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Abstract. In this work a new mixed integer nonlinear optimization model (MINLP) for scheduling problems without using big M constraints, based on a combination of slot representation and generalized precedence variables is proposed. The decomposition strategy for tackling its resolution consists of a sequence of LP and MILP optimization subproblems. The first one is obtained by fixing a binary feasible point in the original MINLP problem, afterwards, the latter one is generated by adding a new cut based on LP-problem optimal values to find a different binary feasible point, so as to repeat the procedure till convergence. A successful application to find the optimal scheduling in multiproduct batch plants where each order specifies the product and the amount to be manufactured as well as the promised due date and the release time is presented. The set of batches is optimally scheduled to meet the product orders as close to their due dates as possibleIn this work a new mixed integer nonlinear optimization model (MINLP) for scheduling problems without using big M constraints, based on a combination of slot representation and generalized precedence variables is proposed. The decomposition strategy for tackling its resolution consists of a sequence of LP and MILP optimization subproblems. The first one is obtained by fixing a binary feasible point in the original MINLP problem, afterwards, the latter one is generated by adding a new cut based on LP-problem optimal values to find a different binary feasible point, so as to repeat the procedure till convergence. A successful application to find the optimal scheduling in multiproduct batch plants where each order specifies the product and the amount to be manufactured as well as the promised due date and the release time is presented. The set of batches is optimally scheduled to meet the product orders as close to their due dates as possible