Abstract
In the current global market organizations face uncertainties and shorter response time. In order to remain competitive many organizations adopted flexible resources capable of performing several operations with different performance capabilities. The unrelated parallel-machines makespan minimization problem (RmIiCmax) is known to be NP-hard or too complex to be solved exactly. Among the several heuristics used for solving this problem, it is possible to identify MCT (Minimum Completion Time) that allocates tasks in a random order to the minimum completion time machine. This paper proposes an ordered approach to the MCT heuristic. MOMCT (Modified Ordered Minimum Completion Time), which will order tasks in accordance to the mean difference of the completion time on each machine and the minimum completion time machine. The computational study demonstrated the improved performance of the proposed ordered approach to the MCT heuristic.

Abstract
Scheduling problems occurring in parallel machines manufacturing environments are quite usual and many different methods have been applied for solving it. These methods vary from the application of more or less simple heuristics and rules up to more complex methods, including distinct kind of metaheuristics. In this paper we discuss a fuzzy optimization method using simulated annealing (Fuzzy-SA) for solving an unrelated parallel machines manufacturing scheduling problem. To demonstrate the potential of our method we use an illustrative example of a parallel machines scheduling (PMS) problem and then we analyse it and perform statistical tests with 20 instances.

Abstract
Extended Manufacturing Environments (EMEs) are nowadays growing due to the increase on Distributed and Virtual Enterprises, which led to an emergent need to apply scheduling approaches accordingly. This can be achieved in several different ways, namely by putting forward new approaches or by trying to adapt existing ones. In this paper the adaptation of some existing scheduling methods is proposed for solving a two stage manufacturing scheduling problem, and an illustrative example is presented. Several approaches were analyses, namely through the use of the ANOV A and the Post Hoc Scheffe's test, that demonstrated the superior performance of one of the proposed methods.

Abstract
This paper addresses the developing of Learning-Assisted Intelligent Scheduling Systems that uses active learning by accumulation and interpretation of scheduling experience or even by observation of expert's decisions. The design of intelligent systems (IS) that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. The model for the proposed system will be presented.

Abstract
Meta-heuristics have been applied for a long time to the Travelling Salesman Problem (TSP) but information is still lacking in the determination of the parameters with the best performance. This paper examines the impact of the Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) parameters in the TSP. One special consideration of this paper is how the Neighborhood Structure (NS) interact with the other parameters and impacts the performance of the meta-heuristics. NS performance has been the topic of much research, with NS proposed for the best-known problems, which seem to imply that the NS influences the performance of meta-heuristics, more that other parameters. Moreover, a comparative analysis of distinct meta-heuristics is carried out to demonstrate a non-proportional increase in the performance of the NS.

Abstract
The ability to adjust itself to users' profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning and training of user modelling techniques combined with existing system components. Based on the architecture for user modelling on Intelligent and Adaptive Scheduling Systems, this paper presents an analysis of using the mentioned architecture to characterize user's behaviours and a case study comparing the employment of different user classifiers. Bayesian and Artificial Neural Networks were selected as the elements of the computational study and this paper presents a description on how to prepare them to deal with user information.