Edexcel AS and A Level Modular Mathematics Decision Mathematics 2 D2
Part of the Course Materials series
- Currently out of stock
- Publication Date
- February 2010
Edexcel’s course for the GCE specification
Matching the specification, Edexcel AS and A Level Modular Mathematics D2 features:
- Completely rewritten by examiners for the new specification.
- Student-friendly worked examples and solutions, leading up to a wealth of practice questions.
- Sample exam papers for thorough exam preparation.
- Regular review sections consolidate learning.
- Opportunities for stretch and challenge presented throughout the course.
PLUS Free LiveText CD-ROM, containing Solutionbank
and Exam Café
to support, motivate and inspire students to reach their potential for exam success.
- Solutionbank contains fully worked solutions with hints and tips for every question in the Student Books.
- Exam Café includes a revision planner and checklist as well as a fully worked examination-style paper with examiner commentary.
1 Transportation problems
1.1 Terminology used in describing and modelling the transportation problem
1.2 Finding an initial solution to the transportation problem
1.3 Adapting the algorithm to deal with unbalanced transportation problems
1.4 Knowing what is meant by a degenerate solution and how to manage such solutions
1.5 Finding shadow costs
1.6 Finding improvement indices and using these to find entering cells
1.7 Using the stepping-stone method to obtain an improved solution
1.8 Formulating a transport problem as a linear programming problem
2 Allocation (assignment) problems
2.1 Reducing cost matrices
2.2 Using the Hungarian algorithm to find a least cost allocation
2.3 Adapting the algorithm to use a dummy location
2.4 Adapting the algorithm to manage incomplete data
2.5 Modifying the algorithm to deal with a maximum profit allocation
2.6 Formulating allocation problems as linear programming problems
3 The travelling salesman problem
3.1 Understanding the terminology used
3.2 Knowing the difference between the classical and practical problems
3.3 Converting a network into a complete network of least distances
3.4 Using a minimum spanning tree method to find an upper bound
3.5 Using a minimum spanning tree method to find a lower bound
3.6 Using the nearest neighbour algorithm to find an upper bound
4 Further linear programming
4.1 Formulating problems as linear programming problems
4.2 Formulating problems as linear programming problems, using slack variables
4.3 Understanding the simplex algorithm to solve maximising linear programming problems
4.4 Solving maximising linear programming problems using simplex tableaux
4.5 Using the simplex tableau method to solve maximising linear programming problems requiring integer solutions