Abstract
Tactical production-distribution "planning models have attracted a great deal of attention in the past decades. In these models, production and distribution decisions are considered simultaneously such that the combined plans are more advantageous than the plans resolved in a hierarchical planning process. We consider a two-stage production process, where in the first stage raw materials are transformed into continuous resources that feed the discrete production of end products in the second stage. Moreover, the setup times and costs of resources depend on the sequence in which they are processed in the first stage. The minimum scheduling unit is the product family which consists of products sharing common resources and manufacturing processes. Based on different mathematical modelling approaches to the production in the first stage, we develop a sequence-oriented formulation and a product-oriented formulation, and propose decomposition-based heuristics to solve this problem efficiently. By considering these dependencies arising in practical production processes, our model can be applied to various industrial cases, such as the beverage industry or the steel industry. Computation tests on instances from an industrial application are provided at the end of the paper.

Abstract

Abstract
This paper describes an enhanced model for the Short Term Hydro Scheduling Problem, HSP, that includes the impact of operation decisions on the market prices and the possibility of adjusting the tailwater level and the generation and pumping efficiencies as a function of the flow. The solution approach uses an iterative procedure that solves in each iteration a linearized HSP problem using the linprog function of the MATLAB (R) Optimization Toolbox and that updates the value of the head to be used in the next iteration. The paper also includes results from a realistic Case Study based on the cascade of 9 hydro stations (4 of them with pumping) installed in the Portuguese section of the Douro River.

Abstract
Posicast feedback control systems are very sensitive to model uncertainty. This paper proposes the use of Particle Swarm Optimization (PSO) to auto-tune two-degrees of freedom control systems. The system considers as a pre-filter a half-cycle Posicast command shaper and a PID controller in the feedback loop. A model reference technique is proposed to track differences among model and system to be controlled, feeding a decision block which will trigger an auto-tuning optimization mechanism. Preliminary simulation results are presented showing the proposed technique effectiveness to deal with prescribed plant uncertainties.

Abstract
We address the problem of generating electricity through Underwater Kite Power Systems. For this problem, we develop an optimal control problem formulation using a continuous-time model of the kite to devise the trajectories and controls for the kite that maximize the total energy produced in a given time interval. This is an highly nonlinear problem for which the optimization is challenging. We also develop a numerical solution scheme for the optimal control problem based on direct methods and on adaptive time-mesh refinement. We report results that show that the problem can be quickly solved with a high level of accuracy when using our adaptive mesh refinement strategy. The results confirm the values of electrical power that can be produced with such device.

Abstract
Window functions are extremely useful and have become increasingly popular, allowing ranking, cumulative sums and other analytic aggregations to be computed over a highly flexible and configurable sliding window. This powerful expressiveness comes naturally at the expense of heavy computational requirements which, so far, have been addressed through optimizations around centralized approaches by works both from the industry and academia. Distribution and parallelization has the potential to improve performance, but introduces several challenges associated with data distribution that may harm data locality. In this paper, we show how data similarity can be employed across partitions during the distributed execution of these operators to improve data co-locality between instances of a Distributed Query Engine and the associated data storage nodes. Our contribution can attain network gains in the average of 3 times and it is expected to scale as the number of instances increase. In the scenario with 8 nodes, we were to able attain bandwidth and time savings of 7.3 times and 2.61 times respectively. © IFIP International Federation for Information Processing 2017.