Chapter 1 Basic Simulation Modeling

Chapter 1 Basic Simulation Modeling
Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

CONTENTS 1.1 The Nature of Simulation
1.2 Systems, Models, and Simulation 1.3 Discrete-Event Simulation 1.4 Simulation of a Single-Server Queueing System 1.5 Simulation of an Inventory System 1.6 Alternative Approaches to Modeling and Coding Simulations 1.7 Steps in a Sound Simulation Study 1.8 Other Types of Simulation 1.9 Advantages, Disadvantages, and Pitfalls of Simulation Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.1 THE NATURE OF SIMULATION
Simulation: Imitate the operations of a facility or process, usually via computer What’s being simulated is the system To study system, often make assumptions/approximations, both logical and mathematical, about how it works These assumptions form a model of the system If model structure is simple enough, could use mathematical methods to get exact information on questions of interest — analytical solution Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.1 The Nature of Simulation (cont’d.)
But most complex systems require models that are also complex (to be valid) Must be studied via simulation — evaluate model numerically and collect data to estimate model characteristics Example: Manufacturing company considering extending its plant Build it and see if it works out? Simulate current, expanded operations — could also investigate many other issues along the way, quickly and cheaply Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Some (not all) application areas Designing and analyzing manufacturing systems Evaluating military weapons systems or their logistics requirements Determining hardware requirements or protocols for communications networks Determining hardware and software requirements for a computer system Designing and operating transportation systems such as airports, freeways, ports, and subways Evaluating designs for service organizations such as call centers, fast-food restaurants, hospitals, and post offices Reengineering of business processes Determining ordering policies for an inventory system Analyzing financial or economic systems Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Use, popularity of simulation Several conferences devoted to simulation, notably the Winter Simulation Conference ( Surveys of use of OR/MS techniques (examples …) Longitudinal study ( ): Simulation consistently ranked as one of the three most important techniques 1294 papers in Interfaces (1997): Simulation was second only to the broad category of “math programming” Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Impediments to acceptance, use of simulation Models of large systems are usually very complex But now have better modeling software … more general, flexible, but still (relatively) easy to use Can consume a lot of computer time But now have faster, bigger, cheaper hardware to allow for much better studies than just a few years ago … this trend will continue However, simulation will also continue to push the envelope on computing power in that we ask more and more of our simulation models Impression that simulation is “just programming” There’s a lot more to a simulation study than just “coding” a model in some software and running it to get “the answer” Need careful design and analysis of simulation models – simulation methodology Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.2 SYSTEMS, MODELS, AND SIMULATION
System: A collection of entities (people, parts, messages, machines, servers, …) that act and interact together toward some end (Schmidt and Taylor, 1970) In practice, depends on objectives of study Might limit the boundaries (physical and logical) of the system Judgment call: level of detail (e.g., what is an entity?) Usually assume a time element – dynamic system State of a system: Collection of variables and their values necessary to describe the system at that time Might depend on desired objectives, output performance measures Bank model: Could include number of busy tellers, time of arrival of each customer, etc. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.2 Systems, Models, and Simulation (cont’d.)
Types of systems Discrete State variables change instantaneously at separated points in time Bank model: State changes occur only when a customer arrives or departs Continuous State variables change continuously as a function of time Airplane flight: State variables like position, velocity change continuously Many systems are partly discrete, partly continuous Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Ways to study a system Simulation is “method of last resort?” Maybe … But with simulation there’s no need (or less need) to “look where the light is” Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Classification of simulation models Static vs. dynamic Deterministic vs. stochastic Continuous vs. discrete Most operational models are dynamic, stochastic, and discrete – will be called discrete-event simulation models Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3 DISCRETE-EVENT SIMULATION
Discrete-event simulation: Modeling of a system as it evolves over time by a representation where the state variables change instantaneously at separated points in time More precisely, state can change at only a countable number of points in time These points in time are when events occur Event: Instantaneous occurrence that may change the state of the system Sometimes get creative about what an “event” is … e.g., end of simulation, make a decision about a system’s operation Can in principle be done by hand, but usually done on computer Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3 Discrete-Event Simulation (cont’d.)
Example: Single-server queue Estimate expected average delay in queue (line, not service) State variables Status of server (idle, busy) – needed to decide what to do with an arrival Current length of the queue – to know where to store an arrival that must wait in line Time of arrival of each customer now in queue – needed to compute time in queue when service starts Events Arrival of a new customer Service completion (and departure) of a customer Maybe – end-simulation event (a “fake” event) – whether this is an event depends on how simulation terminates (a modeling decision) Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3.1 Time-Advance Mechanisms
Simulation clock: Variable that keeps the current value of (simulated) time in the model Must decide on, be consistent about, time units Usually no relation between simulated time and (real) time needed to run a model on a computer Two approaches for time advance Next-event time advance (usually used) … described in detail below Fixed-increment time advance (seldom used) … Described in Appendix 1A Generally introduces some amount of modeling error in terms of when events should occur vs. do occur Forces a tradeoff between model accuracy and computational efficiency Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3.1 Time-Advance Mechanisms (cont’d.)
More on next-event time advance Initialize simulation clock to 0 Determine times of occurrence of future events – event list Clock advances to next (most imminent) event, which is executed Event execution may involve updating event list Continue until stopping rule is satisfied (must be explicitly stated) Clock “jumps” from one event time to the next, and doesn’t “exist” for times between successive events … periods of inactivity are ignored Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3.1 Time-Advance Mechanisms (cont’d.)
Next-event time advance for the single-server queue ti = time of arrival of ith customer (t0 = 0) Ai = ti – ti-1 = interarrival time between (i-1)st and ith customers (usually assumed to be a random variable from some probability distribution) Si = service-time requirement of ith customer (another random variable) Di = delay in queue of ith customer Ci = ti + Di + Si = time ith customer completes service and departs ej = time of occurrence of the jth event (of any type), j = 1, 2, 3, … Possible trace of events (detailed narrative in text) Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3.2 Components and Organization of a Discrete-Event Simulation Model
Each simulation model must be customized to target system But there are several common components, general organization System state – variables to describe state Simulation clock – current value of simulated time Event list – times of future events (as needed) Statistical counters – to accumulate quantities for output Initialization routine – initialize model at time 0 Timing routine – determine next event time, type; advance clock Event routines – carry out logic for each event type Library routines – utility routines to generate random variates, etc. Report generator – to summarize, report results at end Main program – ties routines together, executes them in right order Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.3.2 Components and Organization of a Discrete-Event Simulation Model (cont’d.)

