Discrete Event Modeling in a New Transportation Simulation Developments in Business Simulation and Experiential Learning, Volume 33, 2006 DISCRETE EVENT MODELING IN A NEW TRANSPORTATION SIMULATION David A. Jordan Geneva College dajordan@geneva.edu Michael L. Bruce Anderson University mlbruce@anderson.edu ABSTRACT This demonstration session will summarize the development a new service based transportation simulation as utilized by the Business Departments at Geneva College and Anderson University. The goals of this session are to present discrete event modeling as a valid business simulation technique and to introduce to current business professors, and/or interested individuals, a new service based simulation. This simulation seeks to model the decisions made by managers of a small trucking firm. It is hoped that this session will help attendees assess the benefits and challenges associated with using a service based simulation. INTRODUCTION This paper looks at discrete event modeling as applied to a new transportation simulation. Transportation companies schedule trucks for hauling loads based on availability. This simulation models ten small trucking firms that deliver various products to the marketplace. Nine teams are controlled by the computer and one team is played by the user. Each team begins with different cargo types (consists) but may branch out as the simulation progresses. DISCRETE EVENT MODELING In discrete event models, discrete entities change state as events occur in the simulation. Incoming orders, parts being assembled, and customers arriving are examples of discrete events. The state of the model changes only when those events occur; the passing of time has no direct effect. A factory that assembles parts is a good example of a discrete event system. The individual entities (parts) are assembled based on events, i.e. receipt of orders, machines available, etc. The time between events in a discrete event model is seldom uniform. Discrete event modeling uses a clock to move a set of entities along a timeline. While an entity moves along the timeline it encounters various events. Each event causes the state of the entity to change. Figure 1 depicts such a timeline with four events that affect Entity A. Entity A begins at event start at clock time 0. At clock time 2, Entity A begins activity 1. This activity takes 8 time units to complete. When event 2 occurs, at clock time 10, activity 1 is completed and Entity A continues until clock time 20 when event 3 takes place. Activity 2 begins with event 3 and continues until event 4 at clock time 35. Activity 2 takes 15 time units to complete. At clock time 50, Entity A has completed all processing, and event end occurs. In discrete event modeling, each entity must know which event is next and the time it begins. Each activity encountered takes a finite amount of time to complete. Figure 2 shows the values of the simulation variables (clock, next time, and next event) for Entity A as it is moved down the timeline described in Figure 1. For example, the clock variable is set to 0, next event is set to 1 and next event is set to 2 at the start of a simulation run. Upon reaching time 2, the clock is set to 2, next event is set to 2 and next time is set to 10. Figure 1 Example Timeline Event 1 Event 2 Event 3 Event 4 Entity A Start Clock 0 2 10 20 35 50 End Activity 1 Activity 2 341 mailto:dajordan@geneva.edu mailto:mlbruce@anderson.edu Developments in Business Simulation and Experiential Learning, Volume 33, 2006 Entity A Start Clock 0 Sta Entity B Entity C 1 2 With multiple en down the timeline. must know which en timeline. This requ activities to be perfor Figure 3 displays coordination. . Ther example that start a reaching the end of timeline. Each entity represents the system Figure 4 summa the first seven steps o begin the run, the clo advance to time 1 to and Entity A is starte time, which is start reaches time 2 and En start event for Entity continues until the clo Figure 2 Values of Simulation Variables for Example Timeline Clock Next Event Next Time 0 1 2 2 2 10 10 3 20 20 4 35 35 End 50 50 Figure 3 Multiple Entity Example Event 1 Event 2 Event 3 Event 4 5 7 10 30 50 End Activity 1 Activity 2 Event 1 Event 2 Event 3 Event 4rt End Activity 1 Activity 2 Event 1 Event 2Start End Activity 1 13 18 22 39 40 42 53 553 