities around the model Can be inconvenient to do this in the model How do you know how many to throw and where? (This is what you re trying to estimate in the first place.) Make the run so long that bias is overwhelmed Might work if initial bias is weak or dissipates quickly Let model warm up, still starting empty and idle Simulate module: Warm-Up Period (time units!) Clears all statistics at that point for summary report, any crossreplication data saved with Statistics module s Outputs area (but not Time-Persistent or Tallies) 7-24

25 Warm Up and Run Length (cont d.) Warm-up and run length times? Most practical idea: preliminary runs, plots Simply eyeball them Statistics module, Time-Persistent and Tallies areas, then Plot with Output Analyzer Be careful about variability make multiple replications, superimpose plots Also, be careful to note explosions Model 7.4: modify Model 7.1 (Transporters) Run for 100,000 minutes, 10 replications, add WIP Save within-run WIP values, Cycle Times of parts 7-25

26 Warm Up and Run Length (cont d.) No explosions All seem to be settling into steady state Run length seems adequate to reach steady state Hard to judge warm-up

27 Warm Up and Run Length (cont d.) Crop plots to time 0-5,000 Plot dialog, Display Time from to Conservative warm-up: maybe 2,000 If measures disagreed, use max warm-up 7-27

28 Truncated Replications If you can identify appropriate warm-up and runlength times, just make replications as for terminating simulations Only difference: Specify Warm-Up Period in Simulate module Proceed with confidence intervals, comparisons, all statistical analysis as in terminating case Model 7.5: modify Model 7.4 (10 replications) Warm-Up Period = 2000 minutes Statistics module, Outputs area entries to save summary statistics across replications 7-28

29 Truncated Replications (cont d.) Output Analyzer, Classical Confidence Intervals Separate invocations due to different units Interpretation for steadystate expectations here Want smaller? More reps, same length Longer reps, same number of them 7-29

30 Batching in a Single Run If model warms up very slowly, truncated replications can be costly Have to pay warm-up on each replication Alternative: Just one R E A L L Y long run Only have to pay warm-up once Problem: Have only one replication and you need more than that to form a variance estimate (the basic quantity needed for statistical analysis) Big no-no: Use the individual points within the run as data for variance estimate Usually correlated (not indep.), variance estimate biased 7-30

31 Batching in a Single Run (cont d.) Break each output record from the run into a few large batches Tally (discrete-time) outputs: Observation-based Time-Persistent (continuous-time): Time-based Take averages over batches as basic statistics for estimation: Batch means Tally outputs: Simple arithmetic averages Time-Persistent: Continuous-time averages Treat batch means as IID Key: batch size for low correlation (details in text) Still might want to truncate (once, time-based) 7-31

32 Batching in a Single Run (cont d.) Picture for WIP (time-persistent): For observation-based Tallies, just count points To batch and analyze (details in text): Statistics module, Time-Persistent, Tally areas to save within-run records (could be big files) Output Analyzer, Analyze/Batch/Truncate or Warning if batches are too small for IID Get means.flt file; Classical C.I. as before 7-32

33 Automatic Run-Time Confidence Intervals via Batch Means Arena will automatically attempt to form 95% confidence intervals on steady-state output measures via batch means Half Width column in summary output Ignore if you re doing a terminating simulation Uses internal rules for batch sizes (details in text) Won t report anything if your run is not long enough (Insuf) if you don t have the minimum amount of data Arena requires even to form a c.i. (Correl) if you don t have enough data to form nearly-uncorrelated batch means, required to be safe 7-33

34 Recommendations, Other Methods What to do? for Steady-State Analysis Frankly, try to avoid steady-state simulations Look at goal of the study If you really do want steady-state First try warm-up and truncated replications Automatic run-time batch-means c.i. s Batch-means c.i. s by hand if necessary Other methods, goals Large literature on steady-state analysis Other goals: Comparison, Selection, Ranking 7-34

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