Real-World Simulation Examples for Student Learning

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Prof Hassan Rajaei Ph.D. – Bowling Green State University; Department of Computer Science

Objectives

Simulation is a powerful tool for teaching students about the techniques as well as providing deeper understanding of courses such as networking, operating systems, operational research, just to name a few. Simulation is a well-known technique for evaluating what-if scenarios and decision making in industry, defense, finance, and many others. Students quickly realize these values and want to learn how to master this technique.

Teaching simulation techniques often requires attractive problem assignments. Real-world has numerous examples that excite students to study and motivate them focusing on their learning objectives. Further, it challenges them to develop models to reflect the reality. Clear examples can teach students how to collect data, develop the base model, improve it to advanced models, analyze the obtained results, and think about the usability of their simulation results. These learning outcomes can clearly demonstrate valuable educational objectives.

A simulation tool like ProModel has numerous example models in its library, but the educational objectives can be best achieved through step-by-step experimental development of useful samples. ProModel can be a great help by exploring the details of similar examples.

This article, presents an example where a group of students developed a simulation model for the Bowling Green State University (BGSU) Students Union Cafeteria. Managing a university dining hall often exhibits challenges for the food services located in it. This study focused on reducing the average waiting time of the diners, while increasing overall efficiency of the services.

Simulating the Nest Cafeteria

This project focused on finding solutions for the Falcon’s Nest Cafeteria to increase the efficiency and decrease the average time of the customer spent in the system.

Overview of the Nest: Students cafeteria at BGSU functions as an important part of the University’s dining service. This cafeteria serves thousands of students every day. During the rush-hours of lunch and dinner, this place gets really congested with long queues contributing to long waiting times. In this simulation, the Nest model consists of five main components: Customers, Servers, Locations, Queues, and Cashiers.

Using ProModel: This tool was selected for multiple reasons: a) the availability; b) the course used the tool and trained students; c) the tool supports discrete-event systems; d) large number of library models; e) statistical analysis and output results; f) animations.

Problems Encountered: The main problem faced was lack of statistics and accurate information. Other barriers included project time limit and lack of deeper familiarity of ProModel.

Possible Solutions: Based on primary analysis, two potential solutions were feasible:

1) Increase the attractiveness of other food stations which have lower waiting time;

2) Increase number of food servers.

Three approaches to reach the goals:

a) Ask the SME to provide all data and statistics;

b) Make a very detailed model over the actual system;

c) Combination of (a) & (b) methods.

Approach c was adopted for the study.

Simulation Models

Four models were developed: 1) base, 2) intermediate, 3) advanced, 4) final

Base Model: The base model had very basic setups with one food station and one cashier. The objective was to test the station service and the customers’ arrival, and their flow in the system.

Intermediate Model: All food stations were added according to the Nest along with the logic for entities to move through the system with a shared queue.

 Advanced Model: The advanced model includes all queues targeting to obtain realistic statistics using several scenarios (Figure 1).

Figure 1

Figure 1:  The Advanced Model improved from the intermediate one

Final Model: After developing three scenarios, obtaining good confidence, making sure they were on the right track, students moved towards developing the final model shown in Figure 2. It was implemented using a time schedule to simulate the rush hour and normal operating hours.

Figure 2

Figure 2:  Final simulation model for the Falcon’s Nest Cafeteria

Results and Analysis

In this simulation, students first aimed to find an ideal solution to demonstrate how to reduce the waiting time. It turned out that such a scenario would need more implementation time. Instead, students focused on two solutions:

1) To make other food stations more attractive;

2) Adding additional workers to the top three food stations. Test cases were developed for each solution.

The result shown in Figure 3 demonstrate a reduction in the average time compared to the baseline, except Case 3. The figure suggests an 11.1% decrease in average time spent in the Nest.

Figure 3

Figure 3: Solution 1 demonstrating reduction of Average Time in the system

Next method focused on improving the waiting time by adding food runners to 3 populated stations. This method was simulated and tested with 4 scenarios, and was compared with the baseline.