1.3.2 Components and Organization of a Discrete-Event Simulation Model (cont’d.)
More on entities Objects that compose a simulation model Usually include customers, parts, messages, etc. … may include resources like servers Characterized by data values called attributes For each entity resident in the model there’s a record (row) in a list, with the attributes being the columns Approaches to modeling Event-scheduling – as described above, coded in general-purpose language Process – focuses on entities and their “experience,” usually requires special-purpose simulation software Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4 SIMULATION OF A SINGLE-SERVER QUEUEING SYSTEM
Will show how to simulate a specific version of the single-server queueing system Book contains code in FORTRAN and C … slides will focus only on C version Though simple, it contains many features found in all simulation models Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.1 Problem Statement Recall single-server queueing model
Assume interarrival times are independent and identically distributed (IID) random variables Assume service times are IID, and are independent of interarrival times Queue discipline is FIFO Start empty and idle at time 0 First customer arrives after an interarrival time, not at time 0 Stopping rule: When nth customer has completed delay in queue (i.e., enters service) … n will be specified as input Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.1 Problem Statement (cont’d.)
Quantities to be estimated Expected average delay in queue (excluding service time) of the n customers completing their delays Why “expected?” Expected average number of customers in queue (excluding any in service) A continuous-time average Area under Q(t) = queue length at time t, divided by T(n) = time simulation ends … see book for justification and details Expected utilization (proportion of time busy) of the server Another continuous-time average Area under B(t) = server-busy function (1 if busy, 0 if idle at time t), divided by T(n) … justification and details in book Many others are possible (maxima, minima, time or number in system, proportions, quantiles, variances …) Important: Discrete-time vs. continuous-time statistics Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.2 Intuitive Explanation
Given (for now) interarrival times (all times are in minutes): 0.4, 1.2, 0.5, 1.7, 0.2, 1.6, 0.2, 1.4, 1.9, … Given service times: 2.0, 0.7, 0.2, 1.1, 3.7, 0.6, … n = 6 delays in queue desired “Hand” simulation: Display system, state variables, clock, event list, statistical counters … all after execution of each event Use above lists of interarrival, service times to “drive” simulation Stop when number of delays hits n = 6, compute output performance measures Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.2 Intuitive Explanation (cont’d)
Interarrival times: 0.4, 1.2, 0.5, 1.7, 0.2, 1.6, 0.2, 1.4, 1.9, … Service times: 2.0, 0.7, 0.2, 1.1, 3.7, 0.6, … Status shown is after all changes have been made in each case … Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Interarrival times: 0.4, 1.2, 0.5, 1.7, 0.2, 1.6, 0.2, 1.4, 1.9, … Service times: 2.0, 0.7, 0.2, 1.1, 3.7, 0.6, … Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Interarrival times: 0.4, 1.2, 0.5, 1.7, 0.2, 1.6, 0.2, 1.4, 1.9, … Service times: 2.0, 0.7, 0.2, 1.1, 3.7, 0.6, … Final output performance measures: Average delay in queue = 5.7/6 = 0.95 min./cust. Time-average number in queue = 9.9/8.6 = 1.15 custs. Server utilization = 7.7/8.6 = 0.90 (dimensionless) Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.3 Program Organization and Logic
C program to do this model (FORTRAN as well is in book) Event types: 1 for arrival, 2 for departure Modularize for initialization, timing, events, library, report, main Changes from hand simulation: Stopping rule: n = 1000 (rather than 6) Interarrival and service times “drawn” from an exponential distribution (mean b = 1 for interarrivals, 0.5 for service times) Density function Cumulative distribution function Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.3 Program Organization and Logic (cont’d.)
How to “draw” (or generate) an observation (variate) from an exponential distribution? Proposal: Assume a perfect random-number generator that generates IID variates from a continuous uniform distribution on [0, 1] … denoted the U(0, 1) distribution … see Chap. 7 Algorithm: 1. Generate a random number U 2. Return X = – b ln U Proof that algorithm is correct: Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.5 C Program; 1.4.6 Simulation Output and Discussion
Refer to pp. 30, 31, in the book (Figures 1.8, 1.9, ) and the file mm1.c Figure 1.19 – external definitions (at top of file) Figure 1.20 – function main Figure 1.21 – function initialize Figure 1.22 – function timing Figure 1.23 – function arrive (flowchart: Figure 1.8) Figure 1.24 – function depart (flowchart: Figure 1.9) Figure 1.25 – function report Figure 1.26 – function update_time_avg_stats Figure 1.27 – function expon Figure 1.28 – output report mm1.out Are these “the” answers? Steady-state vs. terminating? What about time in queue vs. just time in system? Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.7 Alternative Stopping Rules
