Linear Programming Old name for linear optimization –Linear objective functions...

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Transcript of Linear Programming Old name for linear optimization –Linear objective functions...

  • Slide 1
  • Slide 2
  • Linear Programming Old name for linear optimization Linear objective functions and constraints Optimum always at boundary of feasible domain First solution algorithm, Simplex algorithm developed by George Dantzig, 1947 What is a simplex (e.g. triangle, tetrahedron)? We will study limit design of skeletal structures as an application of LP.
  • Slide 3
  • Example (Vanderplaats, Multidiscipline Design Optimization, p. 128)
  • Slide 4
  • Solution with Matlab linprog Simplest form solves f=[-4 -1]; A=[1 -1; 1 2; -1 0; 0 -1]; b=[2 8 0 0] ; [x,obj]=linprog(f,A,b) Optimization terminated. x =4.0000 2.0000 obj =-18.0000 Matrix form
  • Slide 5
  • Problem linprog Solve the following problem using linprog and also graphically (do not use the equality constraint to reduce the number of variables).
  • Slide 6
  • Limit analysis of trusses Elastic-perfectly plastic behavior Normally, beyond yield the stress will continue to increase, so the assumption is conservative. We will see it will simplify estimating the collapse load of a truss.
  • Slide 7
  • Three bar truss example 3.1.1
  • Slide 8
  • Beyond yield Recall Member B yields first However, load can be increased until members A and C also yield
  • Slide 9
  • Lower bound theorem The Lower Bound Theorem: If a stress distribution can be found that is in equilibrium internally and balances the external loads, and also does not violate the yield conditions, these loads will be carried safely by the structure. Leads to an optimization problem with equations of equilibrium as equality constraints, and yield conditions as inequality constraints.
  • Slide 10
  • LP formulation of truss collapse load Example 3.2 Implication of lower bound theorem: Any p for which we can find ns that satisfy the equation is safe LP problem: Find loads to maximize p subject to above constraints Non-dimensionalize!
  • Slide 11
  • Non-dimensional form LP problem f=[0 0 0 -1]; A=eye(4); b=[1 1 1 1000]'; Aeq=[0.5 1 0.5 -1; sqrt(3)/2 0 -sqrt(3)/2 -1]; beq=zeros(2,1); lb=-[1 1 1 0]; x=linprog(f,A,b,Aeq,beq,lb) Optimization terminated. x =1.0000 1.0000 -0.4641 1.2679
  • Slide 12
  • Problem limit design Limit design is to the truss cross sectional areas to minimize the weight of the truss subject to a given collapse load p. Formulate the limit design of the truss in Slide 9 for given loads p as an LP and solve using linprog. Define a nominal area The non-dimensional design variables will now be the areas divided by A and the three member loads, divided by A times sigma0.