In this paper we present algorithms to approximate the solution for the multiparametric 0-1-mixed integer linear programming problem relative to the objective function. We consider the uncertainty for the parameters that define the cost vector corresponding to a subset of 0-1-variables by assuming that each parameter belongs to a known interval. We suppose that we have enough time to obtain an epsilon-optimal multiparametric solution. Then, when the true cost vector becomes known we can obtain an epsilon-optimal solution quickly. Our algorithms work by solving an appropiate finite sequence of nonparametric problems in such a manner that the solutions of the problems in the sequence provide us with an epsilon-optimal multiparametric solution.
Escuela de Computación, Facultad de Ciencias, Universidad Central de Venezuela, August 2012.
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