Abstract
Nowadays, organizations are often faced with the development of complex and innovative projects. This type of projects often involves performing tasks which are subject to failure. Thus, in many such projects several possible alternative actions are considered and performed simultaneously. Each alternative is characterized by cost, duration, and probability of technical success. The cost of each alternative is paid at the beginning of the alternative and the project payoff is obtained whenever an alternative has been completed successfully. For this problem one wishes to find the optimal schedule, i.e., the starting time of each alternative, such that the expected net present value is maximized. This problem has been recently proposed in Ranjbar (Int Trans Oper Res 20(2):251-266, 2013), where a branch-and-bound approach is reported. Since the problem is NP-Hard, here we propose to solve the problem using genetic algorithms.

Abstract
The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data. © 2015 IEEE.

Abstract
Human consciousness is a target of research in multiple fields of knowledge, that presents it as an important characteristic to better handle complex and diverse situations. Artificial consciousness models have arose, together with theories that attempt to model what we understand about consciousness, in a way that could be implemented an artificial conscious being. The main motivations to study artificial consciousness are related to the creation of agents more similar to human beings, in order to build more efficient machines. This paper presents an experiment using the Global Workspace Theory and the LIDA Model to build a conscious mobile robot in a virtual environment, using the LIDA framework as a implementation of the LIDA Model. The main objective is to evaluate if it is possible to use conscience as implemented by the LIDA framework to simplify decision making processes during navigation of a mobile robot subject to interaction with people, as part of a cicerone robot development.

Abstract
This paper describes a control technique for enhancing the stable operation of distributed generation (DG) units based on renewable energy sources, during islanding and grid-connected modes. The Passivity-based control technique is considered to analyze the dynamic and steady-state behaviors of DG units during integration and power sharing with loads and/or power grid, which is an appropriate tool to analyze and define a stable operating condition for DG units in microgrid technology. The compensation of instantaneous variations in the reference current components of DG units in ac-side, and dc-link voltage variations in dc-side of interfaced converters, are considered properly in the control loop of DG units, which is the main contribution and novelty of this control technique over other control strategies. By using the proposed control technique, DG units can provide the continuous injection of active power from DG sources to the local loads and/or utility grid. Moreover, by setting appropriate reference current components in the control loop of DG units, reactive power and harmonic current components of loads can be supplied during the islanding and grid-connected modes with a fast dynamic response. Simulation results confirm the performance of the control scheme within the microgrid during dynamic and steady-state operating conditions.

Abstract
Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.

Abstract
The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.