Robust optimization is based on the assumption that uncertain data has a convex set as well finite termed uncertainty. The discussion starts with determining robust counterpart, which accomplished by assuming indeterminate in form of boxes, intervals, box-intervals, ellipses, or polyhedra. In this study, counterpart characterized box-interval uncertainty set. formulation also associated master ...

In this work, we present an algorithmic framework based on Benders decomposition for the Capacitated p-Cable Trench Problem with Covering. We show that our approach can be applied to most variants of the Cable Trench Problem (CTP) that have been considered in the literature. The proposed algorithm is augmented with a stabilization procedure to accelerate the convergence of the cut loop and with...

We study server scheduling in multiclass service systems under stochastic uncertainty in the customer arrival volumes. Common practice in such systems is to first identify staffing levels, and then determine schedules for the servers that cover these targets. We propose a new stochastic integer programming model that integrates these two decisions, which can yield lower scheduling costs by expl...

Logic-based Benders decomposition can combine mixed integer programming and constraint programming to solve planning and scheduling problems much faster than either method alone. We find that a similar technique can be beneficial for solving pure scheduling problems as the problem size scales up. We solve single-facility non-preemptive scheduling problems with time windows and long time horizon...

This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage...

We address a location-allocation problem that requires deciding the location of a set of facilities, the allocation of customers to those facilities under facility capacity constraints, and the allocation of the customers to trucks at those facilities under per truck traveldistance constraints. We present a hybrid approach that combines integer programming and constraint programming using logic...