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.

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

Establishing guidelines for obtaining optimal cell sources to use in tendon tissue engineering strategies

Cell-based approaches in tissue engineering (TE) have been barely explored for the treatment of tendon and ligament (T/L) tissues, requiring the establishment of a widely available cell source with tenogenic potential. As T/L cells are scarce, stem cells may provide a good alternative. Understanding how resident cells behave in vitro, might be useful for recapitulating the tenogenic potential of stem cells for tendon TE applications. Therefore, we propose to isolate and characterize human T/L-derived cells (hTDCs and hLDCs) and compare their regenerative potential with stem cells from adipose tissue (hASCs) and amniotic fluid (hAFSCs)(1). T/L cells were isolated using different procedures and stem cells isolated as described elsewhere(1). Moreover, T/L cells were stimu- lated into the three mesenchymal lineages, using standard differentia- tion media. Cells were characterized for the typical stem cell markers as well as T/L related markers, namely tenascin-C, collagen I and III, decorin and scleraxis, using different complementary techniques such as real time RT-PCR, immunocytochemistry and flow cytometry. No differences were observed between T/L in gene expression and protein deposition. T/L cells were mostly positive for stem ness markers (CD73/CD90/CD105), and have the potential to differentiate towards osteogenesis, chondrogenesis and adipogenesis, demonstrated by the positive staining for AlizarinRed, SafraninO, ToluidineBlue and OilRed. hASCs and hAFSCs exhibit positive expression of all tenogenic mark- ers, although at lower levels than hTDCs and hLDCs. Nevertheless, stem cells availability is key factor in TE strategies, despite that it’s still required optimization to direct their tenogenic phenotype.

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.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

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.

Automatização e otimização do escalonamento de pessoal hospitalar

Dissertação de mestrado em Engenharia de Sistemas

Implementação de um sistema de previsão e controlo para operação otimizada de um sistema de recolha e armazenamento de água pluvial

Dissertação de mestrado integrado em Engenharia Civil

Optimization of low-cost nutritional supplementation for lignocellulosic ethanol fermentation

[Excerpt] Bioethanol from lignocellulosic materials (LCM), also called second generation bioethanol, is considered a promising alternative to first generation bioethanol. An efficient production process of lignocellulosic bioethanol involves an effective pretreatment of LCM to improve the accessibility of cellulose and thus enhance the enzymatic saccharification. One interesting approach is to use the whole slurry from treatment, since allows economical and industrial benefits: washing steps are avoided, water consumption is lower and the sugars from liquid phase can be used, increasing ethanol concentration [1]. However, during the pretreatment step some compounds (such as furans, phenolic compounds and weak acids) are produced. These compounds have an inhibitory effect on the microorganisms used for hydrolysate fermentation [2]. To overcome this, the use of a robust industrial strain together with agro-industrial by-products as nutritional supplementation was proposed to increase the ethanol productivities and yields. (...)

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Aplicação de princípios e ferramentas lean numa unidade de cirurgia de ambulatório de um hospital

Dissertação de mestrado integrado em Engenharia Industrial

Seleção de microalgas com potencial de produção de biocombustíveis

Dissertação de mestrado em Bioengenharia

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.