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Chapter5-ModelingDetailedOperations

Course: ECON 102, Spring 2012
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5 Last Modeling Detailed Operations Chapter revision August 20, 2009 Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 1 of 51 What Well Do ... Model 5-1: Simple call center Lower-level modeling, Advanced Process panel Three-way decisions, Variables, Expressions, Storages Blocks panel Terminating vs. steady-state operation Logical (fake) entities Terminating Condition in...

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5 Last Modeling Detailed Operations Chapter revision August 20, 2009 Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 1 of 51 What Well Do ... Model 5-1: Simple call center Lower-level modeling, Advanced Process panel Three-way decisions, Variables, Expressions, Storages Blocks panel Terminating vs. steady-state operation Logical (fake) entities Terminating Condition in Run > Setup Model 5-2: Enhanced call center Nonstationary Poisson arrival process Sets Resource, Counter New Statistic data module Types Counter, Time Persistent Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 2 of 51 What Well Do ... (contd.) Model 5-3: Enhanced call center with more output performance measures New Statistic data module Type Output Additional variable resources look at staffing levels Model 5-4: (s, S) inventory Not queueing Choose to use low-level Blocks, Elements panels (SIMAN) Can be done with higher-level panels Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 3 of 51 Model 5-1: Simple Call Center Setup One phone number for customers to call in to 26 trunk lines, one needed for each call (incoming or outgoing, talking or on hold) Arriving call finding no free trunk lines gets busy signal, goes away Calls arrive with interarrivals ~ EXPO (0.857) min. Count number of such rejected calls First call arrives at time 0 Three incoming call types Initial recording to decide ~ UNIF (0.1, 0.6) min. Tech support (76%), sales (16%), order status (8%) Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 4 of 51 Model 5-1: Simple Call Center Setup (contd.) Tech-support calls For product type 1 (25%), 2 (34%), or 3 (41%) Recording/select time ~ UNIF (0.1, 0.5) Needs qualified tech-support person Two for type 1, three for type 2, three for type 3 No crossover to another type ... will allow this in Model 5-2 Separate FIFO queues for each type Conversation time ~ TRIA (3, 6, 18) min. for all types Then leaves system Sales calls All the same Four sales staff, all the same One FIFO queue feeding all sales staff Conversation time ~ TRIA (4, 15, 45) Then leaves system Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 5 of 51 Model 5-1: Simple Call Center Setup (contd.) Order-status calls All the same Handled automatically by phone system Conversation time ~ TRIA (2, 3, 4) After conversation, 15% of callers opt to talk to a person No limit on number in process at a time, except for trunk-line limit Routed to sales staff, conversation lasts an additional TRIA (2, 3, 4) Sales calls have higher priority (non-preemptive) Center receives calls 8am 6pm Must terminate arrival process at 6pm Operate past 6pm if necessary to flush out all calls Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 6 of 51 Model 5-1: Simple Call Center Setup (contd.) Output performance measures Number of calls attempted, rejected, completed By call type total time in system By resource time on hold, number of calls on hold Resource utilization of personnel, trunk lines Terminating or steady-state Time frame of interest for each replication Terminating specific starting, stopping conditions (this model) Stopping conditions could be of several forms fixed time, count, condition (here) Steady-state output performance measures are a limit as simulated time Choice usually depends on intent of study, not on model logic Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 7 of 51 Model 5-1: Simple Call Center Modeling Panels Basic Process Advanced Process Entity movement, material handling Blocks, Elements Smaller building elements, other functions, more detail Advanced Transfer Highest, fastest modeling level, usually the place to start Lowest modeling level, SIMAN simulation language Repeats some capabilities of higher-level panels Some functions available only here Other special-purpose panels License-dependent Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 8 of 51 Model 5-1: Simple Call Center Data Structure Re-use data in several places Arena (global) Variables Define once, global to whole model Redefine once modeling generality, user efficiency Store numbers (not formulas) Define, initialize in Variable data module (Basic Process) Can change during run (Assign module, other ways) Scalar, 1-d array (vector), 2-d array (matrix) Arena (global) Expressions Store formulas (as well as numbers, but cant change) Use math ops, numbers, random variates, Attributes, Variables, ... Define in Expression data module (Advanced Process) Scalar, 1-d array (vector), 2-d array (matrix) Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 9 of 51 Model 5-1: Simple Call Center Arrivals, Direct to Service Create attempted calls Entity type Incoming Call, change later Max Arrivals = MaxCalls, Variable initialized to 999999 Entities per Arrival = CallsPerArrival, Variable initialized to 1 At 6pm (time 600 minutes) change this to 0 to kill arrivals ... later Entity data module At 6pm (time 600 minutes) change this to 1 to stop arrivals ... later Incoming Call Entity Type already there For Initial Picture, select Picture.Black Ball Record module for an attempted call Add 1 to Counter Name Attempted Calls Results Category Overview report, User Specified More detailed description mouse over modules, read Data Tips that pop up Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 10 of 51 Model 5-1: Simple Call Center Arrivals, Direct to Service (contd.) Decide module Trunk Line Available? Type = 2-way by Condition Select (logical) Expression for If NR() is number of units of that resource that are busy now MR() is number of units of that resource that exist now Alternate strategy Queue module from Blocks panel ... details in text False Record rejected call counter, Dispose True: Seize a unit of Trunk Line Resource Release later Resources data module for Trunk Line and other Resource levels Increment Variable Total WIP for number of active calls Used in stopping rule at or after 6pm to sense if system is empty Store module to animate entity during next Delay module Add Storage animation separately, identify with this logical storage by name Storage data module entry made there by Store module Delay module to listen to initial recording, make selection Could have used Process module, but this is simpler, faster Unstore module to make entity animation disappear Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 11 of 51 Model 5-1: Simple Call Center Arrivals, Direct to Service (contd.) Decide module Determine Call Type Three-sided coin flip Type = N-way by Chance Add button for more sides of coin Get new exit point for each Add, plus one for Else Note that probabilities are entered as percentages (0-100, not 0-1) Last entry is else Direct call to one of tech support, sales, or orderstatus areas Backed each area with colored box Alternative way to organize Submodels Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 12 of 51 Model 5-1: Simple Call Center Tech-Support Calls Assign module Store Delay Unstore for recording, product type selection Decide module for product type Different three-sided coin flip Direct to appropriate Process module for that product type Process modules for tech-support service Change Entity Type for separating out in results Change Entity Picture for animation Seize-Delay-Release Seize a unit from appropriate multi-unit Resource Use Tech Time defined in Expression data module Proceed to system exit logic ... later Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 13 of 51 Model 5-1: Simple Call Center Sales Calls Assign module change Entity Type, Picture Process module Seize-Delay-Release Seize a unit of Sales Resource Sales calls priority over order-status calls that seek a person? Queue data module, Process Sales Call.Queue Type = Lowest Attribute Value Shared queue (with order-status calls seeking a person) Not the only Attribute Name = Sales Call Priority way to do this Undefined for sales calls, so has value 0 ... will set to 1 for order-status calls that seek a person, putting sales calls ahead in the queue Proceed to system-exit logic Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 14 of 51 Model 5-1: Simple Call Center Order-Status Calls Assign module change Entity Type, Picture Delay block (Blocks panel) for robo-chat Includes Store/Unstore logic alternative to earlier method No automatic entry in Storage data module, so must enter manually Decide module No sales person required go directly to system-exit logic Sales person required: Assign module set Sales Call Priority Attribute to 1 so these will have lower priority than real sales calls Seize module for a unit of Sales resource Define Queue Name = Process Sales Call.Queue shared with sales calls Process module does not allow for specifying a shared queue, so cant use here Delay for conversation with sales person Release the unit of Sales resource Proceed to system-exit logic Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 15 of 51 Model 5-1: Simple Call Center System Exit All calls of all types come here when finished Release module release the unit of Trunk Line resource seized upstream Assign module decrement Total WIP variable Record module increment Completed Calls counter Dispose of call Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 16 of 51 Model 5-1: Simple Call Center Arrival-Cutoff Logic Used to choke off arrival stream at 6pm Create a single logical entity at time 600 min. (6pm) Overkill on making sure just one is created Assign module to set Variable MaxCalls to 1 Recall use of MaxCalls for Max Arrivals in Create module for attempted calls Creative use of such Also in this Assign module, set CallsPerArrival to 0 Time Between Arrivals = 999999 min., Max Arrivals = 1 logical (a.k.a. fake) entities enhances modeling flexibility, power, detail Since Create module will always schedule next arrival, and this makes the size of the next illegal arrival zero Dispose of this single logical entity Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 17 of 51 Model 5-1: Simple Call Center Run > Setup Replication Parameters tab (other tabs as usual) Base Time Units = Minutes Replication Length = Infinite (the default) Terminating Condition field: TNOW >= 600.0 Arena clock Variable Greater than or equal to 600 minutes, (6pm) && Logical and Total WIP == 0 Variable we maintained in model Equality test for zero Base Time Units Its 6pm or later and there are no calls in the system. Could have used NR(Trunk Line) instead of Total WIP Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 18 of 51 