Abstract
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

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
This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective - global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.

Abstract
In this paper we present a user-centered interface for a scheduling system. The purpose of this interface is to provide graphical and interactive ways of defining a scheduling problem. To create such user interface an evaluation-centered user interaction development method was adopted: the star life cycle. The created prototype comprises the Task Module and the Scheduling Problem Module. The first one allows users to define a sequence of operations, i.e., a task. The second one enables a scheduling problem definition, which consists in a set of tasks. Both modules are equipped with a set of real time validations to assure the correct definition of the necessary data input for the scheduling module of the system. The usability evaluation allowed us to measure the ease of interaction and observe the different forms of interaction provided by each participant, namely the reactions to the real time validation mechanism.

Abstract
Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Abstract
This paper describes some developing issues for ACS based software tools to support decision making process and 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 (ACS) based algorithm performance is validated with benchmark problems available in the OR library. The obtained results were compared with the optimal (best available results in some cases) and permit to conclude about ACS efficiency and effectiveness. The ACS performance and respective statistical significance was evaluated. © 2013 Springer-Verlag Berlin Heidelberg.