Stop simulation at (exactly) time 8 hours (= 480 minutes), rather than whenever n delays in queue are completed Before, final value of simulation clock was a random variable Now, number of delays completed will be a random variable Introduce an artificial “end-simulation” event (type 3) Schedule it on initialization Event routine is report generator Be sure to update continuous-time statistics to end Changes in C code (everything else is the same) Figure 1.33 – external definitions Figure 1.34 – function main Figure 1.35 – function initialize Figure 1.36 – function report Figure 1.37 – output report mm1alt.out Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.4.8 Determining the Events and Variables
For complex models, it might not be obvious what the events are Event-graph method (Schruben 1983, and subsequent papers) gives formal graph-theoretic method of analyzing event structure Can analyze what needs to be initialized, possibility of combining events to simplify model Software package (SIGMA) to build, execute a simulation model via event-graph representation Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.5 SIMULATION OF AN INVENTORY SYSTEM; 1.5.1 Problem Statement
Single-product inventory Decide how many items to have in inventory for the next n = 120 months; initially (time 0) have 60 items on hand Demands against inventory Occur with inter-demand time ~ exponential with mean 0.1 month Demand size = 1, 2, 3, 4 with resp. probabilities 1/6, 1/3, 1/3, 1/6 Inventory review, reorder – stationary (s, S) policy … at beginning of each month, review inventory level = I If I  s, don’t order (s is an input constant); no ordering cost If I < s, order Z = S – I items (S is an input constant, order “up to” S); ordering cost = Z; delivery lag ~ U(0.5, 1) month Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.5.1 Problem Statement (cont’d.)
Demand in excess of current (physical) inventory is backlogged … so (accounting) inventory could be < 0 Let I(t) be (accounting) inventory level at time t (+, 0, –) I+(t) = max {I(t), 0} = number of items physically on hand at time t I –(t) = max {–I(t), 0} = number of items in backlog at time t Holding cost: Incur $1 per item per month in (positive) inventory Time-average (per month) holding cost = Shortage cost: Incur $5 per item per month in backlog Time-average (per month) backlog cost = Average total cost per month: Add ordering, holding, shortage costs per month Try different (s, S) combinations to try to reduce total cost Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.5.2 Program Organization and Logic
State variables: Inventory level, amount of an outstanding order, time of the last (most recent) event Events: 1. Arrival of an order from the supplier 2. Demand for the product 3. End of the simulation after n = 120 months 4. Inventory evaluation (maybe ordering) at beginning of a month Random variates needed Interdemand times: exponential, as in queueing model Delivery lags ~ U(0.5, 1): (1 – 0.5)U, where U ~ U(0, 1) Demand sizes: Split [0, 1] into subintervals of width 1/6, 1/3, 1/3, 1/6; generate U ~ U(0, 1); see which subinterval U falls in; return X = 1, 2, 3, or 4, respectively Why the ordering of event types 3 and 4? Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.5.4 C Program; 1.5.5 Simulation Output and Discussion
Refer to pp , in the book (Figures , ) and the file inv.c Figure 1.57 – external definitions (at top of file) Figure 1.58 – function main Figure 1.59 – function initialize Figure 1.60 – function order_arrival (flowchart: Figure 1.43) Figure 1.61 – function demand (flowchart: Figure 1.44) Figure 1.62 – function evaluate (flowchart: Figure 1.45) Figure 1.63 – function report Figure 1.64 – function update_time_avg_stats (flowchart: Figure 1.46) Figure 1.65 – function random_integer Figure 1.66 – function uniform Figure 1.67 – output report inv.out Reaction of individual cost components to changes in s and S … overall? Uncertainty in output results (this was just one run)? Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.6 ALTERNATIVE APPROACHES TO MODELING AND CODING SIMULATIONS
Parallel and distributed simulation Various kinds of parallel and distributed architectures Break up a simulation model in some way, run the different parts simultaneously on different parallel processors Different ways to break up model By support functions – random-number generation, variate generation, event-list management, event routines, etc. Decompose the model itself; assign different parts of model to different processors – message-passing to maintain synchronization, or forget synchronization and do “rollbacks” if necessary … “virtual time” Web-based simulation Central simulation engine, submit “jobs” over the web Wide-scope parallel/distributed simulation Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.7 STEPS IN A SOUND SIMULATION STUDY

