2Learning Objectives Learn sensitivity concepts Understand, using graphs, impact of changes in objective function coefficients, right-hand-side values, and constraint coefficients on optimal solution of a linear programming problem.Generate answer and sensitivity reports using Excel's Solver.Interpret all parameters of reports for maximization and minimization problems.Analyze impact of simultaneous changes in input data values using 100% rule.Analyze the impact of addition of new variable using pricing-out strategy.
3Introduction (1 of 2)Optimal solutions to LP problems have been examined under deterministic assumptions.Conditions in most real world situations are dynamic and changing.After an optimal solution to problem is found, input data values are varied to assess optimal solution sensitivity.This process is also referred to as sensitivity analysis or post-optimality analysis.
4Introduction (2 of 2)Sensitivity analysis determines the effect on optimal solutions of changes in parameter values of the objective function and constraint equationsChanges may be reactions to anticipated uncertainties in the parameters or the new or changed information concerning the model
5The Role of Sensitivity Analysis of the Optimal Solution Is the optimal solution sensitive to changes in input parameters?Possible reasons for asking this question:Parameter values used were only best estimates.Dynamic environment may cause changes.“What-if” analysis may provide economical and operational information.
61. Sensitivity Analysis of Objective Function Coefficients.
7Ranges of OptimalityThe optimal solution will remain unchanged as long as:an objective function coefficient lies within its range of optimalitythere are no changes in any other input parameters.The value of the objective function will change if the coefficient multiplies a variable whose value is nonzero.
8Sensitivity Analysis Using Graphs Example 1: High Note Sound Company(HNSC) (1 of 4) HNSC Manufactures quality CD players and stereo receivers.Each product requires skilled craftsmanship.LP problem formulation:Objective: maximize profit = $50C + $120Rsubject to2C + 4R 80 (Hours of electricians' time available)3C + R 60 (Hours of audio technicians' time available)C, R 0 (Non-negativity constraints)Where:C = number of CD players to make.R = number of receivers to make.
9Sensitivity Analysis Using Graphs Example 1(2 of 4)
10Sensitivity Analysis Using Graphs Example 1 (3 of 4)Impact of price change of ReceiversIf unit profit per stereo receiver (R) increased from $120 to $150, is corner point a still the optimal solution? YES !But Profit is $3,000 = 0 ($50) + 20 ($150)
11Sensitivity Analysis Using Graphs Example 1 (4 of 4) Impact of price change of ReceiversIf receiver’s profit coefficient changed from $120 to $80, slope of isoprofit line changes causing corner point (b) to become optimal.But Profit is $1,760 = 16 ($50) + 12 ($80).
13Sensitivity Analysis Using Graphs Example 2:(2 of 5) We now demonstrate the search for an optimal solutionStart at some arbitrary profit, say profit = $2,000...X21200Then increase the profit, if possible......and continue until it becomes infeasibleProfit =$5040Profit = $2,3,4,800Recall the feasible Region600X1400600800
14Sensitivity Analysis Using Graphs Example 2 (3 of 5) MODEL SOLUTIONSpace Rays = 480 dozensZappers = 240 dozensProfit = $5040This solution utilizes all the plastic and all the production hours.Total production is only 720 (not 800).Space Rays production exceeds Zapper by only 240 dozens (not 450).
15Sensitivity Analysis Using Graphs Example 2 (4 of 5) 1200The effects of changes in an objective function coefficient on the optimal solution800600Max 8x1 + 5x2Max 4x1 + 5x2Max 3.75x1 + 5x2Max 2x1 + 5x2X1400600800
16Sensitivity Analysis Using Graphs Example 2 (5 of 5) 1200Range of optimalityMax8x1 + 5x210Max 10 x1 + 5x2Max 3.75 x1 + 5x23.75800600Max8x1 + 5x2Max 3.75x1 + 5x2X1400600800The effects of changes in an objective function coefficients on the optimal solution
17The optimality range for an objective coefficient is the range of values over which the current optimal solution point will remain optimal
18For two variable LP problems the optimality ranges of objective function coefficients can be found by setting the slope of the objective function equal to the slopes of each of the binding constraints
19Sensitivity Analysis Using Graphs Example 3: Beaver Creek Pottery (1 of 4)Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 404x2 + 3x2 120x1, x2 0
20Sensitivity Analysis Using Graphs Example 3 (2 of 4)Maximize Z = $100x1 + $50x2subject to: 1x1 + 2x2 404x2 + 3x2 120x1, x2 0
21Sensitivity Analysis Using Graphs Example 3 (3 of 4)Maximize Z = $40x1 + $100x2subject to: 1x1 + 2x2 404x2 + 3x2 120x1, x2 0
22Objective Function Coefficient Sensitivity Range The sensitivity range for an objective function coefficient is the range of values over which the current optimal solution point will remain optimal.The sensitivity range for the xi coefficient is designated as ci.
