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
Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal.

Abstract
Developments in advanced autonomous production resources have increased the interest in the Single-Machine Scheduling Problem (SMSP). Until now, researchers used SMSP with little to no practical application in industry, but with the introduction of multi-purpose machines, able of executing an entire task, such as 3D Printers, replacing extensive production chains, single-machine problems are becoming a central point of interest in real-world scheduling. In this paper we study how simple, easy to implement, Just-in-Time (JIT) based, constructive heuristics, can be used to optimize customer and enterprise oriented performance measures. Customer oriented performance measures are mainly related to the accomplishment of due dates while enterprise-oriented ones typically consider other time-oriented measures. © 2015 AISTI.

Abstract
In the fiercely competitive era induced by expansion of open business archetypes, the managerial aspects of Extended Manufacturing Environments (EMEs) are experiencing growing concerns. There is no scope of leaving a possible operational improvement unexplored. For enhanced operational efficiency and capacity utilization the balancing and scheduling problems of EMEs are, therefore, rightfully considered and an integer programme is proposed in this paper. The model is designed in a spread sheet and solved through What'sBest optimizer. The model capabilities is assessed through a test problem. The results have demonstrated that the model is capable of defining optimized production schedules for EMEs. © 2015 AISTI.

Abstract
Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyperheuristic could be advantageous on solving dynamic adaptation optimization problems.