We propose a general-purpose heuristic approach combining metaheuristics and Mixed Integer Programming to find high quality solutions to the challenging singleand parallel-machine capacitated lotsizing and scheduling problem with sequence-dependent setup times and costs. Commercial solvers fail to solve even medium-sized instances of this NP-hard problem, therefore heuristics are required to fi...

This paper deals with the k-way ratio cut hypergraph partitioning utilizing the Mixed discrete continuous variant of the Bayesian Optimization Algorithm (mBOA). We have tested our algorithm on three partitioning taxonomies: recursive minimum ratio cut, multi-way minimum ratio cut and recursive minimum cut bisection. We have also derived a new approach for modeling of Boolean functions using bin...

A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization). The genetic algorithms which result from the meta-evolution...

Optimization problems attempt to find out the minimum or maximum of a certain function (usually referred to as the cost function). The cost function can either be continuous or discrete. Discrete optimization problems arise, when the variables occurring in the optimization function can take only a finite number of discrete values and also subject to constraint conditions. In continuous optimiza...

Model-based testing relies on the use of behavior models to automatically generate sequences of inputs and expected outputs. These sequences can be used as test cases to the end of both validating the model and also verifying an actual system. In the automotive domain many systems are reactive and exhibit continuous as well as discrete dynamics. This leads to an explosion of the model state spa...

Large-scale computational models have become common tools for analyzing complex manmade systems. However, when coupled with optimization or uncertainty quantification methods in order to conduct extensive model exploration and analysis, the computational expense quickly becomes intractable. Furthermore, these models may have both continuous and discrete parameters. One common approach to mitiga...

Dense image matching is a fundamental lowlevel problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithm...

This paper concerns the optimal control of modular hybrid systems synchronized by shared variables. Instead of synchronizing the discrete dynamics of the system into one global mode before optimization, Constraint Programming (CP) is used to model the discrete dynamics of each modular system separately. Integrated in the CP solver are also classic Operations Research (OR) models in the form of ...

—The do-not-exceed (DNE) limit method was proposed to accommodate more variable renewable generation (VRG) securely. However, the lack of involving discrete recourse control precludes this method from gaining more flexibility for better VRG integration. This letter formulates a multi-period DNE limit model considering continuous and discrete recourse controls. This model belongs to two-stage ro...