23Objective Function Coefficient Sensitivity Range for c1 and c2 Example 3 (4 of 4) objective function Z = $40x1 + $50x sensitivity range for:x1: 25 c1 x2: 30 c2 80
24Objective Function Coefficient Sensitivity Range (for a Cost Minimization Model)Minimize Z = $6x1 + $3x2subject to:2x1 + 4x2 164x1 + 3x2 24x1, x2 0sensitivity ranges:4 c1 0 c2 4.5
25Multiple changesThe range of optimality is valid only when a single objective function coefficients changes.When more than one variable changes we turn to the 100% rule.
26The 100% Rule1. For each increase (decrease) in an objective function coefficient, calculate (and express as a percentage) the ratio of the change in the coefficient to the maximum possible increase (decrease) as determined by the limits of the range of optimality.2. Sum all these percent changes. If the total is less than 100 percent, the optimal solution will not change. If this total is greater than or equal to 100%, the optimal solution may change.
27Reduced CostsThe reduced cost for a variable at its lower bound (usually zero) yields:The amount the profit coefficient must change before the variable can take on a value above its lower bound.The amount the optimal profit will change per unit increase in the variable from its lower bound.
282. Sensitivity Analysis of Right Hand Side Values
29Changes in Right-Hand-Side Values of Constraints The sensitivity range for a RHS value is the range of values over which the quantity (RHS) values can change without changing the solution variable mix, including slack variables.
30Sensitivity Analysis of Right-Hand SideValues Any change in the right hand side of a binding constraint will change the optimal solution.Any change in the right-hand side of a nonbinding constraint that is less than its slack or surplus, will cause no change in the optimal solution.
31Changes in Constraint Quantity (RHS) Values Increasing the Labor Constraint (1 of 3)Example 1: Beaver CreekMaximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 404x2 + 3x2 120x1, x2 0
32Changes in Constraint Quantity (RHS) Values Sensitivity Range for Labor Constraint (2 of 3)Example 1: Beaver CreekSensitivity range for:30 q1 80 hr
33Changes in Constraint Quantity (RHS) Values Sensitivity Range for Clay Constraint (3 of 3)Example 1: Beaver CreekSensitivity range for:60 q2 160 lb
34Sensitivity Analysis of Right-Hand SideValues & Shadow Prices In sensitivity analysis of right-hand sides of constraints we are interested in the following questions:Keeping all other factors the same, how much would the optimal value of the objective function (for example, the profit) change if the right-hand side of a constraint is changed by one unit?For how many additional or fewer units this per unit this change will be valid?
35Changes in Right-hand-side (RHS) & Shadow Prices RHS of Binding Constraint -If RHS of non-redundant constraint changes, size of feasible region changes.If size of region increases, optimal objective function improves.If size of region decreases, optimal objective function worsens.Relationship expressed as Shadow Price.Shadow Price is change in optimal objective function value for one unit increase in RHS.
36Shadow Prices (Dual Values) Defined as the marginal value of one additional unit of resource.The sensitivity range for a constraint quantity value is also the range over which the shadow price is valid.
37Range of FeasibilityThe set of right - hand side values for which same set of constraints determines the optimal point.Within the range of feasibility, shadow prices remain constant; however, the optimal solution will change.
38Example 2: High Note Sound Company Problem (1 of 6) HNSC Manufactures quality CD players and stereo receivers.Each product requires skilled craftsmanship.LP problem formulation:Objective: maximize profit = $50C + $120Rsubject to2C + 4R 80 (Hours of electricians' time available)3C + R 60 (Hours of audio technicians' time available)C, R 0 (Non-negativity constraints)Where:C = number of CD players to make.R = number of receivers to make.
39Sensitivity Analysis of Right-hand-side (RHS) Values Example 2: High Note Sound Company (2 of 6) May change the feasible region size.May change or move corner points.
40Increase in Electricians’ Available Time Example2: High Note Sound Company (3 of 6) As available electricians’ time increases, corner points a and b will move closer to one other.Further increases in available electricians’ time may make this constraint redundant.
41Decrease in Electricians’ Available Time Example 2: High Note Sound Company (4 of 6) As available electricians’ time decreases, corner points b and c move closer to one another from their current locations.Corner points b and c will no longer be feasible, and intersection of electricians’ time constraint with horizontal (C) axis will become a new feasible corner point.
42Primary information is provided by Shadow Price Resources available: 80 hours of electricians’ time.60 hours of audio technicians’ time.Final Values in table reveal optimal solution requires:all 80 hours of electricians’ time.Only 20 hours of audio technicians’ time.Electricians’ time constraint is binding.Audio technicians’ time constraint is non-binding.40 unused hours of audio technicians’ time are referred to as slack.
43Changes in Right-Hand-Side (RHS) Example 2: High Note Sound Company (5 of 6)In case of electrician hours Shadow Price is $30.For each additional hour of electrician time that firm can increase profits by $30.
44Changes in RHS of a Non-binding Constraint Example 2: High Note Sound Company (6 of 6) Audio technicians’ time has 40 unused hours.No interest in acquiring additional hours of resource.Shadow price for audio technicians’ time is zero.Once 40 hours is lost (current unused portion, or slack) of audio technicians’ time, resource also becomes binding.Any additional loss of time will clearly have adverse effect on profit.Reduced Cost value - shows amount one will ‘lose’ if solution is forced to make an additional unit.
