Normal 0 21 MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabela normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} Linear and nonlinear programming branches of applied mathematics have helped managers in business process management, allowing a decision to be simulated and analyzed extensively prior to its practical implementation. There are several applications in classic literature to solve such problems. More recently, genetic algorithms were available to provide efficient solutions for linear programming and nonlinear problems, which do not demand any requirement on the differentiability of functions involved. The research objective was to test the use of genetic algorithms to solve linear and nonlinear programming problems applied to agro-industry management. Two problem examples were solved. The first one was related to solution of integer linear programming problem and the second example dealt with a problem of nonlinear programming both applied in the agro-industry activities . The results can be considered good providing solutions identical to those obtained by using the well known Excel Solver, which has limitations on the number of variables and on functions continuity involved in nonlinear programming problems.
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