Chapter 8 LP Modeling Applications with Computer Analyses

Chapter 8 LP Modeling Applications with Computer Analyses in Excel and QM for Windows To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created by Jeff Heyl © 2008 Prentice-Hall, Inc. © 2009 Prentice-Hall, Inc.

Learning Objectives After completing this chapter, students will be able to: 1. Model a wide variety of medium to large LP problems 2. Understand major application areas, including marketing, production, labor scheduling, fuel blending, transportation, and finance 3. Gain experience in solving LP problems with QM for Windows and Excel Solver software © 2009 Prentice-Hall, Inc. 8– 2

Chapter Outline 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 Introduction Marketing Applications Manufacturing Applications Employee Scheduling Applications Financial Applications Transportation Applications Transshipment Applications Ingredient Blending Applications © 2009 Prentice-Hall, Inc. 8– 3

Introduction n The graphical method of LP is useful for understanding how to formulate and solve small LP problems n There are many types of problems that can be solved using LP n The principles developed here applicable to larger problems © 2009 Prentice-Hall, Inc. 8– 4

Marketing Applications n Linear programming models have been used in the advertising field as a decision aid in selecting an effective media mix n Media selection problems can be approached with LP from two perspectives n Maximize audience exposure n Minimize advertising costs © 2009 Prentice-Hall, Inc. 8– 5

Marketing Applications n The Win Big Gambling Club promotes gambling n n n junkets to the Bahamas They have $8, 000 per week to spend on advertising Their goal is to reach the largest possible highpotential audience Media types and audience figures are shown in the following table They need to place at least five radio spots per week No more than $1, 800 can be spent on radio advertising each week © 2009 Prentice-Hall, Inc. 8– 6

Marketing Applications n Win Big Gambling Club advertising options MEDIUM AUDIENCE REACHED PER AD COST PER AD ($) MAXIMUM ADS PER WEEK TV spot (1 minute) 5, 000 800 12 Daily newspaper (fullpage ad) 8, 500 925 5 Radio spot (30 seconds, prime time) 2, 400 290 25 Radio spot (1 minute, afternoon) 2, 800 380 20 © 2009 Prentice-Hall, Inc. 8– 7

Win Big Gambling Club n The problem formulation is X 1 = number of 1 -minute TV spots each week X 2 = number of daily paper ads each week X 3 = number of 30 -second radio spots each week X 4 = number of 1 -minute radio spots each week Objective: Maximize audience coverage = 5, 000 X 1 + 8, 500 X 2 + 2, 400 X 3 + 2, 800 X 4 Subject to X 1 ≤ 12 (max TV spots/wk) X 2 ≤ 5 (max newspaper ads/wk) X 3 ≤ 25 (max 30 -sec radio spots ads/wk) X 4 ≤ 20 (max newspaper ads/wk) 800 X 1 + 925 X 2 + 290 X 3 + 380 X 4 ≤ $8, 000 (weekly advertising budget) X 3 + X 4 ≥ 5 (min radio spots contracted) 290 X 3 + 380 X 4 ≤ $1, 800 (max dollars spent on radio) X 1, X 2, X 3, X 4 ≥ 0 © 2009 Prentice-Hall, Inc. 8– 8

Win Big Gambling Club Program 8. 1 A © 2009 Prentice-Hall, Inc. 8– 9

Win Big Gambling Club n The problem solution Program 8. 1 B © 2009 Prentice-Hall, Inc. 8 – 10

Marketing Research n Linear programming has also been applied to marketing research problems and the area of consumer research n Statistical pollsters can use LP to help make strategy decisions © 2009 Prentice-Hall, Inc. 8 – 11

Marketing Research n Management Sciences Associates (MSA) is a marketing research firm n MSA determines that it must fulfill several requirements in order to draw statistically valid conclusions n Survey at least 2, 300 U. S. households n Survey at least 1, 000 households whose heads are 30 years of age or younger n Survey at least 600 households whose heads are between 31 and 50 years of age n Ensure that at least 15% of those surveyed live in a state that borders on Mexico n Ensure that no more than 20% of those surveyed who are 51 years of age or over live in a state that borders on Mexico © 2009 Prentice-Hall, Inc. 8 – 12

Marketing Research n MSA decides that all surveys should be conducted in person n It estimates the costs of reaching people in each age and region category are as follows COST PERSON SURVEYED ($) REGION AGE ≤ 30 AGE 31 -50 AGE ≥ 51 State bordering Mexico $7. 50 $6. 80 $5. 50 State not bordering Mexico $6. 90 $7. 25 $6. 10 © 2009 Prentice-Hall, Inc. 8 – 13

Marketing Research n MSA’s goal is to meet the sampling requirements at the least possible cost n The decision variables are X 1 = number of 30 or younger and in a border state X 2 = number of 31 -50 and in a border state X 3 = number 51 or older and in a border state X 4 = number 30 or younger and not in a border state X 5 = number of 31 -50 and not in a border state X 6 = number 51 or older and not in a border state © 2009 Prentice-Hall, Inc. 8 – 14

