Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Project management under uncertainty: a study on solution methods

Project Management involves onetime endeavors that demand for getting it right the first time. On the other hand, project scheduling, being one of the most modeled project management process stages, still faces a wide gap from theory to practice. Demanding computational models and their consequent call for simplification, divert the implementation of such models in project management tools from the actual day to day project management process. Special focus is being made to the robustness of the generated project schedules facing the omnipresence of uncertainty. An "easy" way out is to add, more or less cleverly calculated, time buffers that always result in project duration increase and correspondingly, in cost. A better approach to deal with uncertainty seems to be to explore slack that might be present in a given project schedule, a fortiori when a non-optimal schedule is used. The combination of such approach to recent advances in modeling resource allocation and scheduling techniques to cope with the increasing flexibility in resources, as can be expressed in "Flexible Resource Constraint Project Scheduling Problem" (FRCPSP) formulations, should be a promising line of research to generate more adequate project management tools. In reality, this approach has been frequently used, by project managers in an ad-hoc way.

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.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Modern optimization in earthwork construction

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

Audit quality: a resource-based perspective

This study considers the Certified Public Accountants (CPA) partners of the audit firms as the core producers of these knowledge-based organisations and investigates the effects of audit firm-level of CPA partners’ experience in audit quality. Audit quality was measured through the audit clients’ earnings quality. The main findings suggested that the audit firm-level of CPA partners’ general experience is positively associated with audit quality. Also, the audit firm-level of CPA partners’ experience acquired with prior audit firms significantly improves audit quality. Concerning the effects of audit firm-level of CPA partners’ experience acquired in the current audit firm in audit quality, results were statistically insignificant. Finally, we found that changes in audit firm-level of CPA partners’ general experience also influence audit quality. We verified that the more positive the change in the audit firm stock of CPA partners’ general experience, the higher is audit quality. We interpret this finding as revealing that the year-to-year changes in audit firm’s stock of CPA partners’ general experience is the mechanism allowing these firms to achieve and maintain the critical mass of this resource and reach higher performance levels. We further showed that our main results were not driven by the engagement partner experience.

A new perspective of students allocation satisfaction in engineering courses in Portugal

Publicado em "AIP Conference Proceedings" Vol. 1648

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

Spawning habitat selection by Octopus vulgaris: new insights for a more effective management of this resource

The selection of spawning habitat of a population of Octopus vulgaris that is subject to a small-scale exploitation was studied in the Cíes Islands within the National Park of the Atlantic Islands of Galicia (NW Spain). The technique used was visual censuses by scuba diving. We conducted 93 visual censuses from April 2012 to April 2014. The total swept area was 123.69 ha. Habitat features (season, depth, zone, bottom temperature, swept area, bottom substrate type, and creels fishing impact) were evaluated as predictors of the presence/absence of spawning dens using GAM models. O. vulgaris has a noteworthy preference for spawning in areas with hard bottom substrate and moderate depth (approximately 20 m). The higher density of spawning dens (1.08ha−1) was found in a surveyed area of 50.14ha located in the northeastern part of the northern Cíes Island. We propose to protect the area comprised from Punta Escodelo to Punta Ferreiro between 5 and 30 m depth. This area has a surface of 158 ha equivalent to 5.98% of the total marine area of the Cíes islands. The strengths and weaknesses of a management strategy based on the protection of the species’ spawning habitat are discussed.

Macroscopic and histopathological study of the placenta: an essential resource in litigation processes

The pathological study of the placenta is of upmost importance in cases of unexplained fetal/perinatal loss and often these carry litigation implications. Integrating pathological findings and the underlying pathophysiological processes, leading to placental lesions, is fundamental for the evaluation of poor fetal and perinatal outcomes and to distinguish from cases of true negligence.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.