We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second stage is a task. solve master problem with mixed integer/linear programming subproblem constraint programming. As cuts, we use simple no-good cuts as well analytic develop for this application. find that LBBD computationally superior integer L-shaped method. In particul...

The flow refueling location problem (FRLP) locates p stations in order to maximize the flow volume that can be accommodated in a road network respecting the range limitations of the vehicles. This paper introduces the charging station location problem with plug-in hybrid electric vehicles (CSLP-PHEV) as a generalization of the FRLP. We consider not only the electric vehicles but also the plug-i...

The paper considers solving of linear programming problems with p-order conic constraints that are related to a certain class of stochastic optimization models with risk objective or constraints. The proposed approach is based on construction of polyhedral approximations for p-order cones, and then invoking a Benders decomposition scheme that allows for efficient solving of the approximating pr...

Maintenance of power generators is essential for reliable and efficient electricity production. Because under maintenance are typically inactive, optimal planning activities must consider the impact outages on system operation. However, in hydropower systems finding a minimum cost schedule challenging optimization problem due to uncertainty water inflows nonlinearity hydroelectricity Motivated ...

Operations Research practitioners often want to model complicated functions that are difficult encode in their underlying optimisation framework. A common approach is solve an approximate model, and then use a simulation evaluate the true objective value of one or more solutions. We propose new integrating into itself. The idea run at each incumbent solution master problem. results used guide t...

We study an adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse. A partition-based formulation is a relaxation of the original stochastic program, and we study a finitely converging algorithm in which the partition is adaptively adjusted until it yields an optimal solution. A solution guided refinement strategy is developed to refine the partition by ...