STEPS IN A SOUND SIMULATION STUDY
Now that we have looked in some detail at the inner workings of a discrete-event simulation. We need to step back and realize that model programming is just part of the overall effort to design or analyze a complex system by simulation. Attention must be paid to a variety of other concerns such as modeling system randomness, validation, statistical analysis of simulation output data and project management. Figure 1.46 shows the steps that will compose a typical, sound simulation study [see also Banks et al. (2005, pp ) and Law (2003)]. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1. Formulate the problem and plan the study.
The number beside the symbol representing each step refers to the more detailed description of that step below. Note that a simulation study is not a simple sequential process. As one proceeds with the study, it may be necessary to go back to a previous step. 1. Formulate the problem and plan the study. Problem of interest is stated by manager. Problem may not be stated correctly or in quantitative terms. An interactive process is often necessary Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

FIGURE 1.46 Steps in a simulation study
Formulate problem and plan the study 2. Collect data and define a model Assumptions document valid? No 3. Yes 4. Construct a computer programme and verify 5. Make pilot runs No Programmed Model valid? 6. Yes Design experiments 7. Make production runs 8. Analyze output data 9. 10. Document, present, and use results FIGURE 1.46 Steps in a simulation study Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Overall objectives of the study
b. One or more kickoff meetings for the study are conducted, with the project manager, the simulation analysts, and subject-matter experts (SMEs) in attendance. The following issues are discussed: Overall objectives of the study Specific questions to be answered by the study (required to decide level of model detail) Performance measures that will be used to evaluate the efficacy of different system configurations Scope of the model System configurations to be modeled (required to decide generality of simulation programme) Time frame for the study and the required resources Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

