Homework #3 Supply Chain Models: Manufacturing & Warehousing (ISyE 3104) - Fall 2001 Due September 20, 2001

Similar documents
Sensitivity Analysis LINDO INPUT & RESULTS. Maximize 7X1 + 10X2. Subject to X1 < 500 X2 < 500 X1 + 2X2 < 960 5X1 + 6X2 < 3600 END

y 3 z x 1 x 2 e 1 a 1 a 2 RHS 1 0 (6 M)/3 M 0 (3 5M)/3 10M/ / /3 10/ / /3 4/3

Midterm 2 Example Questions

Linear Programming: Sensitivity Analysis and Interpretation of Solution

56:171 Operations Research Midterm Examination October 25, 1991 PART ONE

Operations Research I: Deterministic Models

56:171 Operations Research Midterm Exam Solutions October 19, 1994

LP OPTIMUM FOUND AT STEP 2 OBJECTIVE FUNCTION VALUE

56:171 Operations Research Midterm Exam Solutions Fall 1994

IE312 Optimization: Homework #5 Solution Fall Due on Oct. 29, 2010

Optimizing the service of the Orange Line

Operations Research I: Deterministic Models

THE UNIVERSITY OF BRITISH COLUMBIA

56:171 Operations Research Midterm Examination Solutions PART ONE

Do all of Part One (1 pt. each), one from Part Two (15 pts.), and four from Part Three (15 pts. each) <><><><><> PART ONE <><><><><>

56:171 Operations Research Midterm Examination October 28, 1997 PART ONE

56:171 Operations Research Midterm Examination Solutions PART ONE

February 24, 2005

b) [3 marks] Give one more optimal solution (different from the one computed in a). 2. [10 marks] Consider the following linear program:

Dennis L. Bricker Dept. of Industrial Engineering The University of Iowa

Homework. Part 1. Computer Implementation: Solve Wilson problem by the Lindo and compare the results with your graphical solution.

Homework solutions, Chapter 8

Operations Management. Aggregate Planning

56:171 Operations Research Midterm Exam Solutions October 22, 1993

CHAPTER 13: A PROFIT MAXIMIZING HARVEST SCHEDULING MODEL

INTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations

Chapter Two: Linear Programming: Model Formulation and Graphical Solution

Week 6: Sensitive Analysis

Game Theory Tutorial 3 Answers

Econ 172A - Slides from Lecture 7

Lecture 3. Understanding the optimizer sensitivity report 4 Shadow (or dual) prices 4 Right hand side ranges 4 Objective coefficient ranges

Chapter Two: Linear Programming: Model Formulation and Graphical Solution

3.3 - One More Example...

DUALITY AND SENSITIVITY ANALYSIS

Optimization Methods. Lecture 7: Sensitivity Analysis

PAPER 5 : COST MANAGEMENT Answer all questions.

Advanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras

Examinations for Semester II. / 2011 Semester I

COMM 290 MIDTERM REVIEW SESSION ANSWER KEY BY TONY CHEN

The homework is due on Wednesday, September 7. Each questions is worth 0.8 points. No partial credits.

INEN 420 Final Project. Rhoda Daniel Javier

Duality & The Dual Simplex Method & Sensitivity Analysis for Linear Programming. Metodos Cuantitativos M. En C. Eduardo Bustos Farias 1

Master of Business Administration - General. Cohort: MBAG/14/PT Mar. Examinations for Semester II / 2014 Semester I

AM 121: Intro to Optimization Models and Methods Fall 2017

Homework 1 Solutions

OPTIMIZAÇÃO E DECISÃO 10/11

Linear Programming Formulations

Optimization Methods in Management Science

LP Sensitivity Analysis

Chapter Two: Linear Programming: Model Formulation and Graphical Solution

Graphical Sensitivity Analysis

Solution to P2 Sensitivity at the Major Electric Company

ISyE 6201: Manufacturing Systems Instructor: Spyros Reveliotis Spring 2007 Solutions to Homework 1

Optimum Allocation of Resources in University Management through Goal Programming

PERT 12 Quantitative Tools (1)

Empire State Manufacturing Survey

AP 9.2 Notes WEB.notebook February 04, Bellwork

THANK YOU TO OUR SPONSORS

4. Introduction to Prescriptive Analytics. BIA 674 Supply Chain Analytics

{List Sales (1 Trade Discount) Total Cost} (1 Tax Rate) = 0.06K

Policy Brief. Canada s Labour Market Puts in a Strong Performance in The Canadian Chamber is committed to fostering.