Figure 4

Figure 4: Solution 2, advocating one additional worker at each food station

As was expected, by adding a food-runner to each station the average time of the customers would decrease, however, certain stations would benefit most. If case 4 is adopted, there would be a 12.6% reduction in time spent by customers. If only 1 food runner is added, then the result yields only to 6.1% decrease in average time spent in the system by customers.

Concluding Remarks

This article presents an example of a real-world case study conducted by a group of students as a term project in a simulation techniques course shared by senior undergraduate students as well as graduate students. An important result of this study demonstrates how deeply the students were engaged in their learning objectives of the course. In a short period of time, they conducted a complete case study including: observation, gathering data, analyzing the problem at hand, developing models, confirming with the subject matter expert, documenting, and delivering the results. The full article is published in ASEE 2017 Annual Conference.

Professor Hassan Rajaei Ph.D.  

Hassan Rajaei is a Professor of Computer Science at Bowling Green State University, Ohio.  His research interests include Distributed Systems & IoT, Cloud Computing, High Performance Computing (HPC), Computer Simulation, Distributed Simulation, with applications focus on communications & wireless networks. Dr. Rajaei has been active in simulation conferences (e.g. SCS SprintSim, WSC) as organizer as well as research contributor. Dr. Rajaei received his Ph.D. from Royal Institute of Technologies, KTH, Stockholm, Sweden and he holds a MSEE from Univ. of Utah.

ProModel/MedModel 2018 What’s New Webinar

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We will be conducting a live ProModel / MedModel 2018 Release Webinar on Wed Nov 15 from 1-2 pm ET.

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The webinar will give you a look at the updated look and feel of the application’s more modern, fluent user interface that provides more ease and control of your model building experience. This significant version will include such features as a Ribbon Toolbar, Docking Windows, and Right-Click Context Menus as described below:

  • Ribbon Toolbar: The traditional menus and toolbars are being replaced with a fluent Ribbon toolbar like you find in Microsoft Office applications. The new Ribbon will make it easier to access the various modules and features within the application and better facilitate touch screen and high-resolution devices.

Ribbon Toobar screen shot

  • Docking Windows: Windows will be docked within the new workspace interface, which means that when you adjust the size of one window, the others automatically resize accordingly. Say goodbye to overlapping windows. You will also be able to stack windows on top of each other and quickly access them from their respective tab thus saving valuable view space.

Dock Screen Shot

  • Right-Click Context Menus:  Context menus will be available in every table and accessible by right-clicking in any field within that table. For example, you will be able to quickly delete, insert or move a record with a simple right-click of the mouse.

Right Click Screen Shot

Join the webinar to hear all about what’s new in the ProModel / MedModel 2018 Release on Wed Nov 15 from 1-2 pm ET.

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Ancillary Tools Helpful for a Successful ProModel Discrete Event Simulation Project

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Prof. Scott Metlen Univ. of Idaho

Introduction

Using ProModel to teach process management inadvertently necessitates that students become more proficient with many tools centered on data and working with people. Of course, students learn many aspects of ProModel such as the need to understand parts of a process; these include locations, entities, arrival rates, process logic (LEAP), variables, and attributes.

They also learn about graphics, Statfit, batch/group, create, order, wait until, logic statements that operationalize business rules, and many other commands that help to model a process. However, when conducting a successful large process improvement project using discrete event simulation for an organization, students need to become proficient with many other tools to best utilize ProModel over the course of semester long projects.

Project Management Methodology

Understanding and being able to set up a project using project management methodology is critical to having a successful ProModel project. As in any project the scope and expected outcomes need to be delineated. To design the work break down structure for the project it is also critical to understand what tasks need to be accomplished to produce the final output, and when those tasks need to be completed.

Tasks include developing the scope and expected outcomes working with the project sponsor, analyzing and preparing data for entry into ProModel, base model construction, verification and validation of the base model, determining what treated models to build, statistical analysis of the outputs from each model relative to the base model, cost/benefit analysis, and a report delineating findings and recommendations. Each team in the class I guide has to complete a Project Execution Plan (PEP) and then discuss in their final paper how well they met their time gates, why they did or did not meet those dates, and what they did to catch up if they did not meet those time lines. There are times in the project where they learn the lesson of not utilizing the ‘student syndrome’.

