Review Exercise 2. 1 a Chemical A 5x+ Chemical B 2x+ 2y12 [ x+ Chemical C [ 4 12]

Similar documents
Slide 1 Math 1520, Lecture 10

Standard Form An LP is in standard form when: All variables are non-negativenegative All constraints are equalities Putting an LP formulation into sta

4.3 Minimizing & Mixed Constraints

Moments Mixed exercise 4

Decision Mathematics D1 (6689) Practice paper A mark scheme

Proof by induction ME 8

Decision Mathematics D1

Mark scheme Pure Mathematics Year 1 (AS) Unit Test 2: Coordinate geometry in the (x, y) plane

1. Introduce slack variables for each inequaility to make them equations and rewrite the objective function in the form ax by cz... + P = 0.

Mathematics (JAN12MD0201) General Certificate of Education Advanced Level Examination January Unit Decision TOTAL.

Decision Mathematics D1

Review exercise 2. 1 The equation of the line is: = 5 a The gradient of l1 is 3. y y x x. So the gradient of l2 is. The equation of line l2 is: y =

Slack Variable. Max Z= 3x 1 + 4x 2 + 5X 3. Subject to: X 1 + X 2 + X x 1 + 4x 2 + X X 1 + X 2 + 4X 3 10 X 1 0, X 2 0, X 3 0

Core Mathematics C1 (AS) Unit C1

UNIT-4 Chapter6 Linear Programming

Math 354 Summer 2004 Solutions to review problems for Midterm #1

Pearson Edexcel GCE Decision Mathematics D2. Advanced/Advanced Subsidiary

Decision Mathematics D1

Summary of the simplex method

Professor Alan H. Stein October 31, 2007

The Simplex Method of Linear Programming

Example. 1 Rows 1,..., m of the simplex tableau remain lexicographically positive

Systems Analysis in Construction

Prelude to the Simplex Algorithm. The Algebraic Approach The search for extreme point solutions.

MATH2070 Optimisation

Dr. S. Bourazza Math-473 Jazan University Department of Mathematics

b UVW is a right-angled triangle, therefore VW is the diameter of the circle. Centre of circle = Midpoint of VW = (8 2) + ( 2 6) = 100

Chapter 4 The Simplex Algorithm Part I

Non-Standard Constraints. Setting up Phase 1 Phase 2

Lesson 27 Linear Programming; The Simplex Method

Decision Mathematics D1 Advanced/Advanced Subsidiary. Tuesday 9 June 2015 Morning Time: 1 hour 30 minutes

9 Mixed Exercise. vector equation is. 4 a

DEPARTMENT OF STATISTICS AND OPERATIONS RESEARCH OPERATIONS RESEARCH DETERMINISTIC QUALIFYING EXAMINATION. Part I: Short Questions

9.5 THE SIMPLEX METHOD: MIXED CONSTRAINTS

Review Questions, Final Exam

Pure Mathematics Year 1 (AS) Unit Test 1: Algebra and Functions

min 4x 1 5x 2 + 3x 3 s.t. x 1 + 2x 2 + x 3 = 10 x 1 x 2 6 x 1 + 3x 2 + x 3 14

April 2003 Mathematics 340 Name Page 2 of 12 pages

Solutionbank Edexcel AS and A Level Modular Mathematics

A-LEVEL Further Mathematics

AM 121: Intro to Optimization

Special cases of linear programming

REVISION SHEET DECISION MATHS 2 DECISION ANALYSIS

Decision Mathematics D1 Advanced/Advanced Subsidiary. Wednesday 23 January 2013 Morning Time: 1 hour 30 minutes

LINEAR PROGRAMMING 2. In many business and policy making situations the following type of problem is encountered:

Linear Programming II NOT EXAMINED

Linear Programming and the Simplex method

c) Place the Coefficients from all Equations into a Simplex Tableau, labeled above with variables indicating their respective columns

Decision Mathematics D1

1. Algebraic and geometric treatments Consider an LP problem in the standard form. x 0. Solutions to the system of linear equations

Circles, Mixed Exercise 6

Draft Version 1 Mark scheme Further Maths Core Pure (AS/Year 1) Unit Test 1: Complex numbers 1