c. Select the software for the model (see Chap.3)
2.Collect data and define a model. Collect information on the system layout and operating procedures. No single person or document is sufficient. Some people may have inaccurate information—make sure that true SMEs are identified. Operating procedures may not be formalized. b. Collect data (if possible) to specify model parameters and input probability distributions (see Chap. 6). Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Time and money constraints
c. Delineate the above information and data in an "assumptions document," which is the conceptual model (see Sec ). d. Collect data (if possible) on the performance of the existing system (for validation purposes in Step 6). e. Choosing the level of model detail (see Sec. 5.2), which is an art, should depend on the following: Project objectives Performance measures Data availability Credibility concerns Computer constraints Opinions of SMEs Time and money constraints Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

f. There need not be a one-to-one correspondence between each element of the model and the corresponding element of the system. Start with a “simple” model and embellish it as needed. Modeling each aspect of the system will seldom be required to make effective decisions, and might result in excessive model execution time, in missed deadlines, or in obscuring important system factors. h. Interact with the manager (and other key project personnel) on a regular basis (see Sec ). Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

3. Is the assumptions model valid?
Perform a structured walk-through of the assumptions document before an audience of managers, analysts, and SMEs (see Sec ). This will Help ensure that the model's assumptions are correct and complete Promotes interaction among the project members. Promote ownership of the model. Take place before programming begins, to avoid significant reprogramming later Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

4.Construct a computer program and verify.
a. Program the model in a programming language (e.g., C or C++) or in simulation software (e.g., Arena, Extend, Flexsim and ProModel). Benefits of using a programming language are that one is often known, they offer greater program control, they have a low purchase cost, and they may result in a smaller model execution time. The use of simulation software (see Chap. 3), on the other hand, reduces programming time and results in a lower project cost. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

b. Verify (debug) the simulation computer program (see Sec. 5.3).
5. Make pilot runs. Make pilot runs for validation purposes in Step 6. 6. Is the programmed model valid? If there is an existing system, then compare model and system (from Step 2) performance measures for the existing system (see Sec. 5,4.5). Regardless of whether there is an existing system, the simulation analysts and SMEs should review the model results for correctness. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Specify the following for each system configuration of interest:
c. Use sensitivity analyses (see Sec ) to determine what model factors have a significant impact on performance measures and, thus, have to be modeled carefully. 7. Design experiments. Specify the following for each system configuration of interest: Length of each simulation run Length of the warmup period, if one is appropriate Number of independent simulation runs using different random numbers (see Chap. 7)—facilitates construction of confidence intervals Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Production runs are made for use in Step 9.
8. Make production runs. Production runs are made for use in Step 9. 9. Analyze output data. Two major objectives in analyzing output data are: Determining the absolute performance of certain system configurations (see Chap. 9) Comparing alternative system configurations in a relative sense (see Chap. 10 and Sec. 11.2) 10. Document, present, and use results. a. Document assumptions (See Step 2), computer program, and study's results for use in the current and future projects. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