Model 5-1: Simple Call Center Animation Place three Storage animations Four Queue animations Initial Recording Delay, Tech Call Recording Delay, Order Status Delay Select proper Identifier in each from pull-down list Graphic behaves like Queue animations Three tech-support call product types, sales Came with four Process modules specifying Seize Resource animations for three tech-support types, sales Resources Multi-unit Resource animations, as in Models 4-3, 4-4 Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 19 of 51 Model 5-1: Simple Call Center Animation (contd.) Variable animations for WIP at tech calls, sales For tech calls, Arena variable to animate is Process Product Type 1 Tech Call.WIP, etc. pull-down list For sales calls, must include order-status calls seeking a real person: NR(Sales) + NQ(Process Sales Call.Queue) Plot number of trunk lines busy, NR(Trunk Line) Labeling, background boxes as in model logic Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 20 of 51 Model 5-1: Simple Call Center Results (one replication ... sample of size only one!!) Trunk-lines-busy plot 734 attempted calls (User Specified section) Starts, ends at 0 startup, termination logic Capped at 26 during run 643 completed, other 91 rejected Sometimes see mixture of sales (green), orderstatus (blue) entities in sales queue Other usual outputs Times in system separated out by call type Queue lengths, times in queue separated out by resource Resource utilizations normalized to [0, 1] by capacity Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 21 of 51 Model 5-2: Enhanced Call Center Changes Incoming calls arrival rate varies over day Probabilistic model Nonstationary Poisson process More in Section 12.3 Instead of a constant rate (= 1 / mean interarrival time), specify a rate function Arena supports piecewise-constant rate function step functions Easy to specify, strong theoretical support Rate-function specification: Caution its easy to generate this incorrectly ... see text for details Simulation with Arena, 5th ed. In Arena, rates MUST be in arrivals per HOUR, regardless of base time units or time intervals Chapter 5 Modeling Detailed Operations Slide 22 of 51 Model 5-2: Enhanced Call Center Changes (contd.) Sales-staff size varies over day Data in text, Schedule data module, Sales Schedule Tech-support staff are partially cross-trained, work complicated schedule: Will use Arena Sets concept to implement this cross training Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 23 of 51 Model 5-2: Enhanced Call Center Changes (contd.) 4% of tech-support calls cannot be handled during the call, need offline back-office research Original call ends, same original talk-time distribution, gives up its trunk line, but not counted (yet) as completed Case sent to back office (outside model boundaries), takes EXPO (60) minutes to resolve Offline research may be carried over night, completed on a later day Answer goes back to same tech-support person who took original call, with higher priority than incoming calls, but still might have to queue for this person This tech-support person requests a trunk line for outgoing call, higher priority than incoming calls, but still might have to queue, talks for TRIA (2 ,4 ,9) min., call is now completed Track number of each product type after research is done Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 24 of 51 Model 5-2: Enhanced Call Center Data Structure Resources, Schedules Resource, Schedule data modules Trunk Line fixed capacity at 26 Sales on Schedule Sales Schedule 11 individual tech-support people on individual schedules Caution must fill out each schedule to all 22 half-hour periods, with leading/trailing 0s if necessary ... use Edit via Dialog or Spreadsheet, not graphical schedule editor Ignore option to avoid shifting back schedule over multiple days Include costing data for people in Resource data module Define nonstationary arrival-rate function in Schedule module Arrival Schedule Enter trailing 0s in Edit via Dialog or Spreadsheet, not graphical schedule editor Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 25 of 51 Model 5-2: Enhanced Call Center Data Structure (contd.) Sets collect same-type items together Set, Advanced Set data modules (Basic, Advanced Process panels, resp.) Resource set for each tech-support product type Refer to items in set by original name, or index (subscript) in set Members are those tech-support resources qualified Individual resources already defined Resource data module Overlapping membership some resources in multiple sets Sets are ordered here, put most versatile tech-support people at bottom, to save them for other calls ... Preferred Order in Seize Will Seize from a set in model Counter one set for each hour Count number of rejected calls in each hour Individual counters already defined Statistic data module Use results later to decide when to increase staffing Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 26 of 51 Model 5-2: Enhanced Call Center Modifying the Model Call-arrivals, termination, Run > Setup Create module Delete the entire arrival-cutoff section from Model 5-1 Arrival Schedule cuts off arrivals at 6pm, via 0 rate Delete Total WIP variable used to terminate Model 5-1 Type = Schedule, Schedule Name = Arrival Schedule Use built-in NR(Trunk Line) instead in Terminating Condition Delete Assign modules used to manage Total WIP Record module for rejected calls Index into Counter Set Rejected Calls with index AINT((TNOW/60) + 1) which is 1 for first hour, 2 