Due Day: Dec 8 (Monday), 2008 (9:00AM)

Slides:



Advertisements
Similar presentations
Statistical Inventory control models I
Advertisements

Session 6b. Decision Models -- Prof. Juran2 Overview Decision Analysis Uncertain Future Events Perfect Information Partial Information –The Return of.
Queuing Models.
1 IES 303 Supplement A: Decision making Week 3 Nov 24, 2005 Objective: - Amazon.com Case discussion: competitive advantage - Understand practical techniques.
6 | 1 Copyright © Cengage Learning. All rights reserved. Independent Demand Inventory Materials Management OPS 370.
Q. 9 – 3 D G A C E Start Finish B F.
Chapter 13 - Inventory Management
Chapter 9 Inventory Management.
HW #7 ANSWER
INDR 343 Problem Session
EMGT 501 HW #3 Solutions Chapter 10 - SELF TEST 7
Waiting Lines Example Students arrive at the Administrative Services Office at an average of one every 15 minutes, and their request take on average 10.
S. Chopra/Operations/Managing Services1 Operations Management: Capacity Management in Services Module u Why do queues build up? u Process attributes and.
BA 452 Lesson C.4 The Value of Information ReadingsReadings Chapter 13 Decision Analysis.
© The McGraw-Hill Companies, Inc., 2005 McGraw-Hill/Irwin 21-1 INCREMENTAL ANALYSIS Chapter 21.
Waiting Lines Example-1
Continuous Vs. Discrete: Ex. 1
EMGT 501 Mid-Term Exam Due Day: Oct. 18 (Noon).
Queuing Models Basic Concepts
Session 6b. Decision Models -- Prof. Juran2 Overview Decision Analysis Uncertain Future Events Perfect Information Partial Information –The Return of.
QBM117 Business Statistics
1.(10%) Let two independent, exponentially distributed random variables and denote the service time and the inter-arrival time of a system with parameters.
#11 QUEUEING THEORY Systems Fall 2000 Instructor: Peter M. Hahn
Chapter 13 Queuing Theory
Queueing Theory: Part I
Final Exam Due: December 14 (Noon), 2004
1 OPERATIONS MANAGEMENT for MBAs Second Edition Prepared by Scott M. Shafer Wake Forest University Meredith and Shafer John Wiley and Sons, Inc.
OM&PM/Class 6b1 1Operations Strategy 2Process Analysis 3Lean Operations 4Supply Chain Management 5Capacity Management in Services –Class 6b: Capacity Analysis.
Cost Accumulation, Tracing, and Allocation
Project 2: ATM’s & Queues
INDR 343 Problem Session
Chapter 13 - Inventory Management
© 2003 Anita Lee-Post Inventory management Part 3 By Anita Lee-Post.
1 1 Slide © 2005 Thomson/South-Western EMGT 501 HW Solutions Chapter 12 - SELF TEST 9 Chapter 12 - SELF TEST 18.
QUEUING MODELS Queuing theory is the analysis of waiting lines It can be used to: –Determine the # checkout stands to have open at a store –Determine the.
1 Operations Management Inventory Management. 2 The Functions of Inventory To have a stock of goods that will provide a “selection” for customers To take.
Chapter 12: Inventory Control Models
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 12 Inventory Management.
Spreadsheet Modeling & Decision Analysis
1 Performance Evaluation of Computer Networks: Part II Objectives r Simulation Modeling r Classification of Simulation Modeling r Discrete-Event Simulation.
1 Project Description  Business Background  Class Project.
EMGT 501 Fall 2005 Final Exam Due Day: Dec 12 (Noon)
WOOD 492 MODELLING FOR DECISION SUPPORT Lecture 27 Simulation.
Slides 6 Distribution Strategies
1 Slides used in class may be different from slides in student pack Chapter 17 Inventory Control  Inventory System Defined  Inventory Costs  Independent.
Project 2: ATM’s & Queues. ATM’s & Queues  Certain business situations require customers to wait in line for a service Examples:  Waiting to use an.
Chapter 3 Arbitrage and Financial Decision Making
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
EMGT 501 Fall 2005 Midterm Exam Due Day: Oct 17 (Noon)
Copyright 2013 John Wiley & Sons, Inc. Chapter 7: Supplement B The Economic Order Quantity.
BUAD306 Chapter 13 - Inventory Management. Everyday Inventory Food Gasoline Clean clothes… What else?
© 2008 The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin CHAPTER 4 Cost Accumulation, Tracing, and Allocation.
2008 Probability Distributions. Question One Sillicom find from their broadband customer service hotline that 15% of their broadband customers have problems.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Highly Recommended Unit One: Taking Phone Calls. Telephone Conversation May I help you? / How can I help you? And you are? Whom am I talking to? I’ll.
CDAE Class 23 Nov. 14 Last class: Result of Quiz 6 4. Queuing analysis and applications Project 3 Today: 4. Queuing analysis and applications Problem.
© 2015 McGraw-Hill Education. All rights reserved. Chapter 16 Decision Analysis.
Inventory Control. Meaning Of Inventory Control Inventory control is a system devise and adopted for controlling investment in inventory. It involve inventory.
Managerial Decision Making Chapter 13 Queuing Models.
St. Edward’s University
Distribution Strategies
Chapter 13 - Inventory Management
Marketing Fundamentals
Forecasting Methods Dr. T. T. Kachwala.
Chapter 13 - Inventory Management
The Economic Order Quantity
HW # Due Day: Nov 30.
LESSON 8: RANDOM VARIABLES EXPECTED VALUE AND VARIANCE
EMGT 501 Fall 2005 Final Exam Due Day: Dec 12 (Noon)
Project Description Business Background Class Project.
Presentation transcript:

