# Chapter 3.

## Presentation on theme: "Chapter 3."— Presentation transcript:

Chapter 3

Goal: Design the promotional campaign for Crunchy Start. The three most effective advertising media for this product are Television commercials on Saturday morning programs for children. Advertisements in food and family-oriented magazines. Advertisements in Sunday supplements of major newspapers. The limited resources in the problem are Advertising budget (\$4 million). Planning budget (\$1 million). TV commercial spots available (5). The objective will be measured in terms of the expected number of exposures. Question: At what level should they advertise Crunchy Start in each of the three media?

Cost and Exposure Data Costs Cost Category Each TV Commercial
Each Magazine Ad Each Sunday Ad Ad Budget \$300,000 \$150,000 \$100,000 Planning budget 90,000 30,000 40,000 Expected number of exposures 1,300,000 600,000 500,000 Table 3.1 Cost and exposure data for the Super Grain advertising-mix problem

Figure 3.1 The spreadsheet model for the Super Grain problem (Section 3.1), including the target cell Total Exposures (H13), the changing cells Number of Ads (C13:E13), and the optimal solution obtained by Solver.

Algebraic Formulation
Let TV = Number of commercials for separate spots on television M = Number of advertisements in magazines. SS = Number of advertisements in Sunday supplements. Maximize Exposure = 1,300TV + 600M + 500SS subject to Ad Spending: 300TV + 150M + 100SS ≤ 4,000 (\$thousand) Planning Cost: 90TV + 30M + 40SS ≤ 1,000 (\$thousand) Number of TV Spots: TV ≤ 5 and TV ≥ 0, M ≥ 0, SS ≥ 0.

The TBA Airlines Problem
TBA Airlines is a small regional company that specializes in short flights in small airplanes. The company has been doing well and has decided to expand its operations. The basic issue facing management is whether to purchase more small airplanes to add some new short flights, or start moving into the national market by purchasing some large airplanes, or both. Question: How many airplanes of each type should be purchased to maximize their total net annual profit?

Data for the TBA Airlines Problem
Small Airplane Large Airplane Capital Available Net annual profit per airplane \$1 million \$5 million Purchase cost per airplane 5 million 50 million \$100 million Maximum purchase quantity 2 Table 3.2 Data for the TBA Airlines problem.

Violates Divisibility Assumption of LP
Divisibility Assumption of Linear Programming: Decision variables in a linear programming model are allowed to have any values, including fractional values, that satisfy the functional and nonnegativity constraints. Thus, these variables are not restricted to just integer values. Since the number of airplanes purchased by TBA must have an integer value, the divisibility assumption is violated.

Spreadsheet Model Figure 3.2 A spreadsheet model for the TBA Airlines integer programming problem where the changing cells, Units Produced (C12:D12), show the optimal airplane purchases obtained by the Solver and the target cell, Total Profit (G12), gives the resulting total profit in millions of dollars.

Integer Programming Formulation
Let S = Number of small airplanes to purchase L = Number of large airplanes to purchase Maximize Profit = S + 5L (\$millions) subject to Capital Available: 5S + 50L ≤ 100 (\$millions) Max Small Planes: S ≤ 2 and S ≥ 0, L ≥ 0 S, L are integers.

Think-Big Capital Budgeting Problem
Think-Big Development Co. is a major investor in commercial real-estate development projects. They are considering three large construction projects Construct a high-rise office building. Construct a hotel. Construct a shopping center. Each project requires each partner to make four investments: a down payment now, and additional capital after one, two, and three years. Question: At what fraction should Think-Big invest in each of the three projects?

Financial Data for the Projects
Investment Capital Requirements Year Office Building Hotel Shopping Center \$40 million \$80 million \$90 million 1 60 million 80 million 50 million 2 90 million 20 million 3 10 million 70 million Net present value \$45 million \$70 million \$50 million Table 3.3 Financial data for the projects being considered for partial investment by the Think-Big Development Co.

Figure 3.3 The spreadsheet model for the Think-Big problem (Section 3.2), including the target cell Total NPV (H16), the changing cells Participation Share (C16:E16), and the optimal solution obtained by Solver.

