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

Tris(2-Ethylhexyl) trimellitate (TOTM) a potential reference fluid for high viscosity. Part I: viscosity measurements at temperatures from (303 to 373)K and pressures up to 65MPa, using a novel vibrating-wire instrument

No literature data above atmospheric pressure could be found for the viscosity of TOTIVI. As a consequence, the present viscosity results could only be compared upon extrapolation of the vibrating wire data to 0.1 MPa. Independent viscosity measurements were performed, at atmospheric pressure, using an Ubbelohde capillary in order to compare with the vibrating wire results, extrapolated by means of the above mentioned correlation. The two data sets agree within +/- 1%, which is commensurate with the mutual uncertainty of the experimental methods. Comparisons of the literature data obtained at atmospheric pressure with the present extrapolated vibrating-wire viscosity measurements have shown an agreement within +/- 2% for temperatures up to 339 K and within +/- 3.3% for temperatures up to 368 K. (C) 2014 Elsevier B.V. All rights reserved.

Using remote experimentation in a large undergraduate course: initial findings

The use of remote labs in undergraduate courses has been reported in literature several times since the mid 90's. Nevertheless, very few articles present results about the correspondent learning gains obtained by students, and in what conditions those systems can be more efficient, thus suggesting a lack of data concerning their pedagogical effectiveness. This paper addresses such a gap by presenting some initial findings concerning the use of a remote lab (VISIR), in a large undergraduate course on Physics, with over 550 students enrolled.

Using VISIR in a large undergraduate course: preliminary assessment results

The use of remote labs in undergraduate courses has been reported in literature several times since the mid 90's. Nevertheless, very few articles present results about the learning gains obtained by students using them, especially with a large number of students, thus suggesting a lack of data concerning their pedagogical effectiveness. This paper addresses such a gap by presenting some preliminary results concerning the use of a remote laboratory, known as VISIR, in a large undergraduate course on Applied Physics, with over 500 students enrolled.

Using test infrastructures for (remote) online evaluation of the sensitivity to SEUs of FPGAs

This paper proposes an online mechanism that can evaluate the sensitivity of single event upsets (SEUs) of field programmable gate arrays (FPGAs). The online detection mechanism cyclically reads and compares the values form the external and internal configuration memories, taking into account the mask information. This remote detection method also signals any mismatch as a result of a SEU that affects both used and not-used FPGA parts, which maximizes the monitored area. By utilizing an external, Web-accessible controller that is connected to the test infrastructure, the possibility of running the same operation in a remote manner is enabled. Moreover, the need for a local memory to store the mask values is also eliminated.

Strength improvement of adhesively-bonded joints using a reverse-bent geometry

Adhesive bonding of components has become more efficient in recent years due to the developments in adhesive technology, which has resulted in higher peel and shear strengths, and also in allowable ductility up to failure. As a result, fastening and riveting methods are being progressively replaced by adhesive bonding, allowing a big step towards stronger and lighter unions. However, single-lap bonded joints still generate substantial peel and shear stress concentrations at the overlap edges that can be harmful to the structure, especially when using brittle adhesives that do not allow plasticization in these regions. In this work, a numerical and experimental study is performed to evaluate the feasibility of bending the adherends at the ends of the overlap for the strength improvement of single-lap aluminium joints bonded with a brittle and a ductile adhesive. Different combinations of joint eccentricity were tested, including absence of eccentricity, allowing the optimization of the joint. A Finite Element stress and failure analysis in ABAQUS® was also carried out to provide a better understanding of the bent configuration. Results showed a major advantage of using the proposed modification for the brittle adhesive, but the joints with the ductile adhesive were not much affected by the bending technique.

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to 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 methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.

Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms

This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.