Marketing Research Objective function Minimize total = $7. 50 X 1 + $6. 80 X 2 + $5. 50 X 3 interview costs + $6. 90 X 4 + $7. 25 X 5 + $6. 10 X 6 subject to X 1 + X 2 + X 3 + X 4 + X 5 + X 6 ≥ 2, 300 (total households) X 1 + X 4 ≥ 1, 000 (households 30 or younger) X 2 + X 5 ≥ 600 (households 31 -50) X 1 + X 2 + X 3 ≥ 0. 15(X 1 + X 2+ X 3 + X 4 + X 5 + X 6) (border states) X 3 ≤ 0. 20(X 3 + X 6) (limit on age group 51+ who can live in border state) X 1 , X 2 , X 3 , X 4 , X 5 , X 6 ≥ 0 © 2009 Prentice-Hall, Inc. 8 – 15

Marketing Research n Computer solution in QM for Windows n Notice the variables in the constraints have all been moved to the left side of the equations Program 8. 2 © 2009 Prentice-Hall, Inc. 8 – 16

Marketing Research n The following table summarizes the results of the MSA analysis n It will cost MSA $15, 166 to conduct this research REGION State bordering Mexico State not bordering Mexico AGE ≤ 30 AGE 31 -50 AGE ≥ 51 0 600 140 1, 000 0 560 © 2009 Prentice-Hall, Inc. 8 – 17

Manufacturing Applications n Production Mix n LP can be used to plan the optimal mix of products to manufacture n Company must meet a myriad of constraints, ranging from financial concerns to sales demand to material contracts to union labor demands n Its primary goal is to generate the largest profit possible © 2009 Prentice-Hall, Inc. 8 – 18

Manufacturing Applications n Fifth Avenue Industries produces four varieties of ties n One is expensive all-silk n One is all-polyester n Two are polyester and cotton blends n The table on the below shows the cost and availability of the three materials used in the production process MATERIAL Silk Polyester Cotton COST PER YARD ($) 21 6 9 MATERIAL AVAILABLE PER MONTH (YARDS) 800 3, 000 1, 600 © 2009 Prentice-Hall, Inc. 8 – 19

Manufacturing Applications n The firm has contracts with several major department store chains to supply ties n Contracts require a minimum number of ties but may be increased if demand increases n Fifth Avenue’s goal is to maximize monthly profit given the following decision variables X 1 = number of all-silk ties produced per month X 2 = number polyester ties X 3 = number of blend 1 poly-cotton ties X 4 = number of blend 2 poly-cotton ties © 2009 Prentice-Hall, Inc. 8 – 20

Manufacturing Applications n Contract data for Fifth Avenue Industries VARIETY OF TIE SELLING PRICE PER TIE ($) MONTHLY CONTRACT MINIMUM MONTHLY DEMAND MATERIAL REQUIRED PER TIE (YARDS) MATERIAL REQUIREMENTS All silk 6. 70 6, 000 7, 000 0. 125 100% silk All polyester 3. 55 10, 000 14, 000 0. 08 100% polyester Poly-cotton blend 1 4. 31 13, 000 16, 000 0. 10 50% polyester 50% cotton Poly-cotton blend 2 4. 81 6, 000 8, 500 0. 10 30% polyester 70% cotton Table 8. 1 © 2009 Prentice-Hall, Inc. 8 – 21

Manufacturing Applications n Fifth Avenue also has to calculate profit per tie for the objective function VARIETY OF TIE SELLING PRICE PER TIE ($) MATERIAL REQUIRED PER TIE (YARDS) MATERIAL COST PER YARD ($) COST PER TIE ($) PROFIT PER TIE ($) All silk $6. 70 0. 125 $21 $2. 62 $4. 08 All polyester $3. 55 0. 08 $6 $0. 48 $3. 07 Poly-cotton blend 1 $4. 31 0. 05 $6 $0. 30 0. 05 $9 $0. 45 0. 03 $6 $0. 18 0. 70 $9 $0. 63 Poly-cotton blend 2 $4. 81 $3. 56 $4. 00 © 2009 Prentice-Hall, Inc. 8 – 22

Manufacturing Applications n The complete Fifth Avenue Industries model Objective function Maximize profit = $4. 08 X 1 + $3. 07 X 2 + $3. 56 X 3 + $4. 00 X 4 Subject to 0. 125 X 1 ≤ 800 (yds of silk) 0. 08 X 2 + 0. 05 X 3 + 0. 03 X 4 ≤ 3, 000 (yds of polyester) 0. 05 X 3 + 0. 07 X 4 ≤ 1, 600 (yds of cotton) X 1 ≥ 6, 000 (contract min for silk) X 1 ≤ 7, 000 (contract min) X 2 ≥ 10, 000 (contract min for all polyester) X 2 ≤ 14, 000 (contract max) X 3 ≥ 13, 000 (contract mini for blend 1) X 3 ≤ 16, 000 (contract max) X 4 ≥ 6, 000 (contract mini for blend 2) X 4 ≤ 8, 500 (contract max) X 1, X 2, X 3, X 4 ≥ 0 © 2009 Prentice-Hall, Inc. 8 – 23