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research

32 Chapter 3 Analyzing Solutions. The solution is:

56:171 Operations Research Homework #8 Solution -- Fall Estimated resale price A: Private $ $600 B: Dealer $

DM559/DM545 Linear and integer programming

Lesson Topics. B.3 Integer Programming Review Questions

Econ 172A, W2002: Final Examination, Solutions

COVENANT TRANSPORTATION GROUP ANNOUNCES FOURTH QUARTER FINANCIAL AND OPERATING RESULTS

Grain Operations Webinar Series

Allocate and Level Project Resources

The application of linear programming to management accounting

ANSWERS To next 16 Multiple Choice Questions below B B B B A E B E C C C E C C D B

Introduction to Operations Research

EXPENDITURE APPROACH: The expenditures on all final goods and services made by all sectors of the economy are added to calculate GDP. Expenditures are

W HIGHLIGHTS - EXECUTIVE SUMMARY

36106 Managerial Decision Modeling Sensitivity Analysis

AAT (Diploma in Accounting) Level 4. Budgeting

Product Mix Problem: Fifth Avenue Industries. Linear Programming (LP) Can Be Used for Many Managerial Decisions:

Elastic demand solution methods

1 Shapley-Shubik Model

By: Stephen Fitzroy (presenting), Brian Alstadt, and Derek Cutler. Economic Development Research Group, Inc.

An Introduction to Linear Programming (LP)

$1.00: What factors influenced the quantity you were willing to supply? Graph this like you would with demand. $0/hr

Exercise 1. Jan Abrell Centre for Energy Policy and Economics (CEPE) D-MTEC, ETH Zurich. Exercise

Quantitative Analysis for Management Linear Programming Models:

A simulation study of two combinatorial auctions

NATIONAL ECONOMIC OUTLOOK

Werner Enterprises Reports Improved First Quarter 2015 Revenues and Earnings

Macroeconomics, Spring 2007, Final Exam, several versions, Early May

Baseline U.S. Economic Outlook, Summary Table*

Werner Enterprises Reports Improved Fourth Quarter and Annual 2017 Revenues and Earnings

WAYNE STATE UNIVERSITY Department of Industrial and Manufacturing Engineering

THIRD QUARTER THIRD QUARTER REPORT REPORT

Note on Using Excel to Compute Optimal Risky Portfolios. Candie Chang, Hong Kong University of Science and Technology

TUFTS UNIVERSITY DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING ES 152 ENGINEERING SYSTEMS Spring Lesson 16 Introduction to Game Theory

Econ / Summer 2005

INTERMEDIATE MACROECONOMICS

FRAMINGHAM STATE COLLEGE PRINCIPLES OF MICROECONOMICS PROBLEM SET NUMBER 2

Another Strong Jobs Report, But Economy Remains Weak

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to:

Transcription:

Homework #3 Supply Chain Models: Manufacturing & Warehousing (ISyE 3104) - Fall 2001 Due September 20, 2001 Show all your steps to get full credit. (Total 45 points) Reading assignment: Read Supplement 1, Chapter 3 and Chapter 4. The Yeasty Brewing Company produces a popular local beer known as Iron Stomach. Beer sales are somewhat seasonal, and Yeasty is planning its production and manpower levels on March 31 for the next six months. The demand forecasts are: Month Production Days Forecasted Demand April 11 8,500 May 22 9,300 June 20 12,200 July 23 17,600 August 16 14,000 September 20 6,300 As of March 31, Yeasty had 86 workers on the payroll. Over a period of 26 working days, when there were 100 workers on the payroll, Yeasty produced 12,000 cases of beer. The cost to hire each worker is $125 and the cost of laying off each worker is $300. The regulation imposes that the number of hired or laid off workers should not exceed 10 per month. Regular worker cost is $10/hr/worker (assume 8-hour days) whereas overtime worker cost is $20/hr/worker. The overtime working is a 4-hr shift after a regular working day. (Overtime workers are also hired on a monthly basis.) Subcontracting is available at a cost of $25 per case. As of March 31, Yeasty expects to have 4,500 cases of beer in stock, and it wants to maintain a minimum buffer inventory of 1,000 cases each month. It plans to start October with 3,000 cases on hand. Attached are an LP formulation and the corresponding Lindo output for this problem. a) (7 points) Explain how this problem can be modeled by the following LP. Associate appropriate definitions for the variables and explain the constraints.

