Hasbiyati, Ihda2014-05-222014-05-222014-05-22978-602-18936-0-9wahyu sari yenihttp://repository.unri.ac.id/xmlui/handle/123456789/6321Multi-stage stochastic programming problems arise in many practical situations, such as production and manpower planning, portfolio selections and so on. This paper describes our efforts to develop a nonlinear programming algorithm for problems characterized by a large sparse set of linear constraints and a significant degree of nonlinearity in the objective function. It has been our experience that many linear programming problems are inordinately large because they are attempting to approximate, by piecewise linearization, what is essentially a nonlinear problem. It also appears that many real-life problems are such that only a small percentage of the variables are involved nonlinearly in the objective functionenmulti-stage stochastic nonlinear programsprojected Lagrangescenario analysisdecompositionUR-Proceedings