Abstract
Scheduling continues to play an important role in manufacturing systems. It enables the production of suitable scheduling plans, considering shared resources between several different products, through several manufacturing environments including networked ones. High levels of uncertainty characterize networked manufacturing environments. Processes have specific and complex requirements and management requisites, along with diversified objectives, which are dynamic and often conflicting. Dynamic adaptation and a real-time response for manufacturing scheduling is still possible and is critical in this new manufacturing environments, which have a flexible nature, where disturbances on working conditions occur on a continuous and even unexpected basis. Therefore, scheduling systems should have the ability of automatically and intelligently maintain a real-time adaptation and optimization of orders production, to effectively and efficiently adapt these manufacturing environments to the inherent dynamic of markets. In this paper a collaborative framework for supporting dynamic scheduling in networked manufacturing environments is proposed, based on a hyper-organization model and on hyper-heuristics, in order to obtain feasible and robust scheduling plans. Copyright © 2014, IGI Global.

Abstract
In the current marketplace, enterprises face enormous competitive pressures. Global competition for customers that demand customized products with shorter due dates and the advancement in information technologies, marked the introduction of the Extended Enterprise. In these EMEs (Extended Manufacturing Environments), lean, virtual, networked and distributed enterprises, form MO (Meta-Organizations), which collaborate to respond to the dynamic marketplace. MO members share resources, customers and information. In this paper we present a hybrid framework based on a DKBS (Distributed Knowledge Base System), which includes information about scheduling methods for collaborative enterprises sharing their problems. A core component of this system includes an inference engine as well as two indexes, to help in the classification of the usefulness of the information about the problems and solving methods. A more structured approach for expanding the MO concept is presented, with the HO (Hyper-Organization). The manner in which MO-DSS can communicate, cooperate and share information, in the context of the HO is also detailed. © 2014 IEEE.

Abstract
Scheduling problems in parallel machines have been deeply studied and many are too complex to be solved by exact methods. The unrelated parallel machines makespan minimization problem (Rm||Cmax) is known to be NP-hard and is usually solved using heuristics. Considering heuristics used in these problems, it is possible to identify two different approaches, those that use the execution time to allocate tasks and those that use the completion time. This paper proposes a new heuristic, OMCT (Ordered Minimum Completion Time), based on the performance limitation of the MCT (Minimum Completion Time). The computational study results demonstrate the effectiveness of the proposed heuristic. © 2014 AISTI.

Abstract
User modeling and user adaptive interaction research areas are becoming crucial applied issues to understand and support users as they interact with technology. Modeling the decisions to be made and the constraints placed by market globalization in a way that can address the needs of all stakeholders has been a long time area of academic and industrial research, mainly for Planning, Scheduling, and Strategic decision making areas. Business analysts, developers, and organizations involved in all phases of the business value chain have requirements for applied business insight through modeling. In this paper, an architecture for user modeling on Intelligent and Adaptive Scheduling System is proposed.

Abstract
Contemporary manufacturing scheduling has still limitations in real-world environments where disturbances on working conditions could occur over time. Therefore, human intervention is required to maintain real-time adaptation and optimization and efficiently adapt to the inherent dynamic of markets. This paper addresses the problem of incorporating rush orders into the current schedule of a manufacturing shop floor organization. A set of experiments were performed in order to evaluate the applicability of supervised classification algorithms in the attempt to predict the best integration mechanism when receiving a new order in a dynamic scheduling problem.

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 (Rm||Cmax) 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.