Abstract
Meta-Heuristics (MH) are the most used optimization techniques to approach Complex Combinatorial Problems (COPs). Their ability to move beyond the local optimums make them an especially attractive choice to solve complex computational problems, such as most scheduling problems. However, the knowledge of what Meta-Heuristics perform better in certain problems is based on experiments. Classic MH, as the Simulated Annealing (SA) has been deeply studied, but newer MH, as the Discrete Artificial Bee Colony (DABC) still need to be examined in more detail. In this paper DABC has been compared with SA in 30 academic benchmark instances of the weighted tardiness problem (1 parallel to Sigma w(j)T(j)). Both MH parameters were fine-tuned with Taguchi Experiments. In the computational study DABC performed better and the subsequent statistical study demonstrated that DABC is more prone to find near-optimum solutions. On the other hand SA appeared to be more efficient.

Abstract
Real world optimization problems like Scheduling are generally complex, large scaled, and constrained in nature. Thereby, classical operational research methods are often inadequate to efficiently solve them. Metaheuristics (MH) are used to obtain near-optimal solutions in an efficient way, but have different numerical and/or categorical parameters which make the tuning process a very time-consuming and tedious task. Learning methods can be used to aid with the parameter tuning process. Racing techniques have been used to evaluate, in a refined and efficient way, a set of candidates and discard those that appear to be less promising during the evaluation process. Case-based Reasoning (CBR) aims to solve new problems by using information about solutions to previous similar problems. A novel Racing+CBR approach is proposed and brings together the better of the two techniques. A computational study for the resolution of the scheduling problem is presented, concluding about the effectiveness of the proposed approach.

Abstract
This paper presents the specification of an architecture for self-organizing scheduling systems. The proposed architecture uses learning by observing the experts and interpretation of scheduling experience. The design of intelligent systems 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. In this work, different areas as Intelligent and Adaptive Human-Machine Interfaces, Metacognition and Learning from Observation, Self-managed Systems, amongst others, are joint together resulting in a global fully integrated architecture for self-organizing scheduling systems.

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