TRANSPORTATION SIMULATION tities, each entity is started and moved As the clock advances, the simulation tity has an event occurring next on the ires a check of all entities and the med, after each advance of the clock. an example of multiple entity e are three entities (A, B, C) in this nd change state several times before their respective advances down the is coordinated by the master clock that time. BusSim® Transport and BusSim® Supply model a small trucking firm that delivers various products to the marketplace. Each truck belonging to the firm transports a specific product from producer to market continuously over a three month period. Each truck is subjected to a series of events as it travels between producer and market. Some events are deterministic, that is they are determined by the player of the simulation, while other events are uncertain and controlled by probabilistic outcomes. rizes the necessary information to run f the example shown in Figure 3. To ck starts at time 0 and knows it must start Entity A. When it reaches time 1 d, the clock looks to the next event and Entity B at time 2. When the clock tity B is started, the clock sees that the C at time 3 is next. This process ck has reached the end of the timeline. Figure 5 shows the timeline (clock) and events modeled in these simulations. The dimension of the clock is in hours and shows there are 520 hours simulated each quarter. Trucks begin at their assigned terminal and move to their scheduled producer. All travel times and speeds are determined by the driver of the truck and the distance to the next event. After arriving at the producer the truck takes on its cargo. The load time varies. The truck then travels to the scheduled market and unloads its cargo. During any 342 Developments in Business Simulation and Experiential Learning, Volume 33, 2006 Terminal Trave 0 Term Ro travel activity, the truck may Three chance events, each wit and final locations, are modeled summarizes these chance events as many runs as possible in the respective terminals where preventive maintenance, may oc In BusSim® Transport te compete within a single industry by the computer and one team team begins with different carg branch out as the simulation pr user’s team begins with five tru to five different markets. As team may decide to haul fuel an equipment. Up to five trucks Figure 4 Multiple Entity Simulation Variables Clock Next Event Entity Next Time 0 Start A 1 1 Start B 2 2 Start C 3 3 1 A 5 5 1 B 7 7 2 A 10 10 2 B 13 l i a ex h i . q o cu n . is o o c p d Figure 5 Transportation Simulation Events 520 CLOCK Producer Market Breakdown Accident Terminal Travel Travel Travel Event Accident nal Breakdo d Breakdow perience a different ou n this simula When the tr uarter, they ther activit r. teams with Nine teams played by t types (con gresses. For ks each hau lay progress purchases t may be ass Figure 6 Chance Events Likelihood Lost Time Cost Safety Rating 1 - 4 Days 1,000 - 50,00 wn Prev Maint 1 - 2 Days 500 - 10,000 n Prev Maint 4 - 12 Hrs 100 - 5,000 chance event. tcomes, costs tion. Figure 6 ucks complete return to their ies, such as similar goals are controlled he user. Each sists) but may example, the ling chemicals es, the user’s he appropriate igned to each producer/market route. Figure 7 shows the list of producers and their products that may be purchased and transported by any firm. Figure 8 displays an example of one of the most important reports in this simulation. It shows the results for each truck that transports goods from producer to market. Information displayed includes cargo type, producer, market, loads delivered, revenue generated, truck profit, speed of advance, total distance traveled in miles, delay time in hours, number of breakdowns and the home terminal for each truck. A demonstration of this simulation will take place during the annual conference. We will also present some spreadsheet tools that are provided to the students in their analysis. 