Manufacturing Applications n Excel formulation for Fifth Avenue LP problem Program 8. 3 A © 2009 Prentice-Hall, Inc. 8 – 24

Manufacturing Applications n Solution for Fifth Avenue Industries LP model Program 8. 3 B © 2009 Prentice-Hall, Inc. 8 – 25

Manufacturing Applications n Production Scheduling n Setting a low-cost production schedule over a period of weeks or months is a difficult and important management task n Important factors include labor capacity, inventory and storage costs, space limitations, product demand, and labor relations n When more than one product is produced, the scheduling process can be quite complex n The problem resembles the product mix model for each time period in the future © 2009 Prentice-Hall, Inc. 8 – 26

Manufacturing Applications n Greenberg Motors, Inc. manufactures two different electric motors for sale under contract to Drexel Corp. n Drexel places orders three times a year four months at a time n Demand varies month to month as shown below n Greenberg wants to develop its production plan for the next four months MODEL JANUARY FEBRUARY MARCH APRIL GM 3 A 800 700 1, 000 1, 100 GM 3 B 1, 000 1, 200 1, 400 Table 8. 2 © 2009 Prentice-Hall, Inc. 8 – 27

Manufacturing Applications n Production planning at Greenberg must consider four factors n Desirability of producing the same number of motors each month to simplify planning and scheduling n Necessity to inventory carrying costs down n Warehouse limitations n The no-lay-off policy n LP is a useful tool for creating a minimum total cost schedule the resolves conflicts between these factors © 2009 Prentice-Hall, Inc. 8 – 28

Manufacturing Applications n Double subscripted variables are used in this problem to denote motor type and month of production XA, i = Number of model GM 3 A motors produced in month i (i = 1, 2, 3, 4 for January – April) XB, i = Number of model GM 3 B motors produced in month i n It costs $10 to produce a GM 3 A motor and $6 to produce a GM 3 B n Both costs increase by 10% on March 1, thus Cost of production = $10 XA 1 + $10 XA 2 + $11 XA 3 + 11 XA 4 + $6 XB 1 + $6 XB 2 + $6. 60 XB 3 + $6. 60 XB 4 © 2009 Prentice-Hall, Inc. 8 – 29

Manufacturing Applications n We can use the same approach to create the portion of the objective function dealing with inventory carrying costs IA, i = Level of on-hand inventory for GM 3 A motors at the end of month i (i = 1, 2, 3, 4 for January – April) IB, i = Level of on-hand inventory for GM 3 B motors at the end of month i n The carrying cost for GM 3 A motors is $0. 18 per month and the GM 3 B costs $0. 13 per month n Monthly ending inventory levels are used for the average inventory level Cost of carrying inventory = $0. 18 XA 1 + $0. 18 XA 2 + $0. 18 XA 3 + 0. 18 XA 4 + $0. 13 XB 1 + $0. 13 XB 2 + $0. 13 XB 3 + $0. 13 B 4 © 2009 Prentice-Hall, Inc. 8 – 30

Manufacturing Applications n We combine these two for the objective function Minimize total cost = $10 XA 1 + $10 XA 2 + $11 XA 3 + 11 XA 4 + $6 XB 1 + $6 XB 2 + $6. 60 XB 3 + $6. 60 XB 4 + $0. 18 XA 1 + $0. 18 XA 2 + $0. 18 XA 3 + 0. 18 XA 4 + $0. 13 XB 1 + $0. 13 XB 2 + $0. 13 XB 3 + $0. 13 XB 4 n End of month inventory is calculated using this relationship Inventory at the end of this month = Inventory at the end of last month + Current month’s production – Sales to Drexel this month © 2009 Prentice-Hall, Inc. 8 – 31

Manufacturing Applications n Greenberg is starting a new four-month production cycle with a change in design specification that left no old motors in stock on January 1 n Given January demand for both motors IA 1 = 0 + XA 1 – 800 IB 1 = 0 + XB 1 – 1, 000 n Rewritten as January’s constraints XA 1 – IA 1 = 800 XB 1 – IB 1 = 1, 000 © 2009 Prentice-Hall, Inc. 8 – 32