Relationship Management

To do a good job of all the tasks mentioned above, students have to become accomplished at relationship management. They have to visit with their sponsor not only about the scope and expected outcomes, but what data and information is needed to complete the project. There will be missing data, acronyms that need to be explained, assumptions that have to be made and supported due to the missing data and information, uncertainty about the proper rule to guide the logic, and many other items to discuss on at least a weekly bases with the sponsor. Oftentimes it is being uncomfortable talking to a sponsor that leads to procrastination and missed time gates.

Data Sets and Simulation

Of course there is the ever present need to be able to make sense out of large sets of data and be able to convert them to information that ProModel can utilize. When dealing with nearly 12,000 different types of entities for one process, being processed through a job shop with 1400 unique process centers, the data sets become large. The route array that informs ProModel which machine which entity goes to when can become 12,000 rows and 200 columns, and the duration array can become too large and have to be split into four arrays, each with 3000 rows and 1400 columns.

There are many Excel tools that help the students explore their data sets. These tools include but are not limited to: filters, pivot tables, different types of lookup commands, find and replace, if statements, count statements, the ‘and’ function to build many lines of logic quickly, and different types of conditional formatting.

Once the base model and treated models are created and have generated 30 replications the students determine if the treatments actually made a difference by conducting a hypotheses test, if the null (the means of the samples have a high probability of being drawn from the same population) is rejected they proceed to the cost/benefit analysis. If the null is accepted, they try other treatments. If the treatment was successful, Statfit is utilized to determine the distribution of the output, at which point a Monte Carlo simulation is utilized to generate a larger sample of deltas between the base and a treated model to determine the distribution of deltas used to generate the net benefit.  That benefit could be number of extra units built, decrease in throughput time, time in system, net present value, or some other form of benefit.

Report Out

While the models are being built, the team is also working on their presentations and written reports. Thus, as they are discovering assumptions that they need to make, they are putting them into the oral and written report, thereby learning the value of parallel processing. By the time the last statistical analysis is completed, the presentation and paper are completed and ready for presentation for the teams that do a good job of following their PEP.

Conclusion

As demonstrated above there are many tools that ProModel users need to be proficient with when conducting a successful discrete event simulation using ProModel. However, perhaps it is not only ProModel and the ancillary tools that need to be taught and modeled when teaching a discrete event class, but the willingness to say, “I do not know how to do that, lets do some research and discover how”. That is the most important trait that modelers need to have, the willingness and perseverance to learn new tools and apply them in unique ways to capture unique opportunities.

Meet Professor Scott Metlen, Ph.D.

Dr. Scott Metlen earned his Ph.D. in Business Administration at the University of Utah in 2002 and is currently an associated professor of Production Operations Management at the University of Idaho. Dr. Metlen teaches Quality Management and Systems and Simulation, both are aspects of  Process Management. Prior to his academic carrier, Dr. Metlen spent 20 years managing products and processes in agriculture and food processing. Through a gift from the Micron Foundation, he has the resources to oversee at least twenty process improvement projects for various organizations per year through the classes he teaches. These projects provide meaningful experiential learning for the 40 to 80 students involved.

 

Brazilian Academic Simulation Awards Given in Honor of Rob Bateman

ProModel friends and associates, last October 12 we lost a dear friend, Rob Bateman and it is very hard to believe that a year has already passed.  Coincidentally, just a few days before the loss of our colleague, on October 6, 2015, the first ever ‘Rob Bateman’ award was delivered in the city of Joao Pessoa (north east coast of Brazil).  Here is the web site of the event:  http://www.abepro.org.br/enegep/2016/index.asp.  The Simula Brazil is a national award for simulation systems, organized and hosted by the portal “www.simulacao.net” which is sponsored by the Belge Consulting (www.belge.com.br). The award has institutional support of ABEPRO (www.abepro.org.br) and SOBRAPO (www.sobrapo.org.br) and is linked to the National Production Engineering Meeting (ENEGEP).