for second hour, etc. (AINT truncates decimals toward zero) Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 27 of 51 Model 5-2: Enhanced Call Center Modifying the Model (contd.) Tech-support calls Same through Determine Product Type Decide Add Assign modules for each product type thereafter Entity Type to distinguish product type in reports Entity Picture to distinguish product type in animation Attribute Tech Call Type (1, 2, or 3 by product type) for routing Process modules, Resources subdialogs Type = Set Set Name = Product 1, etc. Selection Rule = Preferred Order, to select earlier entries in set first Recall we put more versatile tech-support people lower in the set list Save Attribute = Tech Agent Index Entity attribute, carried along, in case of back-office research to send back to this same tech-support person for return call Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 28 of 51 Model 5-2: Enhanced Call Center Modifying the Model (contd.) Back office, returned tech-support calls all new Entry via True branch (4%) in Decide module Backoffice Research and Return Call? Release this calls trunk line going offline now Delay (with storage) for EXPO (60) back-office research Increment Tech Return WIP(Tech Call Type) 1-dim. Variable array defined in Variable data module Tech Call Type is 1, 2, or 3, assigned in earlier Assign module Decide module Product Type? based on Entity Type Seize the same tech-support person higher priority Then seize a trunk line (higher priority), make return call Then release this trunk line, tech-support person Decrement Tech Return WIP(Tech Call Type) Send entity to final Record, after trunk-line release there Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 29 of 51 Model 5-2: Enhanced Call Center Modifying the Model (contd.) Statistic data module Ten Counter-type statistics, discussed earlier Four Time-Persistent statistics to track expressions Backoffice Research WIP to track total number of cases in research, via NSTO(Backoffice Research Storage) Tech 1 Total Online WIP Stat, etc., to track number of that product type in back office via Expression Tech 1 Total Online WIP, etc., defined in Expression data module as Process Product Type 1 Tech Call.WIP + Tech Return WIP(1), etc. No changes needed in sales-calls or order-statuscalls section of Model 5-1 Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 30 of 51 Model 5-2: Enhanced Call Center Modifying the Model (contd.) Animation Delete Tech 1, Tech 2, and Tech 3 resource animations Change variables in three tech-support WIP displays to track total number of tech-support calls of that type present New back-office storage animation, variable animation for number present A new queue for each tech-support product type for return calls waiting for service Added a resource animation (from a .plb library) for each individual tech-support person Grouped by product type, colors for capabilities Results Most rejected calls in hours 5-8 ... increase staff then ... ? Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 31 of 51 Model 5-3: Overall Call-Center Stats Setup Develop overall operational-cost measure Two cost categories staffing/resource, and poor service Develop overall measure of service, % of calls rejected Add options for increased staffing, improvement Make 5 replications, focus on weekly costs IID replications, so will not carry over back-office research Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 32 of 51 Model 5-3: Overall Call-Center Stats Staffing/Resource Costs Resource data module hourly costs for people $20/hr. for each sales staffer $18/hr. $20/hr. for each tech-support, depending on skill These salary costs paid when on duty, busy or idle Summing, get $12,820/week (details in text) View all this existing staff as fixed Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 33 of 51 Model 5-3: Overall Call-Center Stats Staffing/Resource Costs (contd.) Increase sales, tech-support staff noon-4pm Variable New Sales = number of new sales staff $17/hr., 4 hrs./day, 5 days/week, so $340/week for each addl. staff Schedule data module to add capacity edit via dialog or spreadsheet, not graphical editor Resource (Sales) already exists in Resource data module Variables New Tech 1, etc., and New Tech All for number of new tech-support people qualified as named $16/hr. for each one-product staff, $18/hr. for each all-product staff $320/week for each single-product staff, $360/week for each all-product staff New entries in Resource data module Larry, Moe, Curly, Hermann for 1, 2, 3, All, resp. Schedule data module to add capacity dialog or spreadsheet edit Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 34 of 51 Model 5-3: Overall Call-Center Stats Staffing/Resource Costs (contd.) Maybe increase number of trunk lines beyond 26 $98/week flat fee for each trunk line Define Expression New Res Cost for all resource costs: New Sales*340 + (New Tech 1 + New Tech 2 + New Tech 3)*320 + New Tech All*360 + 98*MR(Trunk Line) This does not depend on simulation results, only on setup Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 35 of 51 Model 5-3: Overall Call-Center Stats Customer-Dissatisfaction Costs Incur cost for caller wait on hold, past a threshold 3 min. for tech, 1 min. for sales, 2 min. for order-status Beyond threshold, incur per-min. costs of $0.368 for tech, $0.818 for sales, $0.346 for order-status In practice, such costs are difficult to estimate Three new Assign modules (orange backing) accumulate excess (beyond threshold) wait times on