Manufacturing Applications n Constraints for February, March, and April XA 2 + IA 1 – IA 2 = XB 2 + IB 1 – IB 2 = XA 3 + IA 2 – IA 3 = XB 3 + IB 2 – IB 3 = XA 4 + IA 3 – IA 4 = XB 4 + IB 3 – IB 4 = 700 February GM 3 A demand 1, 200 February GM 3 B demand 1, 000 March GM 3 A demand 1, 400 March GM 3 B demand 1, 100 April GM 3 A demand 1, 400 April GM 3 B demand n And constraints for April’s ending inventory IA 4 = 450 IB 4 = 300 © 2009 Prentice-Hall, Inc. 8 – 33

Manufacturing Applications n We also need constraints for warehouse space IA 1 + IB 1 ≤ 3, 300 IA 2 + IB 2 ≤ 3, 300 IA 3 + IB 3 ≤ 3, 300 IA 4 + IB 4 ≤ 3, 300 n No worker is ever laid off so Greenberg has a base employment level of 2, 240 labor hours per month n By adding temporary workers, available labor hours can be increased to 2, 560 hours per month n Each GM 3 A motor requires 1. 3 labor hours and each GM 3 B requires 0. 9 hours © 2009 Prentice-Hall, Inc. 8 – 34

Manufacturing Applications n Labor hour constraints 1. 3 XA 1 + 0. 9 XB 1 1. 3 XA 2 + 0. 9 XB 2 1. 3 XA 3 + 0. 9 XB 3 1. 3 XA 4 + 0. 9 XB 4 All variables ≥ 2, 240 ≤ 2, 560 ≥ 0 (January min hrs/month) (January max hrs/month) (February labor min) (February labor max) (March labor min) (March labor max) (April labor min) (April labor max) Nonnegativity constraints © 2009 Prentice-Hall, Inc. 8 – 35

Manufacturing Applications n Greenberg Motors solution PRODUCTION SCHEDULE JANUARY FEBRUARY Units GM 3 A produced 1, 277 1, 138 842 792 Units GM 3 B produced 1, 000 1, 200 1, 400 1, 700 Inventory GM 3 A carried 477 915 758 450 Inventory GM 3 B carried 0 0 0 300 2, 560 2, 355 2, 560 Labor hours required MARCH APRIL n Total cost for this four month period is $76, 301. 61 n Complete model has 16 variables and 22 constraints © 2009 Prentice-Hall, Inc. 8 – 36

Employee Scheduling Applications n Assignment Problems n Involve determining the most efficient way to assign resources to tasks n Objective may be to minimize travel times or maximize assignment effectiveness n Assignment problems are unique because they have a coefficient of 0 or 1 associated with each variable in the LP constraints and the right-hand side of each constraint is always equal to 1 © 2009 Prentice-Hall, Inc. 8 – 37

Employee Scheduling Applications n Ivan and Ivan law firm maintains a large staff of n n n young attorneys Ivan wants to make lawyer-to-client assignments in the most effective manner He identifies four lawyers who could possibly be assigned new cases Each lawyer can handle one new client The lawyers have different skills and special interests The following table summarizes the lawyers estimated effectiveness on new cases © 2009 Prentice-Hall, Inc. 8 – 38

Employee Scheduling Applications n Effectiveness ratings CLIENT’S CASE LAWYER DIVORCE CORPORATE MERGER EMBEZZLEMENT EXHIBITIONISM Adams 6 2 8 5 Brooks 9 3 5 8 Carter 4 8 3 4 Darwin 6 7 6 4 1 if attorney i is assigned to case j 0 otherwise Let Xij = where i = 1, 2, 3, 4 stands for Adams, Brooks, Carter, and Darwin respectively j = 1, 2, 3, 4 stands for divorce, merger, embezzlement, and exhibitionism © 2009 Prentice-Hall, Inc. 8 – 39

Employee Scheduling Applications n The LP formulation is Maximize effectiveness = 6 X 11 + 2 X 12 + 8 X 13 + 5 X 14 + 9 X 21 + 3 X 22 + 5 X 23 + 8 X 24 + 4 X 31 + 8 X 32 + 3 X 33 + 4 X 34 + 6 X 41 + 7 X 42 + 6 X 43 + 4 X 44 subject to X 11 + X 21 + X 31 + X 41 = 1 X 12 + X 22 + X 32 + X 42 = 1 X 13 + X 23 + X 33 + X 43 = 1 X 14 + X 24 + X 34 + X 44 = 1 X 11 + X 12 + X 13 + X 14 = 1 X 21 + X 22 + X 23 + X 24 = 1 X 31 + X 32 + X 33 + X 34 = 1 X 41 + X 42 + X 43 + X 44 = 1 (divorce case) (merger) (embezzlement) (exhibitionism) (Adams) (Brook) (Carter) (Darwin) © 2009 Prentice-Hall, Inc. 8 – 40

Employee Scheduling Applications n Solving Ivan and Ivan’s assignment scheduling LP problem using QM for Windows Program 8. 4 © 2009 Prentice-Hall, Inc. 8 – 41

