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.

Abstract

Abstract
This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 yr time-frame.

Abstract
In this paper a Cooperation Mechanism for Distributed Scheduling based on Bees based Computing is proposed. Where multiple self-interested agents can reach agreement over the exchange of operations on cooperative resources. Agents must collaborate to improve their local solutions and the global schedule. The proposed cooperation mechanism is able to analyze the scheduling plan generated by the Resource Agents and refine it by idle times reducing taking advantage from cooperative and the self-organized behavior of Artificial Bee Colony technique. The computational study allows concluding about statistical evidence that the cooperation mechanism influences significantly the overall system performance.

Abstract
With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.