1) Min 125H1 + 125H2+ 125H3+125H4 + 125H5 + 125H6 + 300F1 + 300F2 + 300F3+ 300F4 + 300F5 +300F6 + 0.75I1 + 0.75I2 + 0.75I3 + 0.75I4 + 0.75I5 + 0.75I6 + 880W1 + 1760W2 + 1600W3 + 1840W4 + 1280W5 + 1600W6 + 880O1 + 1760O2 + 1600O3 + 1840O4 + 1280O5 + 1600O6 + 25S1 + 25S2 + 25S3 + 25S4 + 25S5 + 25S6 Subject to 2) W0 + H1 - F1 - W1 =0 3) W1 + H2 - F2 - W2 =0 4) W2 + H3 - F3 - W3 =0 5) W3 + H4 - F4 - W4 =0 6) W4 + H5 - F5 - W5 =0 7) W5 + H6 - F6 - W6 =0 8) H1 <= 10 9) H2 <= 10 10) H3 <= 10 11) H4 <= 10 12) H5 <= 10 13) H6 <= 10 14) F1 <= 10 15) F2 <= 10 16) F3 <= 10 17) F4 <= 10 18) F5 <= 10 19) F6 <= 10 20) 50.7W1 + 25.35O1 - U1 - P1 = 0 21) 101.4W2 + 50.70O2 - U2 - P2 = 0 22) 92.2W3 + 46.10O3 - U3 - P3 = 0 23) 106.0W4 + 53.00O4 - U4 - P4 = 0 24) 73.7W5 + 36.88O5 - U5 - P5 = 0 25) 92.2W6 + 46.10O6 - U6 - P6 = 0 26) I0 + P1 + S1 - I1 = 8500 27) I1 + P2 + S2 - I2 = 9300 28) I2 + P3 + S3 - I3 = 12100 29) I3 + P4 + S4 - I4 = 17600 30) I4 + P5 + S5 - I5 = 14000 31) I5 + P6 + S6 - I6 = 6300 32) I0 = 4500 33) I1 >= 1000 34) I2 >= 1000 35) I3 >= 1000 36) I4 >= 1000 37) I5 >= 1000 38) I6 >= 3000 39) W0 = 86 40) O1 - W1 <= 0 41) O2 - W2 <= 0 42) O3 - W3 <= 0

43) O4 - W4 <= 0 44) O5 - W5 <= 0 45) O6 - W6 <= 0 Answer the following questions using sensitivity analysis. Remember that the solution to the problem has two components: objective function value and the values of the variables. b) (3 points) Rather than keeping 1000 units in inventory at the end of July, the firm is considering to keep only 500 units. Would this change improve the objective function or make it worse? How much would the objective function change per unit decrease in the buffer inventory? c) (2 points) If the demand in April increased from 8500 to 9000, what would be the new objective function value? d) (2 points) Suppose the demand in April decreases to 8000. By looking at the sensitivity output from Lindo, can you tell how the solution would change? e) (3 points) If the number of available workers to hire increased from 10 to 15 in May, would Yeasty be interested in hiring any additional workers (in addition to the 10 workers they will hire according to the current solution)? If yes, how much would they be willing to pay each additional worker? f) (2 points) If the cost of hiring a worker in June increased from $125 to $250 due to a shortage in the labor market, how would this change impact the current solution? g) (2 points) If the cost of subcontracting in April increased from $25 to $30, how would this change impact the current solution? h) (3 points) If the cost of subcontracting reduced from $25 to $24 in May, would Yeasty be interested in subcontracting part of the production in May? What if the cost of subcontracting reduces to $20 in May?