Employee Scheduling Applications n Labor Planning n Addresses staffing needs over a particular time n Especially useful when there is some flexibility in assigning workers that require overlapping or interchangeable talents © 2009 Prentice-Hall, Inc. 8 – 42

Employee Scheduling Applications n Hong Kong Bank of Commerce and Industry has n n requirements for between 10 and 18 tellers depending on the time of day Lunch time from noon to 2 pm is generally the busiest The bank employs 12 full-time tellers but has many part-time workers available Part-time workers must put in exactly four hours per day, can start anytime between 9 am and 1 pm, and are inexpensive Full-time workers work from 9 am to 3 pm and have 1 hour for lunch © 2009 Prentice-Hall, Inc. 8 – 43

Employee Scheduling Applications n Labor requirements for Hong Kong Bank of Commerce and Industry TIME PERIOD NUMBER OF TELLERS REQUIRED 9 am – 10 am 10 10 am – 11 am 12 11 am – Noon 14 Noon – 1 pm 16 1 pm – 2 pm 18 2 pm – 3 pm 17 3 pm – 4 pm 15 4 pm – 5 pm 10 © 2009 Prentice-Hall, Inc. 8 – 44

Employee Scheduling Applications n Part-time hours are limited to a maximum of 50% n n of the day’s total requirements Part-timers earn $8 per hour on average Full-timers earn $100 per day on average The bank wants a schedule that will minimize total personnel costs It will release one or more of its part-time tellers if it is profitable to do so © 2009 Prentice-Hall, Inc. 8 – 45

Employee Scheduling Applications n We let F P 1 P 2 P 3 P 4 P 5 = full-time tellers = part-timers starting at 9 am (leaving at 1 pm) = part-timers starting at 10 am (leaving at 2 pm) = part-timers starting at 11 am (leaving at 3 pm) = part-timers starting at noon (leaving at 4 pm) = part-timers starting at 1 pm (leaving at 5 pm) © 2009 Prentice-Hall, Inc. 8 – 46

Employee Scheduling Applications n Objective function Minimize total daily = $100 F + $32(P 1 + P 2 + P 3 + P 4 + P 5) personnel cost subject to F + P 1 0. 5 F + P 1 F F F 4 P 1 + P 2 + P 3 + P 4 + P 5 + 4 P 2 + 4 P 3 + 4 P 4 + 4 P 5 P 1 , P 2 , P 3 , P 4 , P 5 ≥ 0 ≥ 12 ≥ 14 ≥ 16 ≥ 18 ≥ 17 ≥ 15 ≥ 10 ≤ 12 ≤ 0. 50(112) (9 am – 10 am needs) (10 am – 11 am needs) (11 am – noon needs) (noon – 1 pm needs) (1 pm – 2 pm needs) (2 pm – 3 pm needs) (3 pm – 4 pm needs) (4 pm – 5 pm needs) (12 full-time tellers) (max 50% part-timers) © 2009 Prentice-Hall, Inc. 8 – 47

Employee Scheduling Applications n There are several alternate optimal schedules Hong Kong Bank can follow n F = 10, P 2 = 2, P 3 = 7, P 4 = 5, P 1, P 5 = 0 n F = 10, P 1 = 6, P 2 = 1, P 3 = 2, P 4 = 5, P 5 = 0 n The cost of either of these two policies is $1, 448 per day © 2009 Prentice-Hall, Inc. 8 – 48

Financial Applications n Portfolio Selection n Bank, investment funds, and insurance companies often have to select specific investments from a variety of alternatives n The manager’s overall objective is generally to maximize the potential return on the investment given a set of legal, policy, or risk restraints © 2009 Prentice-Hall, Inc. 8 – 49

Financial Applications n International City Trust (ICT) invests in short-term trade credits, corporate bonds, gold stocks, and construction loans n The board of directors has placed limits on how much can be invested in each area INVESTMENT Trade credit INTEREST EARNED (%) MAXIMUM INVESTMENT ($ MILLIONS) 7 1. 0 Corporate bonds 11 2. 5 Gold stocks 19 1. 5 Construction loans 15 1. 8 © 2009 Prentice-Hall, Inc. 8 – 50

Financial Applications n ICT has $5 million to invest and wants to accomplish two things n Maximize the return on investment over the next six months n Satisfy the diversification requirements set by the board n The board has also decided that at least 55% of the funds must be invested in gold stocks and construction loans and no less than 15% be invested in trade credit © 2009 Prentice-Hall, Inc. 8 – 51

Financial Applications n The variables in the model are X 1 = dollars invested in trade credit X 2 = dollars invested in corporate bonds X 3 = dollars invested in gold stocks X 4 = dollars invested in construction loans © 2009 Prentice-Hall, Inc. 8 – 52

