## 2 Global Resources Management in the Industry 4.0
In this work the main focus consists on studying the state of the art research about GRM, and its relation with collaboration theory and practice, in the current I4.0, along with the analysis of expected benefits that can arise from the combined application of management paradigms, along with different types of solving approaches, methods, and algorithms, varying from more or less pure mathematical or optimization methods up to diverse kind of methods, such as those based on AI, for solving management problems in different production environments. These manufacturing environments can vary from more classical ones up to more recent cyber-physical and/or extended, complex, and agile or virtual manufacturing environments. To this end, some relevant and more or less recent GRM paradigms and underlying approaches and systems from the literature are now briefly referred to, in order to better contextualize the work carried out in the scope of the I4.0 context and associated collaborative processes and practices, which is intended to be a novel contribution, as no similar work was identified through the literature analysis performed. In the I4.0 context, one typical example of GRM is distributed manufacturing scheduling (DMS), which is characterized by a set of tasks that have to be chained in order to obtain a coordinated workflow among the dispersed manufacturing resources. This chaining process results in a more or less complex production program through the allocation and sequencing of the tasks on the corresponding production resources, which has to satisfy a set of constraints related either to the production resources itself and/or to the tasks, in order to reach some simple or combined or complex goal. Currently, due to the globalization, DMS plays a crucial role, and diverse approaches have been proposed to accomplish it; a very popular one is based on a multi-agent systems (MAS), through the use of appropriate architectures and protocols [38]. One such contribution concerning DMS is mentioned in [15], which is considered to be necessary in the current global production environments. Another example is presented in [15