A hybrid genetic algorithm for the multi-mode resource-constrained project scheduling

A construction project is a group of discernible tasks or activities that are conduct-ed in a coordinated effort to accomplish one or more objectives. Construction projects re-quire varying levels of cost, time and other resources. To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource con-strained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability for the entire project such as manpower and machines. This paper presents a hybrid genetic algorithm for the multi-mode resource-constrained pro-ject scheduling problem, in which multiple execution modes are available for each of the ac-tivities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme. It is evaluated the quality of the schedules and presents detailed comparative computational re-sults for the MRCPSP, which reveal that this approach is a competitive algorithm.

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Intra-hospital organ and tissue donation coordination project: cost-effectiveness and social benefits

OBJECTIVE To evaluate the viability of a professional specialist in intra-hospital committees of organ and tissue donation for transplantation. METHODS Epidemiological, retrospective and cross-sectional study (2003-2011 and 2008-2012), which was performed using organ donation for transplants data in the state of Sao Paulo, Southeastern Brazil. Nine hospitals were evaluated (hospitals 1 to 9). Logistic regression was used to evaluate the differences in the number of brain death referrals and actual donors (dependent variables) after the professional specialist started work (independent variable) at the intra-hospital committee of organ and tissue donation for transplantation. To evaluate the hospital invoicing, the hourly wage of the doctor and registered nurse, according to the legislation of the Consolidation of Labor Laws, were calculated, as were the investment return and the time elapsed to do so. RESULTS Following the nursing specialist commencement on the committee, brain death referrals and the number of actual donors increased at hospital 2 (4.17 and 1.52, respectively). At hospital 7, the number of actual donors also increased from 0.005 to 1.54. In addition, after the nurse started working, hospital revenues increased by 190.0% (ranging 40.0% to 1.955%). The monthly cost for the nurse working 20 hours was US$397.97 while the doctor would cost US$3,526.67. The return on investment was 275% over the short term (0.36 years). CONCLUSIONS This paper showed that including a professional specialist in intra-hospital committees for organ and tissue donation for transplantation proved to be cost-effective. Further economic research in the area could contribute to the efficient public policy implementation of this organ and tissue harvesting model.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. 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.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. 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

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is 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.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

TRAILER project overview: tagging, recognition and acknowledgment of informal learning experiences

The evolution of new technology and its increasing use, have for some years been making the existence of informal learning more and more transparent, especially among young and older adults in both Higher Education and workplace contexts. However, the nature of formal and non-formal, course-based, approaches to learning has made it hard to accommodate these informal processes satisfactorily, and although technology bring us near to the solution, it has not yet achieved. TRAILER project aims to address this problem by developing a tool for the management of competences and skills acquired through informal learning experiences, both from the perspective of the user and the institution or company. This paper describes the research and development main lines of this project.

Electrorheology study of a series of LC cyanobiphenyls: Experimental and theoretical treatment

In this work we study the electro-rheological behaviour of a series of four liquid crystal (LC) cyanobiphenyls with a number of carbon atoms in the alkyl group, ranging from five to eight (5CB–8CB). We present the flow curves for different temperatures and under the influence of an external electric field, ranging from 0 to 3 kV/mm, and the viscosity as a function of the temperature, for the same values of electric field, obtained for different shear rates. Theoretical interpretation of the observed behaviours is proposed in the framework of the continuum theory of Leslie–Ericksen for low molecular weight nematic LCs. In our analysis, the director alignment angle is only a function of the ratio between the shear rate and the square of the electric field – boundary conditions are neglected. By fitting the theoretical model to the experimental data, we are able to determine some viscosity coefficients and the dielectric anisotropy as a function of temperature. To interpret the behaviour of the flow curves near the nematic–isotropic transitions, we apply the continuum theory of Olmsted–Goldbart, which extends the theory of Leslie–Ericksen to the case where the degree of alignment of the LC molecules can also vary.

The portuguese pharmaceutial market in the near future - A time series exploration approach

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

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.

A promoção da autonomia numa sala do movimento da escola moderna

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Integração energética da unidade 2100 da Fábrica de Óleos Base da Refinaria de Matosinhos

Até 2020, a Europa terá de reduzir 20% das suas emissões de gases com efeito de estufa, 20% da produção de energia terá de ser proveniente de fontes renováveis e a eficiência energética deverá aumentar 20%. Estas são as metas apresentadas pela União Europeia, que ficaram conhecidas por 20/20/20 [1]. A Refinaria de Matosinhosé um complexo industrial que opera no sector da refinação e que apresenta preocupações ao nível da eficiência energética e dos aspectos ambientais subjacentes. No âmbito da racionalização energética das refinarias, a Galp Energia tem vindo a implementar um conjunto de medidas, adoptando as melhores tecnologias disponíveis com o objectivo de diminuir os consumos de energia, promover a eficiência energética e reduzir as emissões de dióxido de carbono. Para ir de encontro a estas medidas foi elaborado um estudo comparativo que permitiu à empresa definir as medidas consideradas prioritárias. Uma solução encontrada visa a execução de projectos que não requerem investimento e que têm acções imediatas, tais como o aumento da eficiência energética das fornalhas [1]. Este trabalho realizado na Galp Energia S.A. teve como objectivo principal a optimização energética da Unidade de Desalfatação do Propano da Fábrica de Óleos Base. Esta optimização baseou-se no aproveitamento energético da corrente de fundo da coluna de rectificação T2003C com uma potência calorífica de 2,79 Gcal/h. Após levantamento de todas as variáveis do processo relativas a esta unidade, especialmente a potência calorífica das correntes envolvidas chegou-se á conclusão que a fornalha H2101 poderá ser substituída por dois permutadores, reduzindo desta forma os consumos energéticos. Pois a corrente de fundo da coluna T2003 com uma potência calorífica 2,79 Gcal/h poderá permutar calor com a corrente da mistura asfalto com propano, fazendo com que esta atinja temperatura superior à obtida com a fornalha em funcionamento. A análise económica ao consumo e respectivo custo do fuelóleo na fornalha para o período de um ano foi realizada, sendo o seu custo de combustível de 611.396,00 €. O valor da aquisição dos permutadores é 86.355,97€, sendo rentável a alteração proposta neste projecto.

Aplicação ao ensino médico do método de aprendizagem por problemas (APP), 2ª Parte: A experiência da disciplina de Fisiopatologia entre 1992 e 1995

Educação Médica. 1997;8 (3): 156-173