Sketching the Feasible Region (Edexcel International AS Maths: Decision 1): Revision Note

Exam code: XMA01

Paul

Written by: Paul

Reviewed by: Dan Finlay

Updated on

Introduction to Solving an LP Problem Graphically

How do I solve a linear programming problem graphically?

  • For problems with two decision variables

    • Plot the constraints (inequalities) accurately on a graph

    • This leads to the feasible region

    • The optimal solution will be one of the vertices of the feasible region

  • In harder problems

    • There may be a third decision variable

      • There will be a connection to one (or both) of the other decision variables

      • All constraints can be rewritten in terms of just two of the decision variables

      • E.g. x, y, z are the numbers of chairs, tables and desks made by a furniture manufacturer
        The number of desks produced is twice the number of tables, i.e. z=2y

    • The optimal solution may not give integer values for the decision variables but the context demands they are

      • E.g. Is it possible to make 3.65 chairs and 4.2 tables per day?

Feasible Region

What is the feasible region?

  • The feasible region is the set of all values that satisfy all the constraints in a linear programming problem

    • In practice, this is the area on a graph that satisfies all of the inequalities

      • This includes the non-negativity inequality

How do I find the feasible region?

  • To find the feasible region

    • Accurately plot each inequality (constraint) on a graph

      • Plot each inequality as a straight line

      • Rearranging to the form ymx+c or ymx+c may help

      • It can be easier to determine two points that lie on each line, plot and join them up

      • Draw the line solid for inequalities involving ≤ or ≥, or dotted for inequalities involving > or <

      • < and > are rare in linear programming problems

    • Shade the part of the graph not satisfied by each inequality

      • It is easier to see a 'blank' area rather than an area shaded several times

    • The feasible region is the area on the graph left unshaded

      • It is the area that satisfies all of the inequalities

      • It is usually labelled with the capital letter R

  • Label the line of each inequality around the edge of the graph

Examiner Tips and Tricks

  • Exam questions will provide a graph for you to accurately plot the inequalities

    • Alternatively they may provide an accurate graph with some or all of the inequalities already plotted

  • It is helpful to complete the shading for each inequality as you add it to the graph

    • If you leave the shading to the end it can become confusing

Worked Example

A linear programming problem is formulated as

Maximise

P=30x+40y

subject to

x+y103x+2y24x+2y18x, y0

Show graphically the feasible region, R, of the linear programming problem.

Answer:

The objective function is not needed to plot the feasible region

Plot each inequality as a straight line graph with the 'unwanted' side shaded
For this problem, all lines will be solid lines
The first constraint will be the line y=x+10 (gradient -1 and y-axis intercept 10)
(You may find it easier to 'see' that points like (0, 10) and (10, 0) lie on the line, which you can plot and join up)

feasible-1

Plot and label the rest of the inequalities in the same way

feasible-2

Label the feasible region with R

feasible-3-green

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Paul

Author: Paul

Expertise: Maths Content Creator

Paul has taught mathematics for 20 years and has been an examiner for Edexcel for over a decade. GCSE, A level, pure, mechanics, statistics, discrete – if it’s in a Maths exam, Paul will know about it. Paul is a passionate fan of clear and colourful notes with fascinating diagrams.

Dan Finlay

Reviewer: Dan Finlay

Expertise: Portfolio Lead

Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.