We combine mixed integer linear programming (MILP) and constraint programming (CP) to solve planning and scheduling problems. Tasks are allocated to facilities using MILP and scheduled using CP, and the two are linked via logic-based Benders decomposition. Tasks assigned to a facility may run in parallel subject to resource constraints (cumulative scheduling). We solve minimum cost problems, as...

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach for resource loading under uncertainty that minimises the expected costs. The approach uses an MIL...

We reformulate branch-and-bound feature selection employing L1 or particular Lp metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.

We give a new mixed integer linear programming (MILP) formulation for Maximum Degree Bounded Connected Subgraph Problem (MDBCSP). The proposed MILP formulation is the first in literature with polynomial number of constraints. Therefore, it will be possible to solve optimally much more instances before in a reasonable time.

Channel routing is a type of problems arising in the detailed routing phase of VLSI physical design automation as well as in the design of printed circuit boards (PCBs). It has been known that channel routing problems can be formulated as constraint programming (CP) problems and thus CP solvers can be used to find solutions. In this article, we present a mixed integer linear programming (MILP) ...

Plain vanilla K-means clustering is prone to produce unbalanced clusters and suffers from outlier sensitivity. To mitigate both shortcomings, we formulate a joint outlier-detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset. We cast this problem as a mixed...

In this paper the problem of finding the sparsest (i.e., minimum cardinality) critical k-tuple including one arbitrarily specified measurement is considered. The solution to this problem can be used to identify weak points in the measurement set, or aid the placement of new meters. The critical k-tuple problem is a combinatorial generalization of the critical measurement calculation problem. Us...

Upgradeability problems are a critical issue in modern operating systems. The problem consists in finding the “best” solution according to some criteria, to install, remove or upgrade packages in a given installation. This is a difficult problem: the complexity of the upgradeability problem is NP complete and modern OS contain a huge number of packages (often more than 20 000 packages in a Linu...

The paper focuses on the solution of the minimum loss reconfiguration problem of distribution networks, including embedded generation, by means of a mixed integer linear programming (MILP) model. The proposed model takes into account typical operating constraints of distribution networks (radial configuration, branch ampacity limits and bus voltage requirements). The accuracy of the results and...