This award aims to encourage young students to use more simulation technology to develop projects and analyze real or fictitious situations through the use of the ProModel modeling and simulation technology (ProModel® or MedModel®) as well as assisting teachers with simulation education. The hope is that this practice will allow for better industrial engineering courses using ProModel and more simulation use in local companies, as well.  This year the award was given to the following recipients:

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Marcelo Fugihara of Belge presenting the award for originality to Jacyszyn Bachega of Universidade Federa de Goias

 

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Marcelo Fugihara of Belge presenting the award for complexity to Thiago Fernando Rosa Tedoro and Professor Jose Lazaro Ferraz of Universidade FACENS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Here is a photo of all of the students in attendance at the event, called Enegep –  Encontro Nacional de Engenharia de Produção

We hope that this award in some small way pays tribute to our friend Rob Bateman.

Your friend in Simulation,

Alain de Norman & Belge team.

Simulating Impatient Customers-Reneging

Dr. Farhad Moeeni - Professor of Computer and Information Technology

Dr. Farhed Moeeni – Prof. of Computer & Information Technology, Arkansas State University

GUEST BLOGGER – Dr. Farhed Moeeni

In an earlier blog I discussed some of the issues related to modeling customers’ behavior; such as impatience, especially the balking behavior.  Customers’ impatience can lead to certain behaviors such as balking, reneging and jockeying.  Ignoring the case of balking at arrival caused by queue capacity, balking happens when customers assess the waiting line, develop some opinion about the required waiting time (likely by scanning the queue length) and decide whether to join the queue or not. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival.  Jockeying happens when two or more service channels provide identical services, independently and with distinct waiting lines.  A customer who is presently waiting in one of the queues chooses to leave the current line and join another queue in the hopes of less waiting time.

Reneging, however happens after a person joins a queue.  In other words, the initial anticipated delay was tolerable.  But later, as time passes, the person becomes impatient and abandons the queue without receiving the service because the prospect of being served within the initially anticipated time diminishes or the predicted remaining waiting time exceeds the customer’s tolerance threshold for further waiting.  Other incidents such as receiving an urgent call may also cause abandoning of the queue.  However, this or similar scenarios are not caused by impatience.

Researchers believe the tolerance threshold is not a constant and can change over the course of the waiting period.  For example, studies indicate that the probability of reneging decreases as the person continues to wait, but only for a certain amount of waiting time after which the customer starts losing patience and the probability of reneging increases.  Another interesting conjecture proposed in the literature is that as the number of people behind a waiting customer increases, the likelihood of the customer reneging decreases.  The decision to renege is also influenced by other factors, for example, personal differences, prior experiences with similar situations, the critical nature of the service to be received, attributes and atmosphere of the waiting area’s surrounding (service-scape), and whether or not an alternative date or time for the same service or a similar service is available. Thus, the decision process for reneging is more complex than balking and modeling and simulating reneging is also more complex.

Obviously, it is not possible to directly characterize the complex behavioral aspects of customers into the simulation model.  Each reneging incident is the result of complex interactions among many factors and the result of a thought process that eventually causes a customer to become impatient and abandon the queue. In other words, reneging cases are not random events; however, from the perspective of a simulation modeler or an observer who is watching the queue, the incidents may look like a random process.

In discrete-event simulation, the modeler has a number of mechanisms or tools available. For example, the modeler may apply some probability distribution to characterize the uncertain behavior of customers or formulate some rules to trigger reneging during the simulation runs.  In addition to probability distributions, the modeler may also identify and apply a number of observable predictor variables to fine tune the reneging behavior.  These variables may include the waiting time of each customer in queue before reneging incidents, the position of each customer in queue just before reneging, the number of customers in queue behind the reneging customer at the moment of abandoning the queue, etc.

