Presentation on theme: "– Effects on final tableaus"— Presentation transcript:
1– Effects on final tableaus Thursday, February 28Sensitivity Analysis 2– More on pricing out– Effects on final tableausHandouts: Lecture Notes
2Partial summary of last lecture The shadow price is the unit change in the optimal objective value per unit change in the RHS.The shadow price for a “≥ 0” constraint is called the reduced cost.Shadow prices usually but not always have economic interpretations that are managerially useful.Shadow prices are valid in an interval, which is provided by the Excel Sensitivity Report.Reduced costs can be determined by pricing out
3Running Example (from lecture 4) Sarah can sell bags consisting of 3 gadgets and 2 widgets for $2 each.She currently has 6000 gadgets and 2000 widgets.She can purchase bags with 3 gadgets and 4 widgets for $3.Formulate Sarah’s problem as an LP and solve it.
4Shadow Prices can be found by examining the initial and final tableaus!
5The Initial Basic Feasible Solution Apply the minratio rulemin(6/3, 2/2).The basic feasible solution is x1 = 0, x2 = 0, x3 = 6, x4 = 2What is the entering variable? x2What is the leaving variable? x4
6The 2nd TableauThe basic feasible solution is x1 = 0, x2 = 1, x3 = 3, x4 = 0, z = 2What is the next entering variable? x1What is the next leaving variable? x3
7The 3rd Tableau The optimal basic feasible solution is x1 = 1, x2 = 3, x3 = 0, x4 = 0, z = 3
8Shadow PriceThe shadow price of a constraint is the increase in the optimum objective value per unit increase in the RHS coefficient, all other data remaining equal.What is the shadow price for constraint 1, gadgets on hand?This is the value of an extra gadget on hand.
9Shadow price vs slack variable Claim: increasing the 6 to a 7 is mathematically equivalentto replacing “x3 ≥ 0” with “x3 ≥ -1”. This is also thereduced cost for variable x3.Reason 1. Permitting Sarah to have 7 thousand gadgets isequivalent to giving her 6 thousand and letting her use 1thousand more than she has (at no cost).
10Shadow price vs slack variable Claim: increasing the 6 to a 7 is mathematically equivalentto replacing “x3 ≥ 0” with “x3 ≥ -1”. This is also thereduced cost for variable x3.Reason 2. Any solution to the original problem can betransformed to a solution with RHS 7 by subtracting 1from x3.x1 = 0, x2 = 1, x3 = 3, x4 = 0 => x1 = 0, x2 = 1, x3 = 2, x4 = 0
11Shadow price vs slack variable Looking at theslack variablein the finaltableau revealsshadow prices.What is the optimal solution if x3 ≥ 0?What is the optimal solution if x3 ≥ -1?What is the shadow price for constraint 1?
12Quick SummaryConnection between shadow prices and reduced cost. If xj is the slack variable for a constraint, then its reduced cost is the negative of the shadow price for the constraint.The reduced cost for a variable is its cost coefficient in the final tableauTo do with your partner: what is the shadow price for the 2nd constraint (widgets on hand)?
17How arethe reducedcosts in the2nd tableaubelowobtained?Subtract 1/3 ofconstraint 1and ½ ofconstraint 2from the initialcosts.
18Implications of Reduced Costs Implication 1: increasing the cost coefficient of a non-basic variable by Δ leads to an increase of its reduced cost by Δ.
19What is theeffect ofadding Δ tothe costcoefficientfor x3?FACT: Adding Δ tothe cost coefficient inan initial tableau alsoadds Δ to the samecoefficient insubsequent tableaus
20What is theeffect ofadding Δ tothe costcoefficientfor x2?
21Subtract Δtimes row 3from row 1 toget it back incanonical form.How large canΔ be?Δ ≤ 1 for thetableau toremain optimal.Bound onchanges in costcoefficients.
22Implications of Reduced Costs Implication 2: We can compute the reduced cost of any variable if we know the original column and if we know the “prices” for each constraint.
23Suppose thatwe addanothervariable, sayx5. Should weproduce x5?What is ?
24= 3/2 - 2*1/3 – 1*1/2 = 1/3FACT: We cancompute thereduced cost of anew variable. Ifthe reduced costis positive, itshould be enteredinto the basis.
25More on Pricing OutEvery tableau has “prices.” These are usually called simplex multipliers.The prices for the optimal tableau are the shadow prices.
26Simplex Multipliersπ1=1/3π2= 1/2FACT: x2 is abasic variableand so
27A useful fact from linear algebra If column j in the initial tableau is a linear combination of the other columns, then it is the same linear combination of the other columns in the final tableau.e.g., if A.3 = A A.1 , then
37On varying the RHS Suppose one adds Δ to b1. – This is equivalent to adding Δ times the column corresponding to the first slack variable– One can compute the shadow price and also the effect onThis transformation also provides upper and lower bounds on the interval for which the shadow price is valid.
39Summary of Lecture Using tableaus to determine information Shadow prices and simplex multipliersChanges in cost coefficientsLinear relationships between columns in theoriginal tableau are preserved in the final tableau.Determining upper and lower bounds on Δ so that the shadow price remains valid.