343 Developments in Business Simulation and Experiential Learning, Volume 33, 2006 Producer 1 2 3 4 5 6 7 8 9 10 Figure 7 Producer Data Product Capacity Price Location Chemicals 1,600 $1,250 0 Fuel 2,500 $2,700 11 Steel 1,200 $4,600 22 Lumber 2,000 $750 33 Grain 2,500 $400 44 Autos 1,500 $19,650 55 Dairy 3,000 $600 66 Meat 3,000 $650 77 Produce 2,500 $500 88 Goods 3,000 $950 99 Figure 8 Example Truck Report 344 Table of Contents Volume 33, 2006 Learning Assurance Using Business Simulations Applications To Executive Management Education Team Teaching In An Integrated Business Course Using Critical Problem Based Learning Factors In An Integrated Undergraduate Business Curriculum: A Business Course Success Personality Type And Strategic Planning Business Games As Strategic Management Laboratories The Relationship Between Students' Success On A Simulation Exercise And Their Perception Of Its Effectiveness As A PBL Problem Forecasting Accuracy And Learning: The Key To Measuring Simulation Performance The Business Strategy Game: A Performance Review Of The New Online Edition Using The Socratic Method And Bloom's Taxonomy Of The Cognitive Domain To Enhance Online Discussion, Critical Thinking, And Student Learning Using Negotiation Exercises To Promote Critical Thinking Skills Effective Leadership Experiences For Management Majors In A Futures Class The Role Of Learning Versus Performance Orientations When Reacting To Negative Outcomes In Simulation Games Is Pay Inversion Ethical? A Three-Part Exercise Simulations And Experiential Exercises - Do They Result In Learning? Have We Figured It Out Yet? Examining Program Management In Business Simulations: Student And Faculty Views Validating Business Simulations: Do Simulations Exhibit Natural Market Structures? Characterizing Business Games Used In Distance Education Utilizing Games In A Graduate Level Instructional Game Course Employment Interview Preparation: Assessing The Writing-To-Learn Approach Simulations - Bridging From Thwarted Innovation To Disruptive Technology Creating An Authentic Cultural Lens Using Case Dialogue Learning By Fire: Reflections Of A First Time Online Instructor An International Internship With A Service-Learning Focus Learner Participation In The Online Learning Experience: Help Or Hindrance? Any Given Sunday: Intervention In Pursuit Of Simulation Team Parity Beginning With The End: Creating An Experiential Exercise From Assessment Criteria Simulating Life Cycles: Life Span As The Measure Of Performance In Business Gaming Simulations It's Puzzling: Communications, Competition, And Cooperation Balanced Scorecard Implementation For Strategy Management: Variation Of Manager Opinion In Real And Simulated Companies Cases And Business Games: The Perfect Match! Three-Attribute Interrelationships For Industry-Level Demand Equations Using A Web-Based Module To Teach Information Literacy Decision Support System For Demand Forecasting In Business Games The Invalidity Of Profit=F(Market Share) PIMS Validation Of Marketing Games Online Market Test Laboratory With The MINSIM* Program The Gas Mileage Game - A Policy Simulation Delivered Cost And Differentiation Applied To Threshold 3rd Ed. The Effect Of Team-Leadership Modes On Team Performance: A Preliminary Study The Design And Use Of A Macroeconomics Simulation Using Maple Software: A Pilot Study The Instructor's Toolbox: A Meaning-Centered Framework For The Social Construction Of Experiential Learning Incorporating Strategic Product-Mix Decisions Into Simulation Games: Modeling The 'Profitable-Product Death Spiral' Group Composition And Groupthink In A Business Game A Direct Approach To Teaching Business Ethics A Decision Support System For Planning Sales, Production, And Plant Addition With Manager: A Computer Simulation Polish - American Entrepreneurial Business Cooperation Workshop Utilizing The Income/Outcome Simulation Student Leader Training Exercise Student Preference To Mode Of Learning In Hong Kong Experiential Learning For Technology-Based And Management Programme In Hong Kong: A China Study Tour A Price Game With Product Differentiation In The Classroom Discrete Event Modeling In A New Transportation Simulation Supply-Side Modeling In A Total Enterprise Simulation The Quality Game Towards A Massive Multiplayer Online Business Simulation Making The Connection: Improving Virtual Team Performance Through Behavioral Assessment Profiling And Behavioral Cues Individual Learning Producing A Learning Organization: 'Playing Dice With Polar Bears' Narratology and Ludology: Competing Paradigms or Complementary Theories in Simulation Framework For Evaluating Internet Research Using Children's Games To Illustrate Strategy Concepts: Is Less Better?