Financial Applications n Objective function Maximize dollars of interest earned = 0. 07 X 1 + 0. 11 X 2 + 0. 19 X 3 + 0. 15 X 4 subject to ≤ X 2 ≤ X 3 ≤ X 4 ≤ X 3 + X 4 ≥ X 1 + X 2 + X 3 + X 4 ≤ X 1 , X 2 , X 3 , X 4 ≥ X 1 1, 000 2, 500, 000 1, 800, 000 0. 55(X 1 + X 2 + X 3 + X 4) 0. 15(X 1 + X 2 + X 3 + X 4) 5, 000 0 © 2009 Prentice-Hall, Inc. 8 – 53

Financial Applications n The optimal solution to the ICT is to make the following investments X 1 = $750, 000 X 2 = $950, 000 X 3 = $1, 500, 000 X 4 = $1, 800, 000 n The total interest earned with this plan is $712, 000 © 2009 Prentice-Hall, Inc. 8 – 54

Transportation Applications n Shipping Problem n The transportation or shipping problem involves determining the amount of goods or items to be transported from a number of origins to a number of destinations n The objective usually is to minimize total shipping costs or distances n This is a specific case of LP and a special algorithm has been developed to solve it © 2009 Prentice-Hall, Inc. 8 – 55

Transportation Applications n The Top Speed Bicycle Co. manufactures and markets a line of 10 -speed bicycles n The firm has final assembly plants in two cities where labor costs are low n It has three major warehouses near large markets n The sales requirements for the next year are n New York – 10, 000 bicycles n Chicago – 8, 000 bicycles n Los Angeles – 15, 000 bicycles n The factory capacities are n New Orleans – 20, 000 bicycles n Omaha – 15, 000 bicycles © 2009 Prentice-Hall, Inc. 8 – 56

Transportation Applications n The cost of shipping bicycles from the plants to the warehouses is different for each plant and warehouse TO FROM NEW YORK CHICAGO LOS ANGELES New Orleans $2 $3 $5 Omaha $3 $1 $4 n The company wants to develop a shipping schedule that will minimize its total annual cost © 2009 Prentice-Hall, Inc. 8 – 57

Transportation Applications n The double subscript variables will represent the origin factory and the destination warehouse Xij = bicycles shipped from factory i to warehouse j n So X 11 = number of bicycles shipped from New Orleans to New York X 12 = number of bicycles shipped from New Orleans to Chicago X 13 = number of bicycles shipped from New Orleans to Los Angeles X 21 = number of bicycles shipped from Omaha to New York X 22 = number of bicycles shipped from Omaha to Chicago X 23 = number of bicycles shipped from Omaha to Los Angeles © 2009 Prentice-Hall, Inc. 8 – 58

Transportation Applications n Objective function Minimize total shipping costs subject to = 2 X 11 + 3 X 12 + 5 X 13 + 3 X 21 + 1 X 22 + 4 X 23 X 11 + X 21 X 12 + X 22 X 13 + X 23 X 11 + X 12 + X 13 X 21 + X 22 + X 23 All variables = 10, 000 = 8, 000 = 15, 000 ≤ 20, 000 ≤ 15, 000 ≥ 0 (New York demand) (Chicago demand) (Los Angeles demand) (New Orleans factory supply) (Omaha factory supply) © 2009 Prentice-Hall, Inc. 8 – 59

Transportation Applications n Formulation for Excel’s Solver Program 8. 5 A © 2009 Prentice-Hall, Inc. 8 – 60

Transportation Applications n Solution from Excel’s Solver Program 8. 5 A © 2009 Prentice-Hall, Inc. 8 – 61

Transportation Applications n Top Speed Bicycle solution TO FROM New Orleans Omaha NEW YORK CHICAGO LOS ANGELES 10, 000 0 8, 000 7, 000 n Total shipping cost equals $96, 000 n Transportation problems are a special case of LP as the coefficients for every variable in the constraint equations equal 1 n This situation exists in assignment problems as well as they are a special case of the transportation problem © 2009 Prentice-Hall, Inc. 8 – 62

Transportation Applications n Truck Loading Problem n The truck loading problem involves deciding which items to load on a truck so as to maximize the value of a load shipped n Goodman Shipping has to ship the following six items ITEM VALUE ($) WEIGHT (POUNDS) 1 22, 500 7, 500 2 24, 000 7, 500 3 8, 000 3, 000 4 9, 500 3, 500 5 11, 500 4, 000 6 9, 750 3, 500 © 2009 Prentice-Hall, Inc. 8 – 63

Transportation Applications n The objective is to maximize the value of items loaded into the truck n The truck has a capacity of 10, 000 pounds n The decision variable is Xi = proportion of each item i loaded on the truck © 2009 Prentice-Hall, Inc. 8 – 64