LP OPTIMUM FOUND AT STEP 37 OBJECTIVE FUNCTION VALUE 1) 1265754. VARIABLE VALUE REDUCED COST H1 10.000000.000000 H2 10.000000.000000 H3 10.000000.000000 H4.000000 352.500100 H5.000000 1162.500000 H6.000000 1725.000000 F1.000000 1718.750000 F2.000000 1331.250000 F3.000000 708.349900 F4.000000 72.499920 F5 10.000000.000000 F6 10.000000.000000 I1 1000.000000.000000 I2 2448.400000.000000 I3 1043.600000.000000 I4 1000.000000.000000 I5 1000.000000.000000 I6 3000.000000.000000 W1 96.000000.000000 W2 106.000000.000000 W3 116.000000.000000 W4 116.000000.000000 W5 106.000000.000000 W6 96.000000.000000 O1.000000 246.250000 O2.000000 568.550000 O3.000000 482.075000 O4.000000 515.000000 O5.000000 358.000000 O6.000000 1600.000000 S1 132.799900.000000 S2.000000 1.500000 S3.000000.750000 S4 5260.400000.000000 S5 6187.800000.000000 S6.000000 25.000000 W0 86.000000.000000 U1.000000 25.000000 P1 4867.200000.000000

U2.000000 23.500000 P2 10748.400000.000000 U3.000000 24.250000 P3 10695.200000.000000 U4.000000 25.000000 P4 12296.000000.000000 U5.000000 25.000000 P5 7812.200000.000000 U6 551.199700.000000 P6 8300.000000.000000 I0 4500.000000.000000 ROW SLACK OR SURPLUS DUAL PRICES 2).000000-1418.750000 3).000000-1031.250000 4).000000-408.349900 5).000000 227.500100 6).000000 1037.500000 7).000000 1600.000000 8).000000 1293.750000 9).000000 906.249900 10).000000 283.349900 11) 10.000000.000000 12) 10.000000.000000 13) 10.000000.000000 14) 10.000000.000000 15) 10.000000.000000 16) 10.000000.000000 17) 10.000000.000000 18).000000 737.500100 19).000000 1300.000000 20).000000-25.000000 21).000000-23.500000 22).000000-24.250000 23).000000-25.000000 24).000000-25.000000 25).000000.000000 26).000000-25.000000 27).000000-23.500000 28).000000-24.250000 29).000000-25.000000 30).000000-25.000000 31).000000.000000 32).000000 25.000000 33).000000-2.250000

34) 1448.400000.000000 35) 43.599810.000000 36).000000 -.750000 37).000000-25.750000 38).000000 -.750000 39).000000 1418.750000 40) 96.000000.000000 41) 106.000000.000000 42) 116.000000.000000 43) 116.000000.000000 44) 106.000000.000000 45) 96.000000.000000 NO. ITERATIONS= 37 RANGES IN WHICH THE BASIS IS UNCHANGED: OBJ COEFFICIENT RANGES VARIABLE CURRENT ALLOWABLE ALLOWABLE COEF INCREASE DECREASE H1 125.000000 1293.750000 INFINITY H2 125.000000 906.249900 INFINITY H3 125.000000 283.349900 INFINITY H4 125.000000 INFINITY 352.500100 H5 125.000000 INFINITY 1162.500000 H6 125.000000 INFINITY 1725.000000 F1 300.000000 INFINITY 1718.750000 F2 300.000000 INFINITY 1331.250000 F3 300.000000 INFINITY 708.349900 F4 300.000000 INFINITY 72.499920 F5 300.000000 737.500100 INFINITY F6 300.000000 1300.000000 INFINITY I1.750000 INFINITY 2.250000 I2.750000 8.937375 1.500000 I3.750000 3.073209.750000 I4.750000 INFINITY.750000 I5.750000 INFINITY 25.750000 I6.750000 INFINITY.750000 W1 880.000000 1293.750000 INFINITY W2 1760.000000 906.249900 INFINITY W3 1600.000000 283.349900 INFINITY W4 1840.000000 72.499920 352.500100 W5 1280.000000 72.499920 352.500100 W6 1600.000000 72.499920 352.500100 O1 880.000000 INFINITY 246.250000 O2 1760.000000 INFINITY 568.550000

