Abstract
This paper explores a technique denoted LASCA to solve large scale optimization problems with metaheuristics by reducing the search space dimension with autoassociative neural networks. The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space. A metaheuristic then evolves in the latter, having its objective function assessed in the original space. The technique is illustrated with an application of an Evolutionary Particle Swarm Optimization (EPSO) algorithm to four benchmarking unconstrained optimization functions and to a wind-hydro constrained coordination problem. The new technique allows an improvement in the quality of the solutions attained.

Abstract

Abstract
The two-dimensional cutting stock problem (2DCSP) consists in the minimization of the number of plates used to cut a set of items. In industry, typically, an instance of this problem is considered at the beginning of each planning time period, what may result in solutions of poor quality, that is, excessive waste, when a set of planning periods is considered. To deal with this issue, we consider an integrated problem, in which the 2DCSP is extended from the solution in only a single production planning period to a solution in a set of production planning periods. The main difference of the approach in this work and the ones in the literature is to allow sufficiently large residual plates (leftovers) to be stored and cut in a subsequent period of the planning horizon, which may further help in the minimization of the waste. We propose two integrated integer programming models to optimize the combined two-dimensional cutting stock and lot-sizing problems, minimizing the total cost, which includes material, waste and storage costs. Two heuristics based on the industrial practice to solve the problem were also presented. Computational results for the proposed models and for the heuristics are presented and discussed.

Abstract
We address two two-dimensional bin packing problems where the bins are rectangular and have the same size. The items are also rectangular and all of them must be packed with the objective of minimizing the number of bins. In the first problem, the two-stage problem, the items must be packed in levels. In the second problem, the restricted 3-stage problem, items can be grouped in stacks which are packed in levels. We propose a new decomposition model where subproblems are associated with the item that initializes a bin. The decomposition is solved by a heuristic which combines (perturbed) column generation, local search, beam branch-and-price, and the use of a general purpose mixed integer programming solver. This approach is closely related with SearchCol, a framework for solving integer programming / combinatorial optimization decomposition models. Computational results with 500 instances from the literature show that the proposed hybrid heuristic is efficient in obtaining high quality solutions. It uses more 8 and 17 bins than the 7364 and 7340 bins of a compact model from the literature for the 2 and 3-stage problems, respectively, while the sum of the time spent for all instances is 35% and 58% of the time spent by the compact model.

Abstract
This work addresses the joint scheduling of continuous caster and hot strip mill processes in the steel industry. Traditionally, slab yards are used to decouple these two stages. However, the rising importance of energy costs and reduced logistic effort gives motivation for a combined scheduling. For each of the processes, a mixed-integer linear optimisation model based on the block planning principle is presented. This approach develops production schedules that take technological sequences of steel grades and milling programmes into account. We consider the integrated steel plant of an international steel company as a case study. Numerical results demonstrate the practicability of this approach under experimental conditions, which reflect typical settings from an industrial application in the steel industry.

Abstract
Background: Hydroxyethyl starch (HES) is one of the most used colloids for intravascular volume replacement during anesthesia. Aim: To investigate the existence of a chemical interaction between HES and the anesthetic propofol by in vitro propofol dosing, computational docking, and examination of a complex between propofol and HES by infrared (IR), ultraviolet (UV), and H-1 and C-13 nuclear magnetic resonance (NMR) spectroscopy. Methods: Ten samples with human plasma mixed with HES or lactated Ringers (n = 5 for each fluid) were prepared, and the propofol free fraction was quantified until 50 min, using gas chromatography-mass spectrometry. The docking study was performed between HES and propofol and compared with controls. The binding affinities between HES and the small molecules were evaluated by binding free energy approximation (Delta Gb, kJ mol(-1)). The IR, UV, and NMR spectra were measured for propofol, HES, and a mixture of both obtained by the kneading method. Results: Propofol concentrations were significantly lower in the HES samples than in the LR samples (p = .021). The spectroscopic characterization of propofol combined with HES revealed differences in spectra and docking studies reinforced a potential interaction between propofol and HES. Conclusions: Propofol and HES form a complex with different physical-bio-chemical behavior than the single drugs, which may be an important drug interaction. Further studies should evaluate its clinical effects.