Transportation Applications n Objective function $22, 500 X 1 + $24, 000 X 2 + $8, 000 X 3 Maximize = load value + $9, 500 X 4 + $11, 500 X 5 + $9, 750 X 6 subject to 7, 500 X 1 + 7, 500 X 2 + 3, 000 X 3 + 3, 500 X 4 + 4, 000 X 5 + 3, 500 X 6 X 1 X 2 X 3 X 4 X 5 X 6 X 1 , X 2 , X 3 , X 4 , X 5 , X 6 ≤ 10, 000 lb capacity ≤ 1 ≤ 1 ≤ 1 ≥ 0 © 2009 Prentice-Hall, Inc. 8 – 65

Transportation Applications n Excel Solver formulation for Goodman Shipping Program 8. 6 A © 2009 Prentice-Hall, Inc. 8 – 66

Transportation Applications n Solver solution for Goodman Shipping Program 8. 6 B © 2009 Prentice-Hall, Inc. 8 – 67

Transportation Applications n The Goodman Shipping problem has an n n interesting issue The solution calls for one third of Item 1 to be loaded on the truck What if Item 1 can not be divided into smaller pieces? Rounding down leaves unused capacity on the truck and results in a value of $24, 000 Rounding up is not possible since this would exceed the capacity of the truck Using integer programming, programming the solution is to load one unit of Items 3, 4, and 6 for a value of $27, 250 © 2009 Prentice-Hall, Inc. 8 – 68

Transshipment Applications n The transportation problem is a special case of the transshipment problem n When the items are being moved from a source to a destination through an intermediate point (a transshipment point), point the problem is called a transshipment problem © 2009 Prentice-Hall, Inc. 8 – 69

Transshipment Applications n Distribution Centers n Frosty Machines manufactures snowblowers in Toronto and Detroit n These are shipped to regional distribution centers in Chicago and Buffalo n From there they are shipped to supply houses in New York, Philadelphia, and St Louis n Shipping costs vary by location and destination n Snowblowers can not be shipped directly from the factories to the supply houses © 2009 Prentice-Hall, Inc. 8 – 70

Transshipment Applications n Frosty Machines network Source Transshipment Point Destination New York City Toronto Chicago Philadelphia Detroit Buffalo St Louis Figure 8. 1 © 2009 Prentice-Hall, Inc. 8 – 71

Transshipment Applications n Frosty Machines data TO CHICAGO BUFFALO NEW YORK CITY Toronto $4 $7 — — — 800 Detroit $5 $7 — — — 700 Chicago — — $6 $4 $5 — Buffalo — — $2 $3 $4 — Demand — — 450 300 FROM PHILADELPHIA ST LOUIS SUPPLY n Frosty would like to minimize the transportation costs associated with shipping snowblowers to meet the demands at the supply centers given the supplies available © 2009 Prentice-Hall, Inc. 8 – 72

Transshipment Applications n A description of the problem would be to minimize cost subject to 1. The number of units shipped from Toronto is not 2. 3. 4. 5. 6. 7. more than 800 The number of units shipped from Detroit is not more than 700 The number of units shipped to New York is 450 The number of units shipped to Philadelphia is 350 The number of units shipped to St Louis is 300 The number of units shipped out of Chicago is equal to the number of units shipped into Chicago The number of units shipped out of Buffalo is equal to the number of units shipped into Buffalo © 2009 Prentice-Hall, Inc. 8 – 73

Transshipment Applications n The decision variables should represent the number of units shipped from each source to the transshipment points and from there to the final destinations T 1 = the number of units shipped from Toronto to Chicago T 2 = the number of units shipped from Toronto to Buffalo D 1 = the number of units shipped from Detroit to Chicago D 2 = the number of units shipped from Detroit to Chicago C 1 = the number of units shipped from Chicago to New York C 2 = the number of units shipped from Chicago to Philadelphia C 3 = the number of units shipped from Chicago to St Louis B 1 = the number of units shipped from Buffalo to New York B 2 = the number of units shipped from Buffalo to Philadelphia B 3 = the number of units shipped from Buffalo to St Louis © 2009 Prentice-Hall, Inc. 8 – 74

Transshipment Applications n The linear program is Minimize cost = 4 T 1 + 7 T 2 + 5 D 1 + 7 D 2 + 6 C 1 + 4 C 2 + 5 C 3 + 2 B 1 + 3 B 2 + 4 B 3 subject to T 1 + T 2 ≤ 800 D 1 + D 2 ≤ 700 C 1 + B 1 = 450 C 2 + B 2 = 350 C 3 + B 3 = 300 T 1 + D 1 = C 1 + C 2 + C 3 T 2 + D 2 = B 1 + B 2 + B 3 T 1 , T 2 , D 1 , D 2 , C 1 , C 2 , C 3 , B 1 , B 2 , B 3 ≥ 0 (supply at Toronto) (supply at Detroit) (demand at New York) (demand at Philadelphia) (demand at St Louis) (shipping through Chicago) (shipping through Buffalo) (nonnegativity) © 2009 Prentice-Hall, Inc. 8 – 75