Section 4.1 Solving Systems of Linear Inequalities

Chapter 1: Linear Programming

Linear Programming, Lecture 4

Ω R n is called the constraint set or feasible set. x 1

CSC Design and Analysis of Algorithms. LP Shader Electronics Example

Decision Mathematics D2 Advanced/Advanced Subsidiary. Thursday 6 June 2013 Morning Time: 1 hour 30 minutes

Decision Mathematics D1 Advanced/Advanced Subsidiary. Friday 17 May 2013 Morning Time: 1 hour 30 minutes

2. Linear Programming Problem

PLC Papers. Created For:

In Chapters 3 and 4 we introduced linear programming

Part III: A Simplex pivot

Review Solutions, Exam 2, Operations Research

Taylor Series 6B. lim s x. 1 a We can evaluate the limit directly since there are no singularities: b Again, there are no singularities, so:

Lecture 11 Linear programming : The Revised Simplex Method

MATH 4211/6211 Optimization Linear Programming

PhysicsAndMathsTutor.com. Mark Scheme (Results) Summer Pearson Edexcel GCE Decision Mathematics /01

Math Models of OR: Some Definitions

Gauss-Jordan Elimination for Solving Linear Equations Example: 1. Solve the following equations: (3)

Constant acceleration, Mixed Exercise 9

The augmented form of this LP is the following linear system of equations:


...(iii), x 2 Example 7: Geetha Perfume Company produces both perfumes and body spray from two flower extracts F 1. The following data is provided:

Mathematics (JUN13MD0201) General Certificate of Education Advanced Level Examination June Unit Decision TOTAL.

Chapter 5 Linear Programming (LP)

Introduction to Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

i j k i j k i j k

Q Scheme Marks AOs Pearson. Notes. Deduces that 21a 168 = 0 and solves to find a = 8 A1* 2.2a

9.1 Linear Programs in canonical form

4. Duality Duality 4.1 Duality of LPs and the duality theorem. min c T x x R n, c R n. s.t. ai Tx = b i i M a i R n

( ) ( ) 2 1 ( ) Conic Sections 1 2E. 1 a. 1 dy. At (16, 8), d y 2 1 Tangent is: dx. Tangent at,8. is 1

UNIVERSITY OF KWA-ZULU NATAL

MA 162: Finite Mathematics - Section 3.3/4.1

The Graphical Method & Algebraic Technique for Solving LP s. Métodos Cuantitativos M. En C. Eduardo Bustos Farías 1

Master of Intelligent Systems - French-Czech Double Diploma. Hough transform

The Simplex Method. Lecture 5 Standard and Canonical Forms and Setting up the Tableau. Lecture 5 Slide 1. FOMGT 353 Introduction to Management Science

Mark Scheme (Results) June GCE Decision D2 (6690) Paper 1

Introduction to the Simplex Algorithm Active Learning Module 3

The Simplex Algorithm and Goal Programming

56:171 Operations Research Fall 1998

IE 400 Principles of Engineering Management. The Simplex Algorithm-I: Set 3

( y) ( ) ( ) ( ) ( ) ( ) Trigonometric ratios, Mixed Exercise 9. 2 b. Using the sine rule. a Using area of ABC = sin x sin80. So 10 = 24sinθ.

OPTIMISATION 3: NOTES ON THE SIMPLEX ALGORITHM

Understanding the Simplex algorithm. Standard Optimization Problems.

Solutionbank Edexcel AS and A Level Modular Mathematics

Simplex Method in different guises

( ) ( ) or ( ) ( ) Review Exercise 1. 3 a 80 Use. 1 a. bc = b c 8 = 2 = 4. b 8. Use = 16 = First find 8 = 1+ = 21 8 = =

Lecture 2: The Simplex method

The Big M Method. Modify the LP

Transcription:

Review Exercise a Chemical A 5x+ y 0 Chemical B x+ y [ x+ y 6] b Chemical C 6 [ ] x+ y x+ y x, y 0 c T = x+ y d ( x, y) = (, ) T = Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a Maximise P= 00x+ 500y b Finishing.5x+ y56 x+ 8y (o.e.) Packing x+ y0 x+ y 0 (o.e.) c d For example, point testing Test all corner points in feasible region. Find profit at each and select point yielding maximum. profit line Draw profit lines. Select point on profit line furthest from the origin. e Using a correct, complete method. Making 6 Oxford and York gives a profit = 500 integer (6, ) 500 (.,.) (, ) 00 (6, 0) 800 (0, 0) 5000 f The line.5x+ y = 9 passes through (6, ) so reduce finishing by hours. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a Objective: maximise P= 0x+ 0 y (or P= 0.x+ 0. y ) subject to: x+ y00 x+ y500 0 x ( x+ y ) xy 00 x 0 ( x+ y ) x y 00 x, y 0 b c Visible use of objective line method objective line drawn or vertex testing all vertices tested Vertex testing (0, 60) 600 (80, 0) 00 (00, 00) 9 000 (00, 00) 8000 Intersection of y= x and x+ y = 500 (00, 00) profit 90 (or 9000 p) Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a b Visible use of objective line method objective line drawn or vertex testing. 5, 5 8, (, 8) 8(, 6) 6 6 6 5 Optimal point, 6 with value 5 6 c Visible use of objective line method objective line drawn, or vertex testing all vertices tested. 5, 6 8, not an integer try (, ) 0 (, 8) 8 not an integer try (8, ) (, 6) Optimal point (8, ) with value, so Becky should use kg of bird feeder and.5 kg of bird table food. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

5 a Objective: maximise P= 0.x+ 0. y ( P= 0x+ 0 y ) subject to: x6.5 y8 x+ y yx x, y0 b Visible use of objective line method objective line drawn (e.g. from (, 0) to (0, )) or all 5 points tested. vertex testing (0, 0) 0;(, 8).;(, 8).;(6.5, 5.5).;(6.5, 0).6 [ ] Optimal point is (6.5, 5.5) 6 500 type X and 5500 type Y c P = 0.(6500) + 0.(5500) = 00 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 5

6 a Maximise P = 50x + 80y + 60z Subject to x+ y+ z 0 x+ y+ z0 x+ y+ z50 where x, y, z 0 b Initialising tableau b.v. x y z r s t value r 0 0 0 s 0 0 0 t 0 0 50 P 50 80 60 0 0 0 0 Choose correct pivot, divide R by State correct row operation R R, R R, R + 80R, R c The solution found after one iteration has a slack of 0 units of black per day d i b.v. x y z r s t value r 0 0 0 y 0 0 0 t 0 0 0 0 P 0 0 0 0 0 0 600 (given) b.v. x y z r s t value z 0 0 6 R y 0 0 6 R R t 0 0 0 0 R no change P 0 0 0 R + 0R ii Not optimal, as there is a negative value in profit row iii x= 0 y= 6 z = 6 P=. r= 0, s= 0, t = 0 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 6

a Objective: Maximise P= x+ 5y+ z Subject to x+ y+ z 5 x+ y+ z0 b x+ y+ z b.v. x y z r s r 0 5 0 s 0 0 y 0 0 P 0 0 0 t value R R R R 6 R 5 0 R + 5R b.v. x y z r s 5 x 0 0 s 0 0 8 y 0 0 8 P 0 0 0 8 P= x=, y=, z = 0 t value 8 8 89 8 R R + R R R R + R c There is some slack on s, so do not increase blending: therefore increase Processing and Packing which are both at their limit at present. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

8 a x+ y+ z b i x+ y+ z+ s = ii s ( 0) is the slack time on the machine in hours c euro d b.v. x y z r s value r 0 R 6R z 0 6 R P 0 0 0 R + R b.v. x y z t s value y 0 R 5 5 z 0 R R 8 8 P 0 0 R + R Profit = euros y = z =.5 x = r = s = 0 e Cannot make a lamp f e.g. (0, 0, 0) or (0, 6, ) or (,, ) checks in both inequalities Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 8

9 a Small (x) Board (m) Time (R) 0 Medium (y) 0 0 Large (z) 5 50 Available 00 000 Board Time 0 5 00 x+ y+ z x+ y+ 6z0 0x+ 0y+ 50z000 x+ y+ 5z00 b P= 0x+ 0y+ 8z c b.v. x y z r s values r 6 0 0 s 5 0 00 P 0 0 8 0 0 0 θ = 0, θ = 50; pivot d Row b.v. x y z r s value operation y 0 0 R s 0 0 R R P 5 0 5 0 600 R + 0R θ = 0, θ = 80;pivot Row b.v. x y z r s value operation y 0 0 R R x 0 80 R P 0 0 0 0 000 R + 5R e This tableau is optimal as there are no negative numbers in the profit line. f Small 80, medium 0; large 0 Profit 000 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 9