Due Day: Dec 8 (Monday), 2008 (9:00AM) EMGT 501 Fall 2008 Final Exam Due Day: Dec 8 (Monday), 2008 (9:00AM) 1

(c) Answer on a series of PPS. Note: (a) Do not send me after copying your computer results. See my HW on my HP regarding how to prepare your answers. (b) I need your professional preparation. Large Characters at the level that I can read. (c) Answer on a series of PPS. (d) Do not discuss the exam with other students. (e) Return your answer attached to your e-mail. 2

Question 1 In the basic EOQ model, suppose the stock is replenished uniformly (rather than instantaneously) at the rate of b items per unit time until the order quantity Q is fulfilled. Withdrawals from the inventory are made at the rate of a items per unit time, where a < b. Replenishments and withdrawals of the inventory are made simultaneously. For example, if Q is 60, b is 3 per day, and a is 2 per day, then 3 units of stock arrive each day for days 1 to 20, 31 to 50, and so on, whereas units are withdrawn at the rate of 2 per day every day. The diagram of inventory level versus time is given below for this example.

Question 1 Cont’d Inventory level (20, 20) Point of maximum inventory (0, 0) M (30, 0) Time (days)

Question 1 Cont’d Find the total cost per unit time in terms of the setup cost K, production quantity Q, unit cost c, holding cost h, withdrawal rate a, and replenishment rate b. Determine the economic order quantity Q*.

Question 2 The reservation office for Central Airlines has two Agents answering incoming phone calls for flight reservations. In addition, one caller can be put on hold until one of the agents in available to take the call. If all three phone lines (both agent lines and the hold line) are busy, a potential customer gets a busy signal, in which case the call may go to another airline. The calls and attempted calls occur randomly (i.e., according to a Poisson process) at a mean rate of 15 per hour. The length of a telephone conversation has an exponential distribution with a mean of 4 minutes.

Question 2 Cont’d Construct the rate diagram for this queuing system. Find the steady-state probability that ( i ) A caller will get to talk to an agent immediately, ( ii ) The caller will be put on hold, and ( iii ) The caller will get a busy signal.

Question 3 A woman considering the purchase of a custom sound stereo system for her car looked at three different systems (A, B, and C), which varied in terms of price, sound quality, and FM radio reception. The following pair-wise comparison matrixes were developed. Compute the priorities for each pair-wise comparison matrix. Determine an overall priority for each system. Which stereo system is preferred?

Question 3 Cont’d Criterion Price Reception Sound Price Sound 1 3 4 1/3 1/4 Price A B C 1 4 2 1/4 1/3 1/2 3 Reception A B C 1 4 2 1/4 1/2 Sound A B C 1 1/2 1/4 2 1/3 4 3

Question 4 Hale’s TV Production is considering producing a pilot for a comedy series in the hope of selling it to a major television network. The network may decide to reject the series, but it may also decide to purchase the rights to the series for either one or two years. At this point in time, Hale may either produce the pilot and wait for the network’s decision or transfer the rights for the pilot and series to a competitor for $100,000. Hale’s decision alternatives and profits (in thousands of dollars) are as follows:

Question 4 Cont’d State of Nature Decision Alternative Reject, s1 1 Year, s2 2 Years, s3 Produce pilot, d1 -100 50 150 Sell to competitor, d2 100 The probabilities for the states of nature are P(s1) = 0.20, P(s2) = 0.30, and P(s3) = 0.50. For a consulting fee of $5000, an agency will review the plans for the comedy series and indicate the overall chances of a favorable network reaction to the series. Assume that the agency review will result in a favorable (F) or an unfavorable (U) review and that the following probabilities are relevant. P(F) = 0.69 P(s1│F) = 0.09 P(s1│U) = 0.45 P(U) = 0.31 P(s2│F) = 0.26 P(s2│U) = 0.39 P(s3│F) = 0.65 P(s3│U) = 0.16

Question 4 Cont’d Construct a decision tree for this problem. What is the recommended decision if the agency opinion is not used? What is the expected value? What is the expected value of perfect information? What is Hale’s optimal decision strategy assuming the agency’s information is used? What is the expected value of the agency’s information? Is the agency’s information worth the $5000 fee? What is the maximum that Hale should be willing to pay for the information? What is the recommended decision?

Number of Defective Parts Found Question 5 The supervisor of a manufacturing process believed that assembly-line speed (in feet/minute) affected the number of defective parts found during on-line inspection. To test this theory, management had the same batch of parts inspected visually at a variety of line speeds. The following data were collected. Line Speed Number of Defective Parts Found 10 20 35 30 60 40 50 85 100

Question 5 Develop the estimated regression equation that relates the line speed to the number of defective parts found. Use both Goal Programming and Least Squares Method to fit a regression line to the data set. Compare these results. Use the equations developed in part (a) to forecast the number of defective parts found for a line speed of 100 feet per minute. Predict the values based upon the two methods.

Assessment II Please indicate the current level of your knowledge. (1: no idea, 2: little, 3: considerable, 4: very well). Topic Your Assessment (1) Queuing (2) Decision Analysis (3) Multi-Criteria Decision Making (4) Forecasting (5) Markov Process Return the assessment to toshi@nmt.edu 15