Stochastic Planning and Scheduling with Logic-Based Benders Decomposition

TL;DR

This paper demonstrates that logic-based Benders decomposition significantly outperforms the integer L-shaped method in solving two-stage stochastic planning and scheduling problems, enabling larger instances to be tackled efficiently.

Contribution

It introduces the first application of LBBD to stochastic optimization with scheduling second-stage problems and compares its performance to the integer L-shaped method.

Findings

LBBD is computationally superior to the integer L-shaped method.

A branch-and-check LBBD variant is several orders of magnitude faster.

LBBD enables solving larger stochastic scheduling instances.

Abstract

We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second-stage is a scheduling task. We solve the master problem with mixed integer/linear programming and the subproblem with constraint programming. As Benders cuts, we use simple nogood cuts as well as analytical logic-based cuts we develop for this application. We find that LBBD is computationally superior to the integer L-shaped method, with a branch-and-check variant of LBBD faster by several orders of magnitude, allowing significantly larger instances to be solved. This is due primarily to computational overhead incurred by the integer L-shaped method while generating classical Benders cuts from a continuous relaxation of an integer programming subproblem. To our knowledge, this is the first application of LBBD to two-stage stochastic optimization with a scheduling second-stage problem, and the first comparison of LBBD with the integer \mbox{L-shaped} method. The results suggest that LBBD could be a promising approach to other stochastic and robust optimization problems with integer or combinatorial recourse.