J)
Richard lvey School of Business 1 I I E
The University of Westem Ontario
"910D05
OPERATIONS STRATEGY AT GALANZ
Dr. Stephen Ng and Barbara U wrote this wse under the supervision of Professors Xiande Zhao, Xuejun Xu and Yang Lei solely to
provide mat
Practice Problems Solutions B3. LP Sensitivity Analysis
Problem B3.1
Explain the 100% rule and its role in analyzing the impact of simultaneous changes in model input data
values.
Problem B3.2
How do we detect the presence of alternate optimal solutions f
Practice Problems Solutions C.Queuing Models
Question C.1
The underlying assumptions are:
1. Arrivals are FIFO.
2. There is no balking or reneging.
3. Arrivals are independent.
4. Arrivals are Poisson.
5. Service times are independent.
6. Average service
3QA3: MS for Business
Midterm Exam Computer
October 2013
Instructions
1. You have one hour to complete three questions. At the end of one hour TAs will start to grade your Excel worksheets.
So use your time carefully.
Use a different Excel file for each q
Practice Problems Questions B2. Standard LP Problems
Problem B2.1 - 3
A manufacturer of travel pillows must determine the production plan for the next production cycle. She
wishes to make at least 300 of each of the three models that her firm offers and n
3QA3: MS for Business
Practice Midterm Exam
October 2013
All answers, calculations, and other work in this Exam must be written with an INK PEN.
1-4. Consider the following flow chart and probabilities distributions for a service system at a bank.
custome
Practice Problems Questions B1. Formulate and Solve LP problems
Problem B1.1
Under what condition is it possible for an LP problem to have more than one optimal solution?
Problem B1.2
Under what condition is it possible for an LP problem to have an unboun
Problem A-1
A news vendor sells magazines at a busy subway stations. Weekly demand for one popular magazine is
distributed as shown in the following table:
Demand
50
75
100
125
150
175
Probability
0.05
0.10
0.25
0.30
0.20
0.10
The vendor normally orders 1
Sensitivity Analysis is an analysis of how changes in the LP problems (constraints or coefficient) affect the solution
Shadow price Marginal Objective Function by changing 1unit RHS (units objective/constraint)Shadow price = 0 when non-binding, Shadow pri
100% Rule for changing constraints RHSs: All< information in the Answer and Sensitivity Report is
validated if
Expected Monetary Value (EMV) = Sum of (Payoffs x Prob.)<- Pick Highest
Expected Value with Perfect Info (EVwPI) = Sum of (Best Payoff x Prob.)
Section A: Simulation
Section A.1: Introduction
A simulation is a mathematical model of a real-world process or system.
The idea is to imitate the real-world system (e.g. revenue management at a trucking company)
mathematically in order to study its chara
i. Input data
-these data follow directly from the problem
Bus's passenger capacity =
Minimum reservation request =
Maximum reservation request =
Probability person shows up
Reservation deposit amount =
Balance of ticket price =
Walk-up ticket price =
Cos
Example A.4 - parts i and ii
i. Input data
-these data follow directly from the problem
Bus's passenger capacity =
Minimum reservation request =
Maximum reservation request =
Probability person shows up
Reservation deposit amount =
Balance of ticket price
Shadow price Marginal Objective Function by changing 1unit RHS (units objective/constraint) Shadow price = 0 when non-binding, Shadow price > 0 when binding.
Shadow price for non-negativity constraints = Reduced costs (how much your objective function cha
Folding back the tree:
The tree in Fig- 8- 1 above is equivaleilt to the following tree,
Payoffs
Hi h Demand 0. so $200 000
outcome Noah: 386,0) '
N 1 Moderate Demand 0-50 $1 00-000
"- Low Dem d 0.2-0
marsh," Noe-e qsfis " 451 20.000
'1 \f ngh
Sensitivity Analysis - gives ranges of values of the objective
function coefficients and the constraint RHS over which the
optimal solution does not change.
Sensitivity analysis for constraints
1. Binding or nonbinding, slack or surplus for a constraint
2
Objective Cell (Max)
Cell
Name
$D$6 (0) Total Profit Margin
Final Value
$4,040
Variable Cells
Cell
$B$5
$C$5
Final Value
320
360
Name
T
C
Constraints
Cell
Name
$D$8
(1) carpentry hours
$D$9
(2) painting hours
$D$10
(3) max. tables
$D$11
(4) max. chairs
LH
Example 1. Furniture Company
1000
900
800
Constraint
(1) 3T + 4C <= 2400
(2) 2T + C <= 1000
(3) T >= 100
(4) C <= 450
(5) T >= 0
(6) C >= 0
Objective function
(0) P = 7T + 5C
Value
T=0, C=?
600
1000
450
all
all
0
T=?, C=0
800
500
all
100
0
all
700
600
500
PRACTICE PROBLEMS
Commerce 3QA3: MS for Business Exam 1 - Problem
February 2016
Name & Student Number:
Answers are at the end of this Practice Exam
Professor Miltenburg
Version: _1_
Instructions:
1. This exam is ? pages long and has 46? multiple-choice qu
Chapter 1
LP Steps:
1. Formulation: translated into mathematical expressions
2. Solution: identify optimal solution; either the graphical or simplex model is
used
3. Interpretation: sensitivity analysis & Answer Report
Known Parameters (input variables) =
3QA3: MS for Business
SAMPLE
Exam 2 Computer
Last name: _SOLUTIONS_ First name: _
April 2016
Student number: _
Security code: _A11_
Circle room where exam was written: KTH-B121, KTH-B123, BSB-241, BSB-244, BSB-249, JHE-234, JHE-233a
Instructions:
1. You m
Supplier Self Service (SUS)
Creating SUS Invoices & Credit Memos (Membuat Invois SUS & Memo Kredit)
CREATING SUS INVOICES & CREDIT MEMOS
SUPPLIER SELF SERVICE (SUS)
USER GUIDE
MEMBUAT INVOIS SUS & MEMO KREDIT
PANDUAN PENGGUNA
Table of Contents (Isi Kandun
Industry 4.0 Is Transforming Supply Chains
What it Means for Your Business and How To Become Industry 4.0-Ready
HOW INDUSTRY 4.0 IS TRANSFORMING SUPPLY CHAINS
Introduction: What is Industry 4.0?
Its a hot term these days: Industry 4.0. But what
exactly is