0 a The constraints include a mixture of and variables. b x + y + z + s = 0 x + y + z + s = 5 x + y + z s + a = c The purpose is to maximise I = a which =x+ y+ zs So Ixy z+ s = This gives the final row of the tableau. d The smallest value in the bottom row is and the smallest θ value is a (6) so this is the pivot and we obtain: b.v. x y z s s s a value s 0 0.5.5 0 0.5 0.5 s 0.5 0.5 0 0.5 0.5 9 x 0.5 0.5 0 0 0.5 0.5 6 P 0.5.5 0 0 0.5 0.5 6 I 0 0 0 0 0 0 0 e There are no negative values in the bottom row, so the optimal value of I is 0 when a = 0 f There is a negative value in the bottom row. g b.v. x y z s s s Value z 0 0 y 0 0 x 0 0 P 0 0 0 5 5 The maximum value of P is 0 = 6 which occurs when x = 5, y=, z=, s = s = s = 0 5 0 Row operation R R R R R R+ R Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 0

a x + y + z + s = 8 x + y + z s + a = 6 x + 5y + z s + a = 0 b New objective is maximise I = (a + a ) a = x + y + z s 6 a = x + 5y + z s 0 In terms of non-basic variables, the new objective is maximise I = 5x + 6y + z s s 6 c b.v. x y z s s s a a value s 0 0 0 0 8 a 0 0 0 6 a 5 0 0 0 0 P 0 0 0 0 0 0 I 5 6 0 0 0 6 d st iteration Pivot is y-column a row Row b.v. x y z s s s a a Value operation s 0 0 0 5 5 5 5 0 R R 5 a 0 0 5 5 5 5 R R 5 y 0 0 0 5 5 5 5 5 R 6 P 0 0 0 0 5 5 5 5 R R 5 6 6 I 0 0 0 R5 + R 5 5 5 5 5 nd iteration Pivot is x column a row Row b.v. x y z s s s a a Value operation s 0 0 5 5 8 R R x 0 5 5 0 5 0 R y 0 8 0 R R P 0 0 6 5 5 8 0 R + R I 0 0 0 0 0 0 0 R5+ R Basic feasible solution is x= 0, y=, z= 0, s 8 =, s = s = a = a = 0 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a x + y + z + s = 5x + y + z + s = 60 x s + a = b P = x + y + z Ma = x + y + z M( x + s ) P = x( + M) + y + z M Ms P ( + M)x y z + Ms = M c b.v. x y z s s s a value s 0 0 0 s 5 0 0 0 60 a 0 0 0 0 P ( + M) 0 0 M 0 M d The most negative value in the P row is in the x-column so, in the first iteration, x enters the basic variables. e st iteration x column a row is the pivot b.v. x y z s s s a Value Row operation s 0 0 8 R R s 0 0 5 5 50 R 5R x 0 0 0 0 P 0 0 0 ( + M) R+ ( + M) R nd iteration z column s row is the pivot b.v. x y z s s s a Value Row operation z 0 0 8 s 0 0 R R x 0 0 0 0 P 0 5 0 0 M R+ R All entries in the P row are non-negative so the tableau represents the optimal solution. x =, y = 0, z = 8, s = 0, s =, s = 0, a = 0 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a x + y + z + s = 6 x + z + s = 5 x + y s + a = 0 b Maximise P = x + y z Ma = x + y z M(0 x y + s ) = x(m ) + y(m + ) z 0M Ms Rearranging gives P (M )x (M + )y + z + Ms = 0M c b.v. x y z s s s a value s 0 0 0 6 s 0 0 0 0 5 a 0 0 0 0 P (M ) (M + ) 0 0 M 0 0M d st iteration y column a row is the pivot b.v. x y z s s s a Value Row operation s 0 0 6 R R s 0 0 0 0 5 y 0 0 0 0 P 5 0 0 0 M + 0 R + ( M + ) R nd iteration s column s row is the pivot Row b.v. x y z s s s a Value operation s 0 0 R s 0 0 0 0 5 y 0 0 0 R + R P 6 0 0 0 M 6 R+ R Maximum value of P = 6 Minimum value of C = 6 This occurs when x = 0, y =, z = 0, s = 0, s = 5, s = Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

5 a 6 a b Here we have that I and J depend only on E, whereas H depends on C, D, E and F. Hence we need separate nodes with a dummy. b D will only be critical if it lies on the longest path Path A to G Length A B E G A C F G 5 A C D E G + x So we need + x to be the longest, or equal longest +x5 x Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a b Total float on A = 0 0 5 = 5 Total float on H = 9 = Total float on B = 0 0 0 = 0 Total float on I = 9 = 0 Total float on C = 0 = Total float on J = 5 8 = Total float on D = 0 0 8 = Total float on K = 5 = 0 Total float on E = 0 0 = Total float on L = 5 8 = 0 Total float on F = 9 0 9 = 0 Total float on M = 5 = 9 Total float on G = 8 = c Critical activities: B, F, I, K and L length of critical path is 5 days d New critical path is B F H L length of new critical path is 6 days 8 a x = 0 y = [latest out of ( + ) and (5 + )] z = 9 [Earliest out of ( ) and (9 ) and (6 )] b Length is Critical activities: B, D, E and L c i Total float on N = = 5 ii Total float on H = 6 5 = 8 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 5

9 a For example, it shows dependence but it is not an activity. G depends on A and C only but H and I depend on A, C and D. b c So B, C, E, F, I, J and L d Total float on A = 0 9 = Total float on H = 5 = Total float on D = = Total float on K = 5 6 = Total float on G =8 5 = e Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 6

0 a Critical activities are B, F, J, K and N length of critical path is 5 hours I is not critical. b Total float on A = 5 0 = Total float on H = 6 = Total float on C = 9 0 6 = Total float on I = 6 9 5 = Total float on D = = 5 Total float on L = = Total float on E = 9 = Total float on M = 6 = Total float on G = 9 = Total float on P = 5 8 = c a d Look at 6.5 in the chart in c. F, E and G b 6 days, workers c Delay the start of H until time d Activity H would have to take place on its own so the project will be delayed by at least days. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

a b 8 days c ADFIKN d e workers f e.g. delay the start times of: E to time G to time H to time J to time 0 L to time M to time 6 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 8

a b A, C, G, H, J, K and L All critical activities have a zero total float. c Total float = 5 = d Either 6 8 =.6 ( d.p.) so at least workers needed (here 6 is the total number of hours required for all the activities) or 69 hours into the project activities J, K, I and M must be happening so at least workers will be needed. e New shortest time is 89 hours. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 9

a b Critical activities: A, C, F and H; length of critical path = c Total float on B = 0 5 = Total float on E = = Total float on D = 9 = Total float on G = 9 8 = d e For example; Minimum time for workers is days. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free. 0

Challenge a Optimal values x = y = 5 b x =, y = P = x = 5, y = P = 5 c x =, y = P = d If the gradient of the objective line is similar to the gradient of a constraint that runs through the optimal vertex, then the optimal integer solution may not lie close to the optimal vertex. Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

Challenge We need to maximise D= x+ y z subject to the constraints x+ y+ z6 x+ z5 x+ y0 We rewrite these using slack, surplus and artificial variables to obtain: x+ y+ z+ s = 6 x+ z+ s = 5 x+ y s+ a = 0 The new objective function is: I = a = x+ ys 0 So Ix y+ s =0 So the initial tableau is: b.v. x y z s s s a Value s 0 0 0 6 s 0 0 0 0 5 a 0 0 0 0 D 0 0 0 0 0 I 0 0 0 0 0 Both the x and y columns have the same value in the I row. But sin the x-column has the smallest θ value (9) so we use that as the pivot. We then obtain: b.v. x y z s s Row s a Value operation x 0 0 0 9 R s 0 R R a 0 5 R R D 0 9 R R I 0 0 0 R5 + R Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.

Challenge continued The y-column still has a negative value in the I row and only the x-row has a positive θ value so we need to use that as the pivot giving. Row b.v. x y z s s s a Value operation y 0 0 0 R s 0 0 0 0 0 5 R+ R a 5 0 0 6 R+ 5R D 6 0 0 0 0 6 R+ 6R I 0 0 0 R5 + R There are now no negative entries in the bottom two rows so we have reached the optimal solution: y =, x = z = 0 and C = 6 Pearson Education Ltd 08. Copying permitted for purchasing institution only. This material is not copyright free.