b. Present study's results.
Use animation (see Chap. 3) to communicate model to managers and other people who are not familiar with all of the model details. Discuss model building and validation process to promote credibility. c. Results are used in decision-making process if they are both valid and credible. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.8 OTHER TYPES OF SIMULATION
Continuous simulation Typically, solve sets of differential equations numerically over time May involve stochastic elements Some specialized software available; some discrete-event simulation software will do continuous simulation as well Combined discrete-continuous simulation Continuous variables described by differential equations Discrete events can occur that affect the continuously-changing variables Some discrete-event simulation software will do combined discrete-continuous simulation as well Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.8 Other Types of Simulation (cont’d.)
Monte Carlo simulation No time element (usually) Wide variety of mathematical problems Example: Evaluate a “difficult” integral Let X ~ U(a, b), and let Y = (b – a) g(X) Then Algorithm: Generate X ~ U(a, b), let Y = (b – a) g(X); repeat; average the Y’s … this average will be an unbiased estimator of I Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1.9 ADVANTAGES, DISADVANTAGES, AND PITFALLS OF SIMULATION
Simulation allows great flexibility in modeling complex systems, so simulation models can be highly valid Easy to compare alternatives Control experimental conditions Can study system with a very long time frame Disadvantages Stochastic simulations produce only estimates – with noise Simulation models can be expensive to develop Simulations usually produce large volumes of output – need to summarize, statistically analyze appropriately Pitfalls Failure to identify objectives clearly up front In appropriate level of detail (both ways) Inadequate design and analysis of simulation experiments Inadequate education, training Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

APPENDIX 1B A PRIMER ON QUEUEING SYSTEMS
A queueing system consists of one or more servers that provide service of some kind to arriving customers. Customers who arrive to find all servers busy (generally) join one or more queues (or lines) in front of the servers, hence the name "queueing" system. Historically, a large proportion of all discrete-event simulation studies have involved the modeling of a real-world queueing system, or at least some component of the system being simulated was a queueing system. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Thus, we believe that it is important for the student of simulation to have at least a basic understanding of the components of a queueing system, standard notation for queueing systems, and measures of performance that are often used to indicate the quality of service being provided by a queueing system. Some examples of real-world queueing systems that have often been simulated are given in Table 1.4. For additional information on queueing systems in general, see Gross and Harris (1998). Bertsekas and Gallager (1992) is recommended for those interested in queueing models of communications networks. Finally, Shanthikumar and Buzacott (1993) discuss stochastic models of manufacturing systems. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Examples of queueing system
TABLE 1.4 Examples of queueing system System Servers Customers Bank Tellers Hospital Doctors, nurses, beds Patients Computer System Central processing unit, input/output devices Jobs Manufacturing System Machines, workers Parts Airport Runways, gates, security check-in-stations Airplanes, travelers Communications network Nodes, links Messages, Packets Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1B.1 COMPONENTS OF A QUEUEING SYSTEM
A queueing system is characterized by three components: arrival process, service mechanism, and queue discipline. Specifying the arrival process for a queueing system consists of describing how customers arrive to the system. Let Ai be the inter arrival time between the arrivals of the (i — l)st and ith customers (see Sec. 1.3). If A1, A2,... are assumed to be IID random variables, we shall denote the mean (or expected) inter arrival time by E(A) and call λ == 1 /E(A) the arrival rate of customers. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

The service mechanism for a queueing system is articulated by specifying the number of servers (denoted by s), whether each server has its own queue or there is one queue feeding all servers, and the probability distribution of customers' service times. Let 5; be the service time of the ith arriving customer. If S1, S2. . .are IID random variables, we shall denote the mean service time of a customer by E(S) and call w = 1/E(S) the service rate of a server. The queue discipline of a queueing system refers to the rule that a server uses to choose the next customer from the queue (if any) when the server completes the service of the current customer. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Commonly used queue disciplines include
FIFO: Customers are served in a first-in, first-out manner. LIFO: Customers are served in a last-in, first-out manner (see Prob. 2.17). Priority: Customers are served in order of their importance (see Prob. 2.22) or on the basis of their service requirements (see Probs. 1.24, 2.20, and 2.21). 1B.2 NOTATION FOR QUEUEING SYSTEMS Certain queueing systems occur so often in practice that standard notations have been developed for them. In particular, consider the queueing system shown in Fig. 1.71, which has the following characteristics: Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

s servers in parallel and one FIFO queue feeding all servers
2. A1 A2 ... are IID random variables. 3. S1, S2, ….. are IID random variables. 4. The Ai’s and Si’s are independent. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