Algebraic Formulation
Let OB = Participation share in the office building, H = Participation share in the hotel, SC = Participation share in the shopping center. Maximize NPV = 45OB + 70H + 50SC subject to Total invested now: 40OB + 80H + 90SC ≤ 25 (\$million) Total invested within 1 year: 100OB + 160H + 140SC ≤ 45 (\$million) Total invested within 2 years: 190OB + 240H + 160SC ≤ 65 (\$million) Total invested within 3 years: 200OB + 310H + 220SC ≤ 80 (\$million) and OB ≥ 0, H ≥ 0, SC ≥ 0.

Template for Resource-Allocation Problems
Figure 3.4 A template of a spreadsheet model for pure resource-allocation problems.

Summary of Formulation Procedure for Resource-Allocation Problems
Identify the activities for the problem at hand. Identify an appropriate overall measure of performance (commonly profit). For each activity, estimate the contribution per unit of the activity to the overall measure of performance. Identify the resources that must be allocated. For each resource, identify the amount available and then the amount used per unit of each activity. Enter the data in steps 3 and 5 into data cells. Designate changing cells for displaying the decisions. In the row for each resource, use SUMPRODUCT to calculate the total amount used. Enter <= and the amount available in two adjacent cells. Designate a target cell. Use SUMPRODUCT to calculate this measure of performance.

Union Airways Personnel Scheduling
Union Airways is adding more flights to and from its hub airport and so needs to hire additional customer service agents. The five authorized eight-hour shifts are Shift 1: 6:00 AM to 2:00 PM Shift 2: 8:00 AM to 4:00 PM Shift 3: Noon to 8:00 PM Shift 4: 4:00 PM to midnight Shift 5: 10:00 PM to 6:00 AM Question: How many agents should be assigned to each shift?

Time Periods Covered by Shift Minimum Number of Agents Needed
Schedule Data Time Periods Covered by Shift Time Period 1 2 3 4 5 Minimum Number of Agents Needed 6 AM to 8 AM 48 8 AM to 10 AM 79 10 AM to noon 65 Noon to 2 PM 87 2 PM to 4 PM 64 4 PM to 6 PM 73 6 PM to 8 PM 82 8 PM to 10 PM 43 10 PM to midnight 52 Midnight to 6 AM 15 Daily cost per agent \$170 \$160 \$175 \$180 \$195 Table 3.5 Data for the Union Airways personnel scheduling problem

Figure 3.5 The spreadsheet model for the Union Airways problem, including the target cell Total Cost (J21), the changing cells Number Working (C21:G21), and the optimal solution as obtained by the Solver.

Algebraic Formulation
Let Si = Number working shift i (for i = 1 to 5), Minimize Cost = \$170S1 + \$160S2 + \$175S3 + \$180S4 + \$195S5 subject to Total agents 6AM–8AM: S1 ≥ 48 Total agents 8AM–10AM: S1 + S2 ≥ 79 Total agents 10AM–12PM: S1 + S2 ≥ 65 Total agents 12PM–2PM: S1 + S2 + S3 ≥ 87 Total agents 2PM–4PM: S2 + S3 ≥ 64 Total agents 4PM–6PM: S3 + S4 ≥ 73 Total agents 6PM–8PM: S3 + S4 ≥ 82 Total agents 8PM–10PM: S4 ≥ 43 Total agents 10PM–12AM: S4 + S5 ≥ 52 Total agents 12AM–6AM: S5 ≥ 15 and Si ≥ 0 (for i = 1 to 5)

Figure 3.6 A template of a spreadsheet model for pure cost-benefit-trade-off problems.

Summary of Formulation Procedure for Cost-Benefit-Tradeoff Problems
Identify the activities for the problem at hand. Identify an appropriate overall measure of performance (commonly cost). For each activity, estimate the contribution per unit of the activity to the overall measure of performance. Identify the benefits that must be achieved. For each benefit, identify the minimum acceptable level and then the contribution of each activity to that benefit. Enter the data in steps 3 and 5 into data cells. Designate changing cells for displaying the decisions. In the row for each benefit, use SUMPRODUCT to calculate the level achieved. Enter >= and the minimum acceptable level in two adjacent cells. Designate a target cell. Use SUMPRODUCT to calculate this measure of performance.

