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Simulation Using ProModel Dr. Charles Harrell Professor, Brigham Young University, Provo. 1 Introductionto Simulation 3 1.1 Introduction 3 1.2 WhatIs Simulation? 5 1.3 WhySimulate? 7.4.3 ValidationExamples 205 7.5 Summary 209 7.6 ReviewQuestions 210 References 210 8 SimulationOutputAnalysis 211.

Customer places the order 4. Customer waits in queue for food item 5. Customer gets the food item 6. Customer waits in queue for hot/cold beverage 7. Customer gets hot/cold beverage 8. Customer leaves the system There are certain decision modules which decide the path of the customer in the Model.

High utilization b. High Waiting time in queues Also, the customers spend more than twice the time waiting in the queue as compared to the time when they are being served. The efficiency of the Starbucks store would increase when the average total time spent by a customer would decrease. According to the above interpretations, it can be concluded that some improvements in the above two resources is needed in order to reduce the average time in the system.

Also, the model consists assign and record modules to calculate the average customer time in the system and the number of customers who bought food items and hot, cold beverages. The important parameters in the Arena model are the Resources and Queues. Here is an overview of the model parameters – Arena Starbucks Type / Action 1. Entities Customers Part 2. Resource 1 Cash Counter Person Seize Delay Release 5. Resource 2 Food Item Server Seize Delay Release 3. Resource 3 Hot Beverage Server Seize Delay Release 4.

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Original Model with capacity 1 for each resource. Food Item resource present. Cash 2 Model with capacity 2 for Cash counter resource. Food Item resource absent. Cash and Hot 2 Model- capacity 2 for both Cash Counter and Hot Beverage Resources. Food Item resource absent.

This text blends theory with practice presenting actual applications in business, services and manufacturing. General topics include simulation basics, planning, data collection and analysis, model building, model verification and validation, output analysis and experimental design. Additionally, this is the first simulation text that covers simulation optimization using modern techniques. For a practical emphasis, complete chapters are devoted to typical modeling issues encountered in manufacturing, material handling and service systems.

Patient flow and staffing are always a top concern for hospitals, but it’s important to remember that utilization levels that are too high are just as bad as levels that are too low, and one of the benefits of simulation in healthcare is the ability to staff to demand. Check out Dale’s work with Robert Wood Johnson University Hospital where they successfully used simulation to manage increased OR patient volume. About Dale Since joining ProModel in 2000, Dale has been developing simulation models used by businesses to perform operational improvement and strategic planning. Prior to joining ProModel Dale spent seven years as a Sr. Corporate Management Engineering Consultant for Baystate Health System in Springfield, MA where he facilitated quality improvement efforts system wide including setting standards and facilitating business re-engineering teams. Earlier he worked as a Project Engineer at the Hamilton Standard Division of United Technologies. Dale has a BS in Mechanical Engineering from the University of Michigan and a Masters of Management Science from Lesley University.

The first three approaches model the underlying tolerance for waiting implicitly. Model 4 allows tolerance variation among customers to be modeled explicitly. A simulation example of Model 3 is presented. The purpose is to demonstrate the structure of the model and not model efficiency and compactness. The model includes a single server, FCFS discipline, random arrival and service. The conditional probability distributions of balking behavior are presented in the table.