Transshipment Applications n The solution from QM for Windows is Program 8. 7 © 2009 Prentice-Hall, Inc. 8 – 76

Ingredient Blending Applications n Diet Problems n One of the earliest LP applications n Used to determine the most economical diet for hospital patients n Also known as the feed mix problem © 2009 Prentice-Hall, Inc. 8 – 77

Ingredient Blending Applications n The Whole Food Nutrition Center uses three bulk grains to blend a natural cereal n They advertise the cereal meets the U. S. Recommended Daily Allowance (USRDA) for four key nutrients n They want to select the blend that will meet the requirements at the minimum cost NUTRIENT USRDA Protein 3 units Riboflavin 2 units Phosphorus 1 unit Magnesium 0. 425 units © 2009 Prentice-Hall, Inc. 8 – 78

Ingredient Blending Applications n We let XA = pounds of grain A in one 2 -ounce serving of cereal XB = pounds of grain B in one 2 -ounce serving of cereal XC = pounds of grain C in one 2 -ounce serving of cereal n Whole Foods Natural Cereal requirements GRAIN COST PER POUND (CENTS) PROTEIN (UNITS/LB) RIBOFLAVIN (UNITS/LB) PHOSPHOROUS (UNITS/LB) MAGNESIUM (UNITS/LB) A 33 22 16 8 5 B 47 28 14 7 0 C 38 21 25 9 6 Table 8. 5 © 2009 Prentice-Hall, Inc. 8 – 79

Ingredient Blending Applications n The objective function is Minimize total cost of = $0. 33 XA + $0. 47 XB + $0. 38 XC mixing a 2 -ounce serving subject to 22 XA + 28 XB + 21 XC 16 XA + 14 XB + 25 XC 8 XA + 7 XB + 9 XC 5 XA + 0 XB + 6 XC XA + XB + XC XA, XB, XC ≥ ≥ ≥ = 0 3 2 1 0. 425 0. 125 (protein units) (riboflavin units) (phosphorous units) (magnesium units) (total mix) © 2009 Prentice-Hall, Inc. 8 – 80

Ingredient Blending Applications n Whole Food solution using QM for Windows Program 8. 8 © 2009 Prentice-Hall, Inc. 8 – 81

Ingredient Blending Applications n Ingredient Mix and Blending Problems n Diet and feed mix problems are special cases of a more general class of problems known as ingredient or blending problems n Blending problems arise when decisions must be made regarding the blending of two or more resources to produce one or more product n Resources may contain essential ingredients that must be blended so that a specified percentage is in the final mix © 2009 Prentice-Hall, Inc. 8 – 82

Ingredient Blending Applications n The Low Knock Oil Company produces two grades of cut-rate gasoline for industrial distribution n The two grades, regular and economy, are created by blending two different types of crude oil n The crude oil differs in cost and in its content of crucial ingredients CRUDE OIL TYPE INGREDIENT A (%) INGREDIENT B (%) COST/BARREL ($) X 100 35 55 30. 00 X 220 60 25 34. 80 © 2009 Prentice-Hall, Inc. 8 – 83

Ingredient Blending Applications n The firm lets X 1 = barrels of crude X 100 blended to produce the refined regular X 2 = barrels of crude X 100 blended to produce the refined economy X 3 = barrels of crude X 220 blended to produce the refined regular X 4 = barrels of crude X 220 blended to produce the refined economy n The objective function is Minimize cost = $30 X 1 + $30 X 2 + $34. 80 X 3 + $34. 80 X 4 © 2009 Prentice-Hall, Inc. 8 – 84

Ingredient Blending Applications n Problem formulation At least 45% of each barrel of regular must be ingredient A (X 1 + X 3) = total amount of crude blended to produce the refined regular gasoline demand Thus, 0. 45(X 1 + X 3) = amount of ingredient A required But 0. 35 X 1 + 0. 60 X 3 = amount of ingredient A in refined regular gas So 0. 35 X 1 + 0. 60 X 3 ≥ 0. 45 X 1 + 0. 45 X 3 or – 0. 10 X 1 + 0. 15 X 3 ≥ 0 (ingredient A in regular constraint) © 2009 Prentice-Hall, Inc. 8 – 85

Ingredient Blending Applications n Problem formulation Minimize cost = 30 X 1 + 30 X 2 + 34. 80 X 3 + 34. 80 X 4 subject to X 1 + X 3 ≥ 25, 000 X 2 + X 4 ≥ 32, 000 – 0. 10 X 1 + 0. 15 X 3 ≥ 0 0. 05 X 2 – 0. 25 X 4 ≤ 0 X 1 , X 2 , X 3 , X 4 ≥ 0 © 2009 Prentice-Hall, Inc. 8 – 86

Ingredient Blending Applications n Solution from QM for Windows Program 8. 9 © 2009 Prentice-Hall, Inc. 8 – 87