Types of Functional Constraints
Form* Typical Interpretation Main Usage Resource constraint LHS ≤ RHS For some resource, Amount used ≤ Amount available Resource-allocation problems and mixed problems Benefit constraint LHS ≥ RHS For some benefit, Level achieved ≥ Minimum Acceptable Cost-benefit-trade-off problems and mixed problems Fixed-requirement constraint LHS = RHS For some quantity, Amount provided = Required amount Transportation problems and mixed problems * LHS = Left-hand side (a SUMPRODUCT function). RHS = Right-hand side (a constant). Table 3.6 Types of Functional Constraints

Continuing the Super Grain Case Study
David and Claire conclude that the spreadsheet model needs to be expanded to incorporate some additional considerations. In particular, they feel that two audiences should be targeted — young children and parents of young children. Two new goals The advertising should be seen by at least five million young children. The advertising should be seen by at least five million parents of young children. Furthermore, exactly \$1,490,000 should be allocated for cents-off coupons.

Benefit and Fixed-Requirement Data
Number Reached in Target Category (millions) Each TV Commercial Each Magazine Ad Each Sunday Ad Minimum Acceptable Level Young children 1.2 0.1 5 Parents of young children 0.5 0.2 Contribution Toward Required Amount Required Amount Coupon redemption \$40,000 \$120,000 \$1,490,000 Table 3.7 and Table 3.8 The benefit data and fixed-requirement data for the Revised Super Grain Corp. Advertising Mix Problem.

Figure 3.7 The spreadsheet model for the revised Super Grain problem (Section 3.4), including the target cell Total Exposures (H19), the changing cells Number of Ads (C19:E19), and the optimal solution obtained by the Solver.

Algebraic Formulation
Let TV = Number of commercials for separate spots on television M = Number of advertisements in magazines. SS = Number of advertisements in Sunday supplements. Maximize Exposure = 1,300TV + 600M + 500SS subject to Ad Spending: 300TV + 150M + 100SS ≤ 4,000 (\$thousand) Planning Cost: 90TV + 30M + 30SS ≤ 1,000 (\$thousand) Number of TV Spots: TV ≤ 5 Young children: 1.2TV + 0.1M ≥ 5 (millions) Parents: 0.5TV + 0.2M + 0.2SS ≥ 5 (millions) Coupons: 40M + 120SS = 1,490 (\$thousand) and TV ≥ 0, M ≥ 0, SS ≥ 0.

Template for Mixed Problems
Figure 3.8 A template of a spreadsheet model for pure cost-benefit-trade-off problems.

LP Example #2 (Diet Problem)
A prison is trying to decide what to feed its prisoners. They would like to offer some combination of milk, beans, and oranges. Their goal is to minimize cost, subject to meeting the minimum nutritional requirements imposed by law. The cost and nutritional contents of each food, along with the minimum nutritional requirements are shown below. Milk (gallons) Navy Beans (cups) Oranges (large Calif. Valencia) Minimum Daily Requirement Niacin (mg) 3.2 4.9 0.8 13.0 Thiamin (mg) 1.12 1.3 0.19 1.5 Vitamin C (mg) 32 93 45 Cost (\$) 2.00 0.20 0.25 Question: What should the diet for each prisoner be?

Algebraic Formulation
Let x1 = gallons of milk per prisoner, x2 = cups of beans per prisoner, x3 = number of oranges per prisoner. Minimize Cost = \$2.00x1 + \$0.20x2 + \$0.25x3 subject to Niacin: 3.2x x x3 ≥ 13 mg Thiamin: 1.12x x x3 ≥ 1.5 mg Vitamin C: 32x1 + 93x3 ≥ 45 mg and x1 ≥ 0, x2 ≥ 0, x3 ≥ 0.

George Dantzig’s Diet heuristic solution. Cost = \$39.93.
Stigler (1945) “The Cost of Subsistence” heuristic solution. Cost = \$39.93. Dantzig invents the simplex method (1947) Stigler’s problem “solved” in 120 man days. Cost = \$39.69. Dantzig goes on a diet (early 1950’s), applies diet model: ≤ 1,500 calories objective: maximize (weight minus water content) 500 food types Initial solutions had problems 500 gallons of vinegar 200 bouillon cubes For more details, see July-Aug 1990 Interfaces article “The Diet Problem”

Least-Cost Menu Planning Models in Food Systems Management
Used in many institutions with feeding programs: hospitals, nursing homes, schools, prisons, etc. Menu planning often extends to a sequence of meals or a cycle. Variety important (separation constraints). Preference ratings (related to service frequency). Side constraints (color, categories, etc.) Generally models have reduced cost about 10%, met nutritional requirements better, and increased customer satisfaction compared to traditional methods. USDA uses these models to plan food stamp allotment. For more details, see Sept-Oct 1992 Interfaces article “The Evolution of the Diet Model in Managing Food Systems”