An improved L-shaped method for two-stage convex 0–1 mixed integer nonlinear stochastic programs

•Algorithm for convex nonlinear stochastic programs with mixed-integer recourse.•Combination of Lagrangean cuts and (strengthened) Benders cuts in L-shaped method.•Application to a planning and a batch plant design problem under uncertainty.•The proposed algorithm outperforms the commercial solvers...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 112; pp. 165 - 179
Main Authors: Li, Can, Grossmann, Ignacio E.
Format: Journal Article
Language:English
Published: Elsevier Ltd 06.04.2018
Subjects:
Convex MINLP
Integer recourse
L-shaped method
Stochastic programming
Convex MINLP
Integer recourse
Stochastic programming
L-shaped method
ISSN:0098-1354, 1873-4375
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Algorithm for convex nonlinear stochastic programs with mixed-integer recourse.•Combination of Lagrangean cuts and (strengthened) Benders cuts in L-shaped method.•Application to a planning and a batch plant design problem under uncertainty.•The proposed algorithm outperforms the commercial solvers for large problems. In this paper, we propose an improved L-shaped method to solve large-scale two-stage convex 0–1 mixed-integer nonlinear stochastic programs with mixed-integer variables in both first and second stage decisions and with relatively complete recourse. To address the difficulties in solving large problems, we propose a Benders-like decomposition algorithm that includes both (strengthened) Benders cuts and Lagrangean cuts in the Benders master problem. The proposed algorithm is applied to solve a batch plant design problem under demand uncertainty, and a planning problem under demand and price uncertainty. It is shown that the proposed algorithm outperforms the commercial solvers, DICOPT, SBB, Alpha-ECP, and BARON, for the problems with a large number of scenarios. Also, although the proposed algorithm cannot close the duality gap, it is proved that it can yield a lower bound that is at least as tight as the one from Lagrangean decomposition.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2018.01.017