hold Tech support (other two are similar): Variable Excess Tech Wait Time increased by MAX(ENTITY.WAITTIME - 3, 0) ENTITY.WAITTIME is built-in Arena attribute holding all wait times (including in queues) so far ... luckily, there were none before the preceding Process module At end, multiply excess wait times by per-min. costs, multiplied by 5 (to put on a weekly basis) 5 $0.368 = $1.84 for tech, 5 $0.818 = $4.09 for sales, 5 $0.346 = $1.73 for order-status Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 36 of 51 Model 5-3: Overall Call-Center Stats Overall Output Performance Measures Statistic data module, Total Cost entry Type = Output, computed only at end of replication New Res Cost + Excess Sales Wait Time * 4.09 + Excess Status Wait Time * 1.73 + Excess Tech Wait Time * 1.84 + 12820 Statistic data module, Percent Rejected entry Counter Total Rejected Calls accumulated in new Record module in call-arrival area (orange backing) Already accumulating hour by hour, but this is total over the day Type = Output 100 * NC(Total Rejected Calls) / NC(Attempted Calls) NC is Arena function that returns the value of that counter Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 37 of 51 Model 5-3: Overall Call-Center Stats Replication Conditions Run > Setup > Replication Parameters, Initialize Between Replications Statistics? System? Details in text Default is both only way to get truly IID replications Destroys overnight tech-support research jobs, but to do otherwise would complicate model so accept Run > Setup > Project Parameters Turned off all but Costing Statistics Collection, for speed Costing required to get ENTITY.WAITTIME Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 38 of 51 Model 5-3: Overall Call-Center Stats Results Results from five replications Base Case no additional staff, still 26 trunk lines Total Cost = $22,500.07 Average over 5 replications Conf. int. half-widths in output Percent Rejected = 12.9% Add 3 of each of five staff types, 3 more trunk lines Total Cost = $22,668.69 Is this better? Percent Rejected = 1.6% Use in Chapt. 6 for statistically valid experiments Statistical precision Compare several alternatives, select best Search for configuration that minimizes cost, subject to upper limit on percent rejected Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 39 of 51 Model 5-4: (s, S) Inventory Simulation Setup Different kind of model not queueing Use Blocks and Elements panels exclusively SIMAN simulation language Mostly just to demonstrate this capability Could be done with higher-level panels weve been using Company carries a single discrete item (widgets) in inventory I(t) = inventory level (an integer) at time t days past the beginning of the simulation; I(0) = 60 Run simulation for 120 round-the-clock days Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 40 of 51 Model 5-4: (s, S) Inventory Simulation Customer Demands Against Inventory Customer interarrival times ~ EXPO (0.1) day (round the clock) Demand size is discrete random variable First arrival not at time 0 but after an interarrival time past 0 1, 2, 3, 4 with respective probabilities 0.167, 0.333, 0.333, 0.167 If enough items are physically on hand in inventory to satisfy a demand, customer gets demand and leaves If demand > number of items on hand, customer gets whatever is there and the rest of the demand is backlogged (I(t) becomes negative) If I(t) was already negative, it just goes more negative Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 41 of 51 Model 5-4: (s, S) Inventory Simulation Inventory Review, Replenishment Take inventory just past midnight each day So at exactly times 0, 1, 2, ..., 119 (not 120 ... see below) Two managerially-chosen constant integers s = 20 and S = 40 (must have s < S if we change these values) If I(t) s, do nothing until next inventory evaluation exactly 24 hours later If I(t) < s, order S I(t) items from supplier (order up to S) Order does not arrive instantly from supplier, but after a delivery lag (a.k.a. lead time) ~ UNIF(0.5, 1.0) day, so sometime during the last half of the day of ordering In the meantime, inventory level could fall further from additional demands, so inventory level will not necessarily pop up to S when the order arrives, but to something less than S Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 42 of 51 Model 5-4: (s, S) Inventory Simulation Cost Structure Average ordering cost per day Average holding cost per day When an order is placed, incur a fixed cost of $32, plus an incremental cost of $3 per item ordered If no order is placed at the beginning of a day, theres no ordering cost, not even the fixed cost At end of simulation, divide total of ordering costs by 120 Whenever I(t) > 0, incur $1 per day per item on hand Average holding cost = Average shortage cost per day Whenever I(t) < 0, incur $5 per day per item in backlog Average shortage cost = Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 43 of 51 Model 5-4: (s, S) Inventory Simulation Cost Structure (contd.) During periods when I(t) = 0 theres neither holding nor shortage cost incurred Overall performance measure = Average total cost per day = sum of average ordering, holding, and shortage costs per day Dont evaluate inventory at time 120 We might order and incur an ordering cost then, but order will never arrive Well fudge this, but an Exercise asks you to do it right Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 44 of 51 Model 5-4: (s, S) Inventory Simulation Data Structure Use Blocks, Elements panels exclusively