Incorporating the above mechanisms is not too complex from the modeling and coding perspective. On the other hand, it is difficult to collect relevant data from the field if the required information is not available. A number of obstacles can contribute to these difficulties. First, sufficient observations are needed to estimate the probability distributions, the shapes and parameters with adequate precision.  Unfortunately, in many instances, reneging does not happen often enough to yield sufficient observational data from the field within a reasonable amount of time.

Second, accurate data collection for events such as reneging may need sophisticated data collection procedures because each customer should be tracked while in the system and the values of relevant state variables along with other important information such as the “waiting time in queue before reneging” and “the position of the customer in queue” before abandoning the queue should also be recorded.   Only the information related to those customers who renege is needed. However, those who eventually renege are not known in advance or at the time of joining the queue; thus everyone should be tracked.  Such elaborate data collection system may be costly to implement, intrusive to customers, or may need consent and collaboration from people.  The latter may also influence the behavior and contaminate the collected data.

I discussed some of the important issues surrounding customer’s reneging behavior in simulation.  The focus of the discussion has so far been on physical queues or waiting lines in which customers should be physically present to receive the service.  Some of the complex characteristics and thought processes associated with physical queues may or may not apply to virtual queuing systems such as call centers, web servers, e-commerce sites and the like.  One simplifying advantage of modeling human impatience in virtual queues is that much of the data needed to model reneging behavior may readily be collected electronically without facing the obstacles explained above. Virtual queuing systems have unique characteristics and deserve a  separate discussion.

Check -out more information about Dr. Moeeni.  If you would like more information about ProModel Simulation and simulation studies about queues check out the ProModel services industry information. Contact ProModel to learn more.

 

 

 

 

Teaching Supply Chain Management with ProModel

profshannonPatrick W. Shannon, Ph.D., is a professor of operations and supply chain management at Boise State University. He taught graduate and undergraduate courses in business statistics, quality management, lean manufacturing and other areas of operations and supply chain management. Professor Shannon developed a curriculum for his supply chain class, using ProModel Simulation which he used for over 10 years.

To provide you some insight into how you can use ProModel in the classroom, Professor Shannon was kind enough to allow us to share the materials he used.

Attached are PDFs of his course materials.

  1. Tri-Star Manufacturing: A Case Study in Lean Implementation
  2. The Tri-Star Simulation Model
  3. Project Requirements and Rules
  4. ProModel Instructions

Dr. Shannon served as dean of the College of Business and Economics from 2008-2014 and has lectured and consulted on statistics, lean manufacturing and quality management, project management, statistical modeling, and demand forecasting for over thirty years. He has co-authored 11 university level textbooks, and he has published numerous articles in such journals as Decision Sciences Journal of Innovative Education, Business Horizons, Transportation Research Record, Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research, Quality Management Journal, and The International Journal of Quality and Reliability Management.

He completed his BS and MS at the University of Montana and his Ph.D. in Statistics and Quantitative Methods at the University of Oregon. In 2015 he presented at the National Kidney Registry (NKR) Symposium in New York City. The presentation, authored by Shannon and Phil Fry, professor of operations management, is titled “Kidney Life Years” and describes the research Fry and Shannon have conducted with the NKR. The purpose of the research is to develop a statistical model to identify the donor characteristics that impact the length of time live donor kidney transplant will last.

Click here to view his LinkedIn Profile.

If you are a professor interested in learning more about ProModel’s Academic offerings, please email cbunker@promodel.com for more information.  You may also check out the following: www.promodel.com/industries/academic

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Remembering Rob Bateman

Photo March 2009

Charles Harrell, Founder ProModel Corporation

Last month we lost a long-time member of the ProModel family and, for many of us, a beloved friend. Following a sudden incident of heart failure while working out in the gym, Rob Bateman passed away on October 11th 2015.  We at ProModel will remember him as a warm, energetic, impassioned leader and friend whose life was devoted to the pursuit of excellence and selfless service. His absence will continue to be profoundly felt in the months and years to come.