45Sensitivity Analysis For A Larger Maximization Example Example 3: Anderson Electronics (1 of 8)Considering producing four potential products: VCRs, stereos, televisions (TVs), and DVD players:Profit per unit:VCR Stereo TV DVD$41 $ $ $54
46LP Formulation Example 3: Anderson Electronics (2 of 8) Objective: maximize profit =$29 V + $32 S + $72 T + $54 Dsubject to3 V + 4 S + 4 T + 3 D (Electronic components)2 V + 2 S + 4 T + 3 D (Non-electronic components)1 V + 1 S + 3 T + 2 D (Assembly time in hours)V, S, T, D 0Where: V = number of VCRs to produce.S = number of Stereos to produce.T = number of TVs to produce.D = number of DVD players to produce.
47Non Zero value decision variables, Stereos and DVDs: Excel Solver Sensitivity Report Example 3: Anderson Electronics (3 of 8)Non Zero value decision variables, Stereos and DVDs:Produce 380 Stereos with unit profit of $32.Decision should not change as profit is between $31.33 and $72:Produce 1060 DVDs with unit profit of $54.Decision should not change as profit is between $49 and $64:
48Example 3: Anderson Electronics (4 of 8) Zero value decision variables, VCRs and TVs:Produce 0 VCRs (Reduced cost of $1.00)Decision to make 0 should not change as profit is below $29 – but should change over and $30:Produce 0 TVs with unit cost of $8.00 (Reduced Cost).Decision to make 0 should not change as profit is below $72 – but should change over and $80:
49(Change / Allowable change) 1 Simultaneous Changes In Parameter Values Example 3: Anderson Electronics (5 of 8)Possible to analyze impact of simultaneous changes on optimalsolution only under specific condition:(Change / Allowable change) 1If decrease RHS from 4,700 to 4,200, allowable decrease is 950.The ratio is: 500 / 950 =If increase 200 hours (from 2,500 to 2,700) in assembly time, allowable increase isThe ratio is: 200 / =The sum of these ratios is: Sum of ratios = = < 1 Since sum does not exceed 1, information provided in sensitivity report is valid to analyze impact of changes.
50Simultaneous Changes In Parameter Values Example 3:Anderson Electronics (6 of 8)Decrease of 500 units in electronic component availability reduces size of feasible region and causes profit to decrease.Magnitude of decrease is $1,000 (500 units x $2 per unit).Increase of 200 hours of assembly time results in larger feasible region and net increase in profit.Magnitude of increase is $4,800 (200 hours x $24 per hour).Net impact of both changes simultaneously is an increase in profit by $3,800 ( $4,800 - $1,000).
51Simultaneous Changes In Parameter Values Example 3: Anderson Electronics (7 of 8)Simultaneous Changes in OFC ValuesWhat is impact if selling price of DVDs drops by $3 per unit and at same time selling price of stereos increases by $8 per unit?For current solution to remain optimal, allowable decrease in DVD players is $5, while allowable increase in OFC for stereos is $40.Sum of ratios is: Sum of ratios = $3 / $5 + $8 / $40 = < 1$3 decrease in profit per DVD player causes total profit to decrease by $3,180 (i.e., $3 x 1,060).$8 increase in unit profit of each stereo results in total profit of $3,040 (i.e., $8 x 380).Net impact is a decrease in profit of only $140 to a new value of $69,260.
52 Checking Validity of the 100% Rule Example 3: Anderson Electronics Example (8 of 8)Calculate ratio of reduction in each resource’s availability to allowable decrease for that resource. Sum of ratios = 5/ / /1325 = < 1Required Profit on Each HTS: 5 x shadow price of electronic components +4 x shadow price of non-electronic components +4 x shadow price of assembly timeor 5 x $ x $ x $24 = $106Profit contribution of each HTS has to at least make up shortfall in profit.OFC for HTS must be at least $106 in order for optimal solution to have non-zero value.
53Sensitivity Analysis - Minimization Example Example 4: Burn-Off Diet Drink Example (1 of 2)Plans to introduce miracle drink that will magically burn fat away.
54LP Formulation Example 4: Burn-Off Diet Drink Example (2 of 2) Objective: minimize daily dose cost in cents.4A + 7B + 6C + 3DSubject toA + B + C + D (Daily dose requirement)3A + 4B + 8C + 10D (Chemical X requirement)5A + 3B + 6C + 6D (Chemical Y requirement)10A + 25B + 20C + 40D (Chemical Z max limit)A, B, C, D 0
55Problem Statement (1 of 2) Example Problem 5Two airplane parts: no.1 and no. 2.Three manufacturing stages: stamping, drilling, milling.Decision variables: x1 (number of part no.1 to produce)x2 (number of part no.2 to produce)Model: Maximize Z = $650x x2subject to:4x x2 105 (stamping,hr)6.2x x2 90 (drilling, hr)9.1x x2 110 (finishing, hr)x1, x2 0