Even for Variables, Expressions, Attributes, Entities, statistics collection, and run control Variables Element (initialized, or default to 0 initially) Inventory Level = I(t), changes during run, initialized to 60 Little s = s = 20 Big S = S = 40 Total Ordering Cost accumulator Setup Cost = 32 Incremental Cost = 3 Unit Holding Cost = 1 Unit Shortage Cost = 5 Days to Run = 119.9999 (The Fudge) Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 45 of 51 Model 5-4: (s, S) Inventory Simulation Data Structure (contd.) Expressions element Define Interdemand Time, Demand Size, Evaluation Interval, Delivery Lag Attributes, Entities elements Just to define these objects Project, Replicate elements Cumulative probabilities in DISC function for Demand Size Similar to Run > Setup DStats element Request accumulation of integrals for total holding, shortage costs Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 46 of 51 Model 5-4: (s, S) Inventory Simulation Data Structure (contd.) Outputs element Two entries, both of Data type Output so that theyre executed only at end of run, and reported Avg Ordering Cost computed Avg Total Cost added up OVALUE returns most recent value DAVG returns time-persistent average Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 47 of 51 Model 5-4: (s, S) Inventory Simulation Logic for Customer Demands Create block for arrival Assign block to decrement Inventory Level by a Demand Size Entity Type is Customer Uses Interdemand Time Expression First Creation after an Interdemand Time Demand Size was defined as an Expression Backlogging naturally happens Dispose block for customer exit If backlogged, is accounted for automatically in the (simple) definition and tracking of Inventory Level Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 48 of 51 Model 5-4: (s, S) Inventory Simulation Inventory Evaluation Create block for Inventory Evaluator entities First Creation at time 0 evaluate inventory at start of run Interval is Evaluation Interval, defined as Expression Branch block somewhat like Decide module To determine whether to place an order now Add branches, each evaluated as true or false Clone of incoming entity sent out along each true branch, but at most Max Number of Branches will be sent out So we set Max Number of Branches to 1 (default is ) First branch of type If if true we want to order Second branch of type Else if true it means that the first branch was false so we dont order just Dispose Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 49 of 51 Model 5-4: (s, S) Inventory Simulation Placing an Order If we exit the Branch block via the top If branch, it must be that I(t) < s so we want to order up to S Assign block Define Order Quantity Attribute Could have made this a Variable in this model with these parameters, but its more general for it to be an Attribute ... why? Increment Total Ordering Cost Variable Delay block for Delivery Lag Assign block to increment Inventory Level by the Order Quantity Dispose block Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 50 of 51 Model 5-4: (s, S) Inventory Simulation Animation Plot separate in the black and in the red curves If in backlog, red curve will be plotted in negative direction due to its Expression Pair of Level (thermometer) animations Fill Direction for in the red is Down Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 51 of 51
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Branch-and-Cut Valid inequality: an inequality satisfied byall feasible solutions Cut: a valid inequality that is not part of thecurrent formulation Violated cut: a cut that is not satisfied bythe solution to the current LP relaxationBranch-and-Cut
Abraham Baldwin Agricultural College - ECON - 102
Branch-and-Cut Valid inequality: an inequality satisfied byall feasible solutions Cut: a valid inequality that is not part of thecurrent formulation Violated cut: a cut that is not satisfied bythe solution to the current LP relaxationBranch-and-Cut
Stanford - CS - 345
CS 345 Data MiningOnline algorithms Search advertisingOnline algorithmsClassic model of algorithmsYou get to see the entire input, then compute some function of it In this context, offline algorithmOnline algorithmYou get to see the input one piece
Stanford - CS - 345
CS 345 Data MiningOnline algorithms Search advertising Online algorithmsClassic model of algorithmsOnline algorithmYou get to see the entire input, then compute some function of it In this context, &quot;offline algorithm&quot; You get to see the input one
Stanford - CS - 345
CS 345 Data MiningOnline algorithms Search advertisingOnline algorithmsClassic model of algorithmsYou get to see the entire input, then compute some function of it In this context, offline algorithmOnline algorithmYou get to see the input one piece
Stanford - CS - 345
CS 345 Data MiningOnline algorithms Search advertising Online algorithmsClassic model of algorithmsOnline algorithmYou get to see the entire input, then compute some function of it In this context, &quot;offline algorithm&quot; You get to see the input one
Stanford - CS - 345
CS 345 Data MiningOnline algorithms Search advertising Online algorithmsClassic model of algorithmsOnline algorithmYou get to see the entire input, then compute some function of it In this context, &quot;offline algorithm&quot; You get to see the input one
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets A-priori Algorithm1The Market-Basket ModelA large set of items, e.g., things sold in a supermarket. A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys
Stanford - CS - 345
&quot;Association Rules&quot;Market Baskets Frequent Itemsets Apriori Algorithm1The MarketBasket Modelx A large set of items, e.g., things sold in a supermarket. x A large set of baskets, each of which is a small set of the items, e.g., the things one customer
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets A-priori Algorithm1The Market-Basket ModelA large set of items, e.g., things sold in a supermarket. A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets Apriori Algorithm1The MarketBasket Modelx A large set of items, e.g., things sold in a supermarket. x A large set of baskets, each of which is a small set of the items, e.g., the things one customer bu
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets A-Priori Algorithm1The Market-Basket ModelA large set of items, e.g., things sold in a supermarket. A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys
Stanford - CS - 345
Association RulesMarket Baskets Frequent Itemsets APriori Algorithm1The MarketBasket Modelx A large set of items, e.g., things sold in a supermarket. x A large set of baskets, each of which is a small set of the items, e.g., the things one customer bu
Stanford - CS - 345
Improvements to A-PrioriPark-Chen-Yu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY AlgorithmHash-based improvement to A-Priori. During Pass 1 of A-priori, most memory is idle. Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY Algorithmx Hashbased improvement to APriori. x During Pass 1 of Apriori, most memory is idle. x Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1PCY Algorithmx Hashbased improvement to APriori. x During Pass 1 of Apriori, most memory is idle. x Use that memory to keep counts of buckets in
Stanford - CS - 345
Improvements to APrioriBloom Filters ParkChenYu Algorithm Multistage Algorithm Approximate Algorithms Compacting Results1Aside: HashBased Filteringx Simple problem: I have a set S of one billion strings of length 10. x I want to scan a larger file F o
Stanford - CS - 345
SQL/MRPeter Pawlowski Member of Technical Staff January 16, 2009ASTER BACKGROUND2Our Founders3 PhD students from Stanford C.S. Cool ideas. . but no funding, no product, no clients!OK, they had $ 10,000.3Our Product: nCluster A massively scalable
Stanford - CS - 345
Clustering AlgorithmsApplications Hierarchical Clustering k Means Algorithms CURE Algorithm1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/Non-Euclidean Spaces Distance Measures1The Problem of ClusteringGiven a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluster
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/NonEuclidean Spaces Distance Measures1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluste
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/Non-Euclidean Spaces Distance Measures1The Problem of ClusteringGiven a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluster
Stanford - CS - 345
Clustering PreliminariesApplications Euclidean/NonEuclidean Spaces Distance Measures1The Problem of Clusteringx Given a set of points, with a notion of distance between points, group the points into some number of clusters, so that members of a cluste
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k -Means Algorithms CURE Algorithm1Methods of ClusteringHierarchical (Agglomerative):Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one.Point Assignment:Ma
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k Means Algorithms CURE Algorithm1Methods of Clusteringx Hierarchical (Agglomerative): Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one. Maintain a set of
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k -Means Algorithms CURE Algorithm1Methods of ClusteringHierarchical (Agglomerative):Initially, each point in cluster by itself. Repeatedly combine the two nearest clusters into one.Point Assignment:Main
Stanford - CS - 345
Clustering AlgorithmsHierarchical Clustering k Means Algorithms CURE Algorithm1Methods of Clusteringx Hierarchical (Agglomerative): Initially, each point in cluster by itself. Repeatedly combine the two &quot;nearest&quot; clusters into one. Maintain a set of
Stanford - CS - 345
CS345 Data Mining Crawling the Web Web Crawling BasicsStart with a &quot;seed set&quot; of tovisit urlsget next url get page extract urlsto visit urlsWebvisited urlsweb pagesCrawling Issues Load on web servers Insufficient resources to crawl entire web
Stanford - CS - 345
Problem 1:a) True Consider visiting the rows in the permuted order. The first time you see a one in any of the two columns, the column C1 \/ C2 will also have a one. Consequently, the first (minimum) row number which corresponds to the min hash value for
Stanford - CS - 345
Locality-Sensitive HashingBasic Technique Hamming-LSH Applications1Finding Similar PairsSuppose we have in main memory data representing a large number of objects.May be the objects themselves (e.g., summaries of faces). May be signatures as in minha
Stanford - CS - 345
LocalitySensitive HashingBasic Technique HammingLSH Applications1Finding Similar Pairsx Suppose we have in main memory data representing a large number of objects. May be the objects themselves (e.g., summaries of faces). May be signatures as in minh
Stanford - CS - 345
Finding Similar PairsDivideComputeMerge LocalitySensitive Hashing Applications1Finding Similar Pairsx Suppose we have in main memory data representing a large number of objects. May be the objects themselves (e.g., summaries of faces). May be signatu