O3 1600.000000 INFINITY 482.075000 O4 1840.000000 INFINITY 515.000000 O5 1280.000000 INFINITY 358.000000 O6 1600.000000 INFINITY 1600.000000 S1 25.000000 9.714004 2.250000 S2 25.000000 INFINITY 1.500000 S3 25.000000 INFINITY.750000 S4 25.000000.750000.683962 S5 25.000000.750000.983717 S6 25.000000 INFINITY 25.000000 W0.000000 INFINITY INFINITY U1.000000 INFINITY 25.000000 P1.000000 25.000000 9.714004 U2.000000 INFINITY 23.500000 P2.000000 8.937375 11.214000 U3.000000 INFINITY 24.250000 P3.000000 3.073209 10.457160 U4.000000 INFINITY 25.000000 P4.000000.683962 3.325472 U5.000000 INFINITY 25.000000 P5.000000.983717 4.782905 U6.000000.750000 3.823211 P6.000000 25.000000.750000 I0.000000 INFINITY INFINITY RIGHTHAND SIDE RANGES ROW CURRENT ALLOWABLE ALLOWABLE RHS INCREASE DECREASE 2.000000.225206 2.619328 3.000000.225206 17.558080 4.000000.472883 26.540870 5.000000 5.978305 49.626420 6.000000 5.978305 83.959300 7.000000 5.978305 INFINITY 8 10.000000 2.619328.225206 9 10.000000 17.558080.225206 10 10.000000 26.540870.472883 11 10.000000 INFINITY 10.000000 12 10.000000 INFINITY 10.000000 13 10.000000 INFINITY 10.000000 14 10.000000 INFINITY 10.000000 15 10.000000 INFINITY 10.000000 16 10.000000 INFINITY 10.000000 17 10.000000 INFINITY 10.000000 18 10.000000 5.978305 10.000000 19 10.000000 5.978305 10.000000

20.000000 4867.200000 132.799900 21.000000 43.599810 5260.400000 22.000000 43.599810 5260.400000 23.000000 12296.000000 5260.400000 24.000000 7812.200000 6187.800000 25.000000 551.199700 INFINITY 26 8500.000000 INFINITY 132.799900 27 9300.000000 43.599810 5260.400000 28 12100.000000 43.599810 5260.400000 29 17600.000000 INFINITY 5260.400000 30 14000.000000 INFINITY 6187.800000 31 6300.000000 551.199700 8300.000000 32 4500.000000 132.799900 4500.000000 33 1000.000000 5260.400000 43.599810 34 1000.000000 1448.400000 INFINITY 35 1000.000000 43.599810 INFINITY 36 1000.000000 6187.800000 1000.000000 37 1000.000000 8300.000000 551.199700 38 3000.000000 551.199700 3000.000000 39 86.000000 2.619328.225206 40.000000 INFINITY 96.000000 41.000000 INFINITY 106.000000 42.000000 INFINITY 116.000000 43.000000 INFINITY 116.000000 44.000000 INFINITY 106.000000 45.000000 INFINITY 96.000000

2. (5 points) What is the opportunity cost of inventory? 3. (6 points) What are the major functions of inventory? 4. HAL Ltd. produces a line of high-capacity disk drives for mainframe computers. The housings for the drives are produces in Hamilton, Ontario, and shipped to the main plant in Toronto. HAL uses the drive housings at a fairly steady rate of 720 per year. Suppose that the housings are shipped in trucks that can hold 40 housings at one time. It is estimated that the fixed cost of loading the housings onto the truck and unloading them on the other end is $300 for shipments of 120 or fewer housings (i.e., three or fewer truckloads). Each trip made by a single truck costs the company $160 in driver time, gasoline, oil, insurance and wear and tear on the truck. a) (9 points) Compute the annual costs of transportation and loading and unloading the housings for the following policies: (1) shipping one truck per week, (2) shipping one full truckload as often as needed, and (3) shipping three full truckloads as often as needed. b) (3 points) For what reasons might the policy in (a) with the highest annual cost be more desirable from a systems point of view than the policy having the lowest annual cost?