We call such a system a GI/G/s queue, where GI (general independent) refers to the ' distribution of the Ai's and G (general) refers to the distribution of the Si’s. If specific distributions are given for the Ai’s and the Si’s (as is always the case for simulation), symbols denoting these distributions are used in place of GI and G. The symbol M is used for the exponential distribution because of the Markovian, i.e., memory less, property of the exponential distribution (see Prob. 4.26), the symbol EK for a k-Erlang distribution (if X is a k-Erlang random variable, then , where the V/s are IID exponential random variables), and D-for deterministic (or constant) times. Thus, a single-server queueing system with exponential inter arrival times and service times and a FIFO queue discipline is called an M/M/l queue. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

1B.3 MEASURES OF PERFORMANCE FOR QUEUEING SYSTEMS
For any GI/G/s queue, we shall call the quantity p = λ / (sw) the utilization factor of the queueing system (sw is the service rate of the system when all servers are busy). It is a measure of how heavily the resources of a queueing system are utilized. 1B.3 MEASURES OF PERFORMANCE FOR QUEUEING SYSTEMS There are many possible measures of performance for queueing systems. We now describe four such measures that are usually used in the mathematical study of queueing systems. The reader should not infer from our choices that these measures are necessarily the most relevant or important in practice (see Chap. 9 for further discussion). As a matter of fact, for some real-world systems these measures may not even be well defined; i.e., they may not exist. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Di = delay in queue of ith customer
Let Di = delay in queue of ith customer Wi = Di + Si= waiting time in system of ith customer Q(t) = number of customers in queue at time t L(t) = number of customers in system at time t [Q(t) plus number of customers being served at time t] Then the measures w.p.1 w.p.1 and (if they exist) are called the steady-state average delay and the steady-state average waiting time. Similarly, the measures Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

w.p.1 and w.p.1 (if they exist) are called the steady-state time-average number in queue and the steady-state time-average number in system. Here and throughout this book, the qualifier "w.p. 1" (with probability 1) is given for mathematical correctness and has little practical significance. For example, suppose that (w.p. 1) for some queueing system. This means that if one performs a very large (an infinite) number of experiments, then in virtually every experiment converges to the finite quantity d. Note that p < 1 is a necessary condition for d, w, Q, and L to exist for a GI/G/s queue. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

(See Sec. 1.4.6 and also Sec. 11.5 for further discussion).
Among the most general and useful results for queueing systems are the conservation equations Q=λd and L = λw These equations hold for every queueing system for which d and w exist [see Stidham (1974)]. (Section 11.5 gives a simulation application of these relationships.) Another equation of considerable practical value is given by w = d + E(S) (See Sec and also Sec for further discussion). Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

It should be mentioned that the measures of performance discussed above can be analytically computed for M/M/s queues (s 1), M/G/1 queues for any distribution G, and for certain other queueing systems. In general, the inter-arrival-time distribution, the service-time distribution, or both must be exponential (or a variant of exponential, such as k-Erlang) for analytic solutions to be possible [see Gross and Harris (1998)]. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

For example, in the case of an M/M/1 queue, it can be shown analytically that the steady-state average number in system is given by L = p/(1 - p) Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

which we plot as a function of p in Fig. 1. 72
which we plot as a function of p in Fig Note that L is clearly not a linear function of p, and for p > 0.8 the plot of L increases exponentially. Although the formula for L is specifically for the M/M/1 queue, the nonlinear behavior seen in Fig is indicative of queueing systems in general. Another interesting (and instructive) example of an analytical solution is the steady-state average delay in queue for an M/G/1 queue, given by Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

where Var(S) denotes the variance of the service-time distribution [see, for example, Ross (1997, p. 444) for a derivation of this formula]. Thus, we can see that if E(S) is large, then congestion (here measured by d) will be larger; this is certainly to be expected. The formula also brings out the perhaps less obvious fact that congestion also increases if the variability of the service-time distribution is large, even if the mean service time stays the same. Intuitively, this is because a highly variable service-time random variable will have a greater chance of taking on a large value (since it must be positive), which means that the (single) server will be tied up for a long time, causing the queue to build up. Simulation Modeling and Analysis – Chapter 1 – Basic Simulation Modeling

Chapter 1 Basic Simulation Modeling

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Chapter 1 Basic Simulation Modeling

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