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1s1The Stream ModelData enters at a rapid rate from one or more input ports. The system cannot store the entire stream. How do you make critical calculations about the stream using a limited
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1The Stream Modelx Data enters at a rapid rate from one or more input ports. x The system cannot store the entire stream. x How do you make critical calculations about the stream using a
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1Data Management Versus Stream ManagementIn a DBMS, input is under the control of the programmer.SQL INSERT commands or bulk loaders.Stream Management is important when the input rate i
Stanford - CS - 345
Mining Data StreamsThe Stream Model Sliding Windows Counting 1's1Data Management Versus Stream Managementx In a DBMS, input is under the control of the programmer. x Stream Management is important when the input rate is controlled externally. Example
Stanford - CS - 345
More Stream-MiningCounting How Many Elements Computing Moments1Counting Distinct ElementsProblem: a data stream consists of elements chosen from a set of size n. Maintain a count of the number of distinct elements seen so far. Obvious approach: mainta
Stanford - CS - 345
More StreamMiningCounting How Many Elements Computing &quot;Moments&quot;1Counting Distinct Elementsx Problem: a data stream consists of elements chosen from a set of size n. Maintain a count of the number of distinct elements seen so far. x Obvious approach: m
Stanford - CS - 345
More Stream-MiningCounting Distinct Elements Computing &quot;Moments&quot; Frequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Distinct ElementsProblem: a data stream consists of elements chosen from a set of size n. Maintain a count
Stanford - CS - 345
More StreamMiningCounting Distinct Elements Computing &quot;Moments&quot; Frequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Distinct Elementsx Problem: a data stream consists of elements chosen from a set of size n. Maintain a count
Stanford - CS - 345
Still More Stream-MiningFrequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting ItemsProblem: given a stream, which items appear more than s times in the window? Possible solution: think of the stream of baskets as one binary st
Stanford - CS - 345
Still More StreamMiningFrequent Itemsets Elephants and Troops Exponentially Decaying Windows1Counting Itemsx Problem: given a stream, which items appear more than s times in the window? x Possible solution: think of the stream of baskets as one binary
Stanford - CS - 345
Stream ClusteringExtension of DGIM to More Complex Problems1Clustering a StreamAssume points enter in a stream. Maintain a sliding window of points. Queries ask for clusters of points within some suffix of the window. Important issue: where are the cl
Stanford - CS - 345
Stream ClusteringExtension of DGIM to More Complex Problems1Clustering a Streamx Assume points enter in a stream. x Maintain a sliding window of points. x Queries ask for clusters of points within some suffix of the window. x Important issue: where ar
Stanford - CS - 345
CS345 Data MiningIntroductions What Is It? Cultures of Data Mining1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Robbie Yanx TA:2Requirementsx Homework (Gradiance and other) 20% x Project 40% x Final Exam 40% Gradiance class code BB8F69
Stanford - CS - 345
CS345 - Data MiningIntroductions What Is It? Cultures of Data Mining1Course StaffInstructors:Anand Rajaraman Jeff UllmanTA:Jeff Klingner2RequirementsHomework (Gradiance and other) 20%Gradiance class code DD984360Project 40% Final Exam 40%3Pr
Stanford - CS - 345
CS345 Data MiningIntroductions What Is It? Cultures of Data Mining1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Jeff Klingnerx TA:2Requirementsx Homework (Gradiance and other) 20% x Project 40% x Final Exam 40% Gradiance class code DD9
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CS345 - Data MiningCourse Introduction Varieties of Data Mining Bonferroni's Principle1Course StaffInstructors:Anand Rajaraman Jeff UllmanTA:Babak Pahlavan2RequirementsHomework (Gradiance and other) 20%Gradiance class code B0E9AA66 Note URL for
Stanford - CS - 345
CS345A: Data Mining on the WebCourse Introduction Issues in Data Mining Bonferroni's Principle1Course Staffx Instructors: Anand Rajaraman Jeff Ullman Babak Pahlavanx TA:2Requirementsx Homework (Gradiance and other) 20% Gradiance class code B0E9A
Stanford - CS - 345
CS345A: Data Mining on the WebCourse Introduction Issues in Data Mining Bonferroni's Principle1Course Staffx Instructors: Anand Rajaraman Jeff Ullmanx Reach us as cs345awin0809staff @ lists.stanford.edu. x More info on www.stanford.edu/class/cs345a.
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Generalizing MapReduceThe Computational Model MapReduceLike Algorithms Computing Joins1Overviewx There is a new computing environment available: x Mapreduce allows us to exploit this environment easily. x But not everything is mapreduce. x What else c
Stanford - CS - 345
CS 345A Data MiningMapReduceSingle-node architectureCPU Machine Learning, Statistics Memory &quot;Classical&quot; Data Mining DiskCommodity ClustersWeb data sets can be very largeTens to hundreds of terabytesCannot mine on a single server (why?) Standard arc
Stanford - CS - 345
CS 345A Data MiningMapReduce Singlenode architectureCPU Machine Learning, Statistics Memory &quot;Classical&quot; Data Mining DiskCommodity ClustersWeb data sets can be very large Cannot mine on a single server (why?) Standard architecture emerging: Te