A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.

The impact of poor power quality in solar photovoltaic microgeneration: technical problems and losses of revenue

This paper presents a critical and quantitative analysis of the influence of the Power Quality in grid connected solar photovoltaic microgeneration installations. First are introduced the main regulations and legislation related with the solar photovoltaic microgeneration, in Portugal and Europe. Next are presented Power Quality monitoring results obtained from two residential solar photovoltaic installations located in the north of Portugal, and is explained how the Power Quality events affect the operation of these installations. Afterwards, it is described a methodology to estimate the energy production losses and the impact in the revenue caused by the abnormal operation of the electrical installation. This is done by comparing the amount of energy that was injected into the power grid with the theoretical value of energy that could be injected in normal conditions. The performed analysis shows that Power Quality severally affects the solar photovoltaic installations operation. The losses of revenue in the two monitored installations M1 and M2 are estimated in about 27% and 22%, respectively.

Project management: evaluation of the problems in the portuguese construction industry

During recent decades it has been possible to identify several problems in construction industry project management, related with to systematic failures in terms of fulfilling its schedule, cost and quality targets, which highlight a need for an evaluation of the factors that may cause these failures. Therefore, it is important to understand how project managers plan the projects, so that the performance and the results can be improved. However, it is important to understand if other areas beyond cost and time management that are mentioned on several studies as the most critical areas, receive the necessary attention from construction project managers. Despite the cost and time are the most sensitive areas/fields, there are several other factors that may lead to project failure. This study aims at understand the reasons that may cause the deviation in terms of cost, time and quality, from the project management point of view, looking at the knowledge areas mentioned by PMI (Project Management Institute).

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Project scheduling and cost minimization with multiple availability constrained resources under stochastic conditions

The authors propose a mathematical model to minimize the project total cost where there are multiple resources constrained by maximum availability. They assume the resources as renewable and the activities can use any subset of resources requiring any quantity from a limited real interval. The stochastic nature is inferred by means of a stochastic work content defined per resource within an activity and following a known distribution and the total cost is the sum of the resource allocation cost with the tardiness cost or earliness bonus in case the project finishes after or before the due date, respectively. The model was computationally implemented relying upon an interchange of two global optimization metaheuristics – the electromagnetism-like mechanism and the evolutionary strategies. Two experiments were conducted testing the implementation to projects with single and multiple resources, and with or without maximum availability constraints. The set of collected results shows good behavior in general and provide a tool to further assist project manager decision making in the planning phase.

Emotional and behavioral problems in preschoolers: risk factors and assessment Issues

Tese de Doutoramento em Psicologia Aplicada.

A civic biology : presented in problems / by George William Hunter.

1914

Optimal scheduling with uncertainty in the numerical data on the basis of a stability analysis

Scheduling, job shop, uncertainty, mixed (disjunctive) graph, stability analysis

On boundary value problems for Willmore surfaces and Hartree-Fock theory of pseudo-relativistic atoms

Magdeburg, Univ., Fak. für Mathematik, kumulative Habil.-Schr., 2011