Implementing and testing branch-and-bound algorithm for the quadratic 0-1 problem on transputers

Discrete optimization problems are very difficult to solve, even if the dimention is small. For most of them the problem of finding an ε-approximate solution is already NP-hard. The branch-and-bound algorithms are the most used algorithms for solving exactly this sort of problems.

A computational study of a parallel Branch and Bound algorithm for the quadratic 0-1 programming problem on transputers

Discrete optimization problems are very difficult to solve, even if the dimantion is small. For most of them the problem of finding an ε-approximate solution is already NP-hard.

An efficient parallel implementation of a least squares problem

Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

An efficient parallel implementation of a least squares problem

Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

A new formulation of the learning problem for a neural network controller

In this paper we consider the learning problem for a class of multilayer perceptrons which is practically relevant in control systems applications. By reformulating this problem, a new criterion is developed, which reduces the number of iterations required for the learning phase.

Using creative problem solving (CPS) to improve leadership in a non-profit organization

The purpose of this study was to evaluate the effectiveness of the Creative Problem Solving (CPS) method in improving the leadership process in a non-profit organization. The research was designed around an intervention and structured in three stages (pre-consult, intervention and follow-up), with a team designated by management, in order to bring leadership cohesion to both departments of the organization and also between the board and executive management. The results, expressed in the tasks performed and in the interviews to team members, allowed us to conclude on the effectiveness of the CPS method to improve organizational leadership, by establishing a stronger relationship between departments, as well as, in the long term, between the board and executive management. These results highlight possible solutions to improve the leadership of non-profit organizations.

The influence of authentic leadership on employees analyzed through self-determination theory

Dissertação de Mestrado, Gestão de Recursos Humanos, Escola Superior de Gestão, Hotelaria e Turismo, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2015

Hans im Glück oder das Problem des Werts : Abschlussvorlesung am 15. Juni 2011 in Hannover

Abschlussvorlesung von Günter Buchholz, in welcher er sich über das Grimmsche Märchen "Hans im Glück" dem Problem des Wertes zuwendet und sich mit der Geschichte der ökonomischen Theorie befasst.

Effects of Unionization on Graduate Student Employees: Faculty-Student Relations, Academic Freedom, and Pay

In cases involving unionization of graduate student research and teaching assistants at private U.S. universities, the National Labor Relations Board has, at times, denied collective bargaining rights on the presumption that unionization would harm faculty-student relations and academic freedom. Using survey data collected from PhD students in five academic disciplines across eight public U.S. universities, the authors compare represented and non-represented graduate student employees in terms of faculty-student relations, academic freedom, and pay. Unionization does not have the presumed negative effect on student outcomes, and in some cases has a positive effect. Union-represented graduate student employees report higher levels of personal and professional support, unionized graduate student employees fare better on pay, and unionized and nonunionized students report similar perceptions of academic freedom. These findings suggest that potential harm to faculty-student relationships and academic freedom should not continue to serve as bases for the denial of collective bargaining rights to graduate student employees.

Genetic programming for the minimum time swing up and balance control acrobot problem

This work describes how genetic programming is applied to evolving controllers for the minimum time swing up and inverted balance tasks of the continuous state and action: limited torque acrobot. The best swing-up controller is able to swing the acrobot up to a position very close to the inverted ‘handstand’ position in a very short time, shorter than that of Coulom (2004), who applied the same constraints on the applied torque values, and to take only slightly longer than the approach by Lai et al. (2009) where far larger torque values were allowed. The best balance controller is able to balance the acrobot in the inverted position when starting from the balance position for the length of time used in the fitness function in all runs; furthermore, 47 out of 50 of the runs evolve controllers able to maintain the balance position for an extended period, an improvement on the balance controllers generated by Dracopoulos and Nichols (2012), which this paper is extended from. The most successful balance controller is also able to balance the acrobot when starting from a small offset from the balance position for this extended period.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.