Objectives Learn how to define Referenced Properties Use Controls to define multiple scenarios in experiment Evaluate different configurations of a Subway.

Slides:



Advertisements
Similar presentations
Waiting Line Management
Advertisements

Model Antrian Ganda Pertemuan 21 Matakuliah: K0414 / Riset Operasi Bisnis dan Industri Tahun: 2008 / 2009.
MGTSC 352 Lecture 23: Inventory Management Big Blue Congestion Management Introduction: Asgard Bank example Simulating a queue Types of congested systems,
QUEUING MODELS Based on slides for Hilier, Hiller, and Lieberman, Introduction to Management Science, Irwin McGraw-Hill.
1 Waiting Lines Also Known as Queuing Theory. 2 Have you ever been to the grocery store and had to wait in line? Or maybe you had to wait at the bank.
Simulation of multiple server queuing systems
Previously Optimization Probability Review Inventory Models Markov Decision Processes.
Model Antrian By : Render, ect. Outline  Characteristics of a Waiting-Line System.  Arrival characteristics.  Waiting-Line characteristics.  Service.
Waiting Lines and Queuing Theory Models
HW # Due Day: Nov 23.
Multiple server queues In particular, we look at M/M/k Need to find steady state probabilities.
Managing Waiting Lines
1 Analysis Of Queues For this session, the learning objectives are:  Learn the fundamental structure of a queueing system.  Learn what needs to be specified.
Simulation with ArenaChapter 2 – Fundamental Simulation Concepts Discrete Event “Hand” Simulation of a GI/GI/1 Queue.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.
Example 14.4 Queuing | 14.2 | 14.3 | 14.5 | 14.6 | 14.7 |14.8 | Background Information n Which system has the.
Introduction to Arena A Simple Simulation. Model1 We examine a simple model: parts arrive at a server, are served, and depart the system. There will be.
1 Multiple class queueing networks Mean Value Analysis - Open queueing networks - Closed queueing networks.
CHAPTER 18 Waiting Lines.
Module C10 Simulation of Inventory/Queuing Models.
Queuing. Elements of Waiting Lines  Population –Source of customers Infinite or finite.
Waiting lines problems
HW # Due Day: Nov 23.
Model Antrian By : Render, ect. M/M/1 Example 2 Five copy machines break down at UM St. Louis per eight hour day on average. The average service time.
1 Ardavan Asef-Vaziri Sep-09Operations Management: Waiting Lines3  Terminology: The characteristics of a queuing system is captured by five parameters:

Queuing Theory (Waiting Line Models)
Buffer or Suffer Principle
DC Cafeteria Simulation Exit Entrance Mr. SubChopsticks Manager of DC caf would like a simulation of the customer activity in a day. You are given the.
Service Systems & Queuing Chapter 12S OPS 370. Nature of Services –A
1 Chapter 16 Applications of Queuing Theory Prepared by: Ashraf Soliman Abuhamad Supervisor by : Dr. Sana’a Wafa Al-Sayegh University of Palestine Faculty.
Introduction to Queuing Theory
4/11: Queuing Models Collect homework, roll call Queuing Theory, Situations Single-Channel Waiting Line System –Distribution of arrivals –Distribution.
Supplement D Waiting Line Models Operations Management by R. Dan Reid & Nada R. Sanders 3rd Edition © Wiley 2005 PowerPoint Presentation by Roger B. Grinde,
Introduction to Queueing Theory
Supplement C Waiting Line Models Operations Management by R. Dan Reid & Nada R. Sanders 4th Edition © Wiley 2010.
1 Queuing Models Dr. Mahmoud Alrefaei 2 Introduction Each one of us has spent a great deal of time waiting in lines. One example in the Cafeteria. Other.
Waiting Lines and Queuing Models. Queuing Theory  The study of the behavior of waiting lines Importance to business There is a tradeoff between faster.
CS433 Modeling and Simulation Lecture 12 Queueing Theory Dr. Anis Koubâa 03 May 2008 Al-Imam Mohammad Ibn Saud University.
1 Queuing Systems (2). Queueing Models (Henry C. Co)2 Queuing Analysis Cost of service capacity Cost of customers waiting Cost Service capacity Total.
Supplement D Waiting Line Models
Reid & Sanders, Operations Management © Wiley 2002 Waiting Line Models A SUPPLEMENT.
Approximating the Performance of Call Centers with Queues using Loss Models Ph. Chevalier, J-Chr. Van den Schrieck Université catholique de Louvain.
CS352 - Introduction to Queuing Theory Rutgers University.
Queues. Examples of Queue Problems Post Office Post Office (Each counter with all services v Different counters for different services) T-Mobile Customer.
Chapter 1 Introduction. “Wait-in-line” is a common phenomenon in everywhere. Reason: Demand is more than service. “How long must a customer wait?” or.
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
1 Ardavan Asef-Vaziri Sep-09Operations Management: Waiting Lines3 Example: The arrival rate to a GAP store is 6 customers per hour and has Poisson distribution.
1 1 Slide Chapter 12 Waiting Line Models n The Structure of a Waiting Line System n Queuing Systems n Queuing System Input Characteristics n Queuing System.
OPERATIONS MANAGEMENT INTEGRATING MANUFACTURING AND SERVICES FIFTH EDITION Mark M. Davis Janelle Heineke Copyright ©2005, The McGraw-Hill Companies, Inc.
Simple Queueing Theory: Page 5.1 CPE Systems Modelling & Simulation Techniques Topic 5: Simple Queueing Theory  Queueing Models  Kendall notation.
QUEUING THOERY. To describe a queuing system, an input process and an output process must be specified. Examples of input and output processes are: SituationInput.
1 BIS 3106: Business Process Management (BPM) Lecture Nine: Quantitative Process Analysis (2) Makerere University School of Computing and Informatics Technology.
Queueing Theory/Waiting Line: Models and Analysis Navneet Vidyarthi
Objectives Model a Subway sandwich shop in Simio Perform basic verification analysis o Define experiment “Responses” o Learn how to interpret SMORE plots.
Questions about conditions and parameters
Chapter 1 Introduction.
Multiplying 2 Digit Factors
Al-Imam Mohammad Ibn Saud University
Application of Queueing
Demo on Queuing Concepts
Queuing Systems Don Sutton.
Supplement D Waiting Line Models
Queuing Theory By: Brian Murphy.
Mitchell Jareo MAT4340 – Operations Research Dr. Bauldry
Topic IV. Single Channel Systems (Limited queue length system)
Discrete Event “Hand” Simulation of a GI/GI/1 Queue
Queuing Analysis.
Capacity rate = 100 customers/hour Input Rate Customers/hour Time 12pm 2pm 10am Capacity rate = 100 customers/hour Inventory Waiting.
Queueing Theory 2008.
Presentation transcript:

Objectives Learn how to define Referenced Properties Use Controls to define multiple scenarios in experiment Evaluate different configurations of a Subway restaurant Perform comparisons using side-by-side SMORE plots

System Description and Data Arrival rate: 32 customers/hour Service times are all triangularly distributed with the following parameters (Minutes): Bread & Meat: (1, 1.5, 2) Oven: (0.5, 1, 1.5) Veggies: (1, 1.5, 2) Cashier: (0.5, 1, 1.5) Drink: (0.5, 1, 1.5) 70% of the customers want their sandwich toasted and 30% get cold subs The current operating policy requires that FIFO (First In First Out) must be maintained throughout the system, i.e., cold sandwiches should also wait in the queue for the oven (if there is one). Customers balk if there are 6 people in the system Bread & Meat Oven Veggies Cashier Drink Exit Enter

Performance Measures Evaluate the performance of the system during lunch rush Perform analysis to answer the following two questions:  Where to add two additional “resources” to reduce lost sales (balks)?  What is the best operating policy given the new system configuration (“Passing” or the current “No passing” policy)? Performance measures  Server utilizations  Average Time In System (TIS) for all customers  Expected Number in System (NIS)  Percentage of customers that balk  Average TIS per customer type Bread & Meat Oven Veggies Cashier Drink Exit Enter

Referenced Properties Specify the value for an object property Define experiment scenarios if set to appear as a control