Abstract A general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through improved use large neighbourhood search heuristics within mixed-integer programming solvers. While solvers are endowed with an array heuristics, few, if any, have been designed for BD. Further, typically is limited to finding solutions BD master problem. Given lack frameworks BD, only ad hoc appr...

Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. The main idea GBD decomposing an MINLP problem into primal master problem, iteratively solved until their solutions converge. However, direct implementation time- memory-consuming....

This article considers the network design problem that searches for a minimum-cost way of installing capacities on the edges of a network in order to simultaneously route a flow from a given access point to a subset of nodes representing customers with positive demands. We first consider compact and exponential-sized MIP formulations of the problem and provide their theoretical and computationa...

We consider a problem arising in the design of green (or energy-saving) Wireless Local Area Networks (WLANs). In this context, decisions on powering-on a set of access points, via the assignment of one power level to each opened access point, and decisions on the assignment of the user terminals to the opened access points, have to be taken simultaneously. In particular, the power level assigne...

Dynamic multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving these problems, including nested Benders decomposition. In this method, recently shown to be superior to the alternatives for large problems, the problem is decomposed into a set of smaller line...

In this paper, we present an algorithm for finding utilitarian optimal solutions to Simple and Disjunctive Temporal Problems with Preferences (STPPs and DTPPs) based on Benders’ decomposition and adopting SAT techniques. In our approach, each temporal constraint is replaced by a Boolean indicator variable and the decomposed problem is solved by a tightly integrated STP solver and SAT solver. Se...