Metop-A generator for single top production via FCNC interactions

We present a generator for single top-quark production via flavour-changing neutral currents. The MEtop event generator allows for Next-to-Leading-Order direct top production pp -> t and Leading-Order production of several other single top processes. A few packages with definite sets of dimension six operators are available. We discuss how to improve the bounds on the effective operators and how well new physics can be probed with each set of independent dimension six operators.

Scheduling of a hydro producer considering head-dependency, price scenarios and risk-aversion

In this paper, a mixed-integer quadratic programming approach is proposed for the short-term hydro scheduling problem, considering head-dependency, discontinuous operating regions and discharge ramping constraints. As new contributions to earlier studies, market uncertainty is introduced in the model via price scenarios, and risk aversion is also incorporated by limiting the volatility of the expected profit through the conditional value-at-risk. Our approach has been applied successfully to solve a case Study based on one of the main Portuguese cascaded hydro systems, requiring a negligible computational time.

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

An optimization approach for the job shop scheduling problem

This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.

A green integrated process for biodiesel production

Dissertation presented at Faculdade de Ciências e Tecnologia from Universidade Nova de Lisboa to obtain the degree of Master in Chemical and Biochemical Engineering

Production of recombinant protein hLIF in static and dynamic systems of animal cell culture

Thesis for the Degree of Master of Science in Biotechnology Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Afetação de recursos em sistemas de produção

O problema do escalonamento, por ser um dos factores fundamentais na tomada de decisão para uma boa gestão das operações, tem sido alvo de um amplo estudo, tanto na sua componente teórica como na sua componente prática. A importância de um escalonamento correto das operações é preponderante, quando as pequenas diferenças, em termos de tempos de produção, podem ter um grande impacto na competitividade da organização. Em muitas unidades produtivas, existem máquinas capazes de realizar as mesmas operações com diferentes desempenhos. Isto pode dever-se à necessidade de flexibilizar os recursos ou mesmo a uma atualização da capacidade produtiva. Embora os problemas de máquinas diferentes em paralelo tenham sido alvo de um vasto estudo, muitos deles não são passíveis de ser resolvidos através de métodos exatos. O problema de minimização do makespan (Rm||Cmax), é NP-hard, sendo habitualmente abordado através de heurísticas. Entre as heurísticas utilizadas em problemas de minimização do makespan em máquinas diferentes em paralelo, é possível identificar duas filosofias de afectação: a que utiliza os tempos de processamento para alocar as tarefas e a que utiliza as datas de conclusão. Nesta dissertação, pretende-se dar uma contribuição para a resolução do problema de afectação de recursos em sistemas de produção. Para tal, foram propostas as heurísticas OMTC 3 e Suffrage One. A contribuição consiste na proposta de versões híbridas e modificadas das heurística MCT e Suffrage, uma vez identificadas várias características que podem limitar o seu desempenho, como o facto da heurística MCT alocar as tarefas numa ordem aleatória ou a heurística Suffrage alocar mais que uma tarefa por iteração. Finalmente, procedeu-se à realização de testes computacionais, para avaliar o desempenho das heurísticas propostas. Os testes realizados permitiram concluir que a heurística OMTC 3 apresentou um melhor desempenho que a heurística MCT.

An ontology to support evolvable production systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Recycling of pultrusion production waste into innovative concrete-polymer composite solutions

In this study, the added value resultant from the incorporation of pultrusion production waste into polymer based concretes was assessed. For this purpose, different types of thermoset composite scrap material, proceeding from GFRP pultrusion manufacturing process, were mechanical shredded and milled into a fibrous-powdered material. Resultant GFRP recyclates, with two different size gradings, were added to polyester based mortars as fine aggregate and filler replacements, at various load contents between 4% up to 12% in weight of total mass. Flexural and compressive loading capacities were evaluated and found better than those of unmodified polymer mortars. Obtained results highlight the high potential of recycled GFRP pultrusion waste materials as efficient and sustainable admixtures for concrete and mortar-polymer composites, constituting an emergent waste management solution.

Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Pliocene-quaternary upwelling in the SoutheasternAtlantic may reflect changes in water mass production

Proceedings of tile 1" R.C.A.N.S. Congress, Lisboa, October 1992

Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Multi-agent system for distributed manufacturing scheduling with genetic algorithms and tabu search

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.