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Approximating the solution for the multiparametric 0-1-mixed integer linear programming problem with interval data

Alejandro Crema(alejandro.crema***at***ciens.ucv.ve)
Edgar Hugo Peraza(eperaza***at***ucla.edu.ve)
Fernando Crema(fcremarm***at***hotmail.com)

Abstract: 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 de fine 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.

Keywords: Integer programming, multiparametric programming, real time.

Category 1: Integer Programming ((Mixed) Integer Linear Programming )

Citation: Escuela de ComputaciĆ³n, Facultad de Ciencias, Universidad Central de Venezuela, August 2012.

Download: [PDF]

Entry Submitted: 08/23/2012
Entry Accepted: 08/24/2012
Entry Last Modified: 08/23/2012

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