I first met Rob just over 25 years ago when he was doing graduate studies at BYU. He took a simulation class from me and I could tell he was excited about the potential benefits of simulation. So after completing a stint with the US state department as a foreign-service officer in 1990, Rob began working as a ProModel distributor. With his international background and grasp of simulation, Rob eventually become the Vice President of International Operations and later established an independent company (Dynamisis A.G.) for directing all international operations for ProModel.

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Robert Bateman

(April 4, 1958 – October 11, 2015)

Rob was an extraordinary individual with remarkable talents. He was one of those individuals who was always on-the-go and seemed to cram more into one day than most of us manage to accomplish in several days. At the same time, he maintained a zest for life and could frequently be seen buzzing around in his sports car with his signature driving cap or biking into work in his cycling shorts and helmet.

Here are a few of the many talents Rob displayed:

  • He was very knowledgeable…about everything. No matter what subject was being discussed, he always had something intelligent to contribute to the discussion. On top of his formal education, which culminated with a Ph.D. in Public Administration/Political Science, Rob filled his spare moments reading books or one of his 14 magazines he subscribed to.
  • As a consummate teacher he was passionate about getting people exposed to simulation. He wrote the first textbook on ProModel for use in college courses. For the past decade, when he wasn’t working with distributors to promote ProModel he was teaching at the local university.
  • He was an effective mentor and gave many individuals their first start in their careers. When several international distributors were asked what they remember about Rob, they invariably said he treated them as valued partners and became someone they could always turn to for advice.
  • He was resourceful and knew how to get by on very little sleep, food and comforts. When there wasn’t sufficient budget or resources to support an initiative he believed in, he somehow always managed to find the means needed to get the job done.
  • He was a real cosmopolitan and world traveler. If you ever called Rob, you would be just as likely reaching him at some airport as in his office. And there didn’t seem to be any country where he felt uncomfortable or couldn’t speak the language.
  • He was a friend to all and he never let business stand in the way of personal relationships. He took time to express an interest in others and always sensed if one was having a bad day or dealing with problems at home. He would do whatever he could to lift them up and help them keep things in perspective.
  • Finally, Rob was funny and had an infectious sense of humor. He could tell endless stories of his travel exploits where he encountered bizarre situations like returning to his car only to find all of his tires stolen. Though Rob took his commitments seriously, he never took himself too seriously.

Here are a few memories related by some of the distributers who worked with him.

A Distributor in Germany and Austria relates, “On my first trip to Utah to visit with Rob as a new ProModel rep, I had the feeling I was meeting with an old friend. I was impressed by his hospitality and the time that he gave me.”

A Brazilian distributor recalled meeting Rob the first time 21 years ago and thinking to himself, “Who is this guy who can conduct a meeting with high level business leaders, comfortably use legal and business terms in both German and English and then turn around the following day and teach a simulation course in Spanish to a group of engineers. How can one person have so many skills?”

As another of his co-workers commented, “I’ve been in rooms with him teaching and negotiating with Nigerians, Germans, Japanese, Brazilians, Mexicans, and more. No matter the nationality, Rob could relate and connect. He was confident, knowledgeable, and personable.”

On a personal note, one co-worker related: “This past year Rob joined the cycling team that I belong to called Team C4C (“Cycle for Cure”). The team was formed to raise money for health-related charities such as the Huntsman Cancer Center and the National MS Society. Rob immediately identified with the purpose of the group and quickly became one of the strongest riders on the team.”

This same co-worker related how Rob was instrumental in helping him complete a grueling ‘Ultimate Challenge’ cycling event saying, “I will always cherish a picture I have of Rob and me crossing the finish line together at Snowbird after riding 100 miles and climbing 10,000 feet in one day. I could not have made it without his encouragement along the way.”

For all those who have been influenced by his exemplary life, Rob will always be remembered as a leader, mentor and friend. Perhaps it is fitting that he pursued a career in simulation modeling since he seems to have understood the impact that models can have, not only on organizations, but on the people around him. Those who knew Rob, know that he was a model of the best that a human being can be, and for that he will always be remembered.