Summary: Operation Research Techniques 1
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1 Mathematically modelling
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Give the definition of solution of an optimization problem
A solution to an optimization problem is a collection of values of the decision variables. 
Give the definition of feasible region of an optimization problem
The feasible region of an optimization problem is the set of feasible solutions to the problem. 
Give the definition of unbounded problem of an optimization problem
Anoptimization problem isunbouded if for any feasible solution x, there exists another feasible solution y whose value is better than the value of x. It has nofinite optimal solution. 
Give the definition of infeasible problem of an optimization problem
A mathematical program is infeasible if there are no feasible solutions. No solution that satisfies all constraints and bounds. 
What is the proportionality assumption of linear programming?
Theassumption that when it takes 3 eggs to make 1 chocolate cake. It takes 9 eggs to make 3 chocolate cakes.
Thisassumption has to hold to be able to applylinear programming . 
What is meant with the divisibility assumption?
A variable can be infinitely divisible and still have meaning. 
When are problems called discrete or combinatorial optimization problems?
When all the variables are binary. 
What is meant with a static and a dynamic model?
In a static model the decision variables do not involve a sequence of decisions over multiple periods. In a dynamic model the decision variables do involve sequences of decisions over multiple periods. 
What is meant with deterministic and stochastic models?
In a deterministic problem the value of the objective function and whether or not the constraints are satisfied is known with certainty. Otherwise stochastic. 
What is the isocost line?
The line on which you can find an optimal solution considering a minimization problem.
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Topics related to Summary: Operation Research Techniques 1

Mathematical modelling with integers

Combinatorial optimization problems

Simple alogrithm

Duality and column generation

Complexity  Easy and hard problems

Complexity  Algorithm running time

Complexity  P and NP

Complexity  NP  completeness

Constructive heuristics  Introduction heuristics

Constructive heuristics  Greedy approach

Constructive heuristics  Probabilistic approach and adaptive search

Improvement heuristics I  Local Search, steepest descent

Improvement heuristics II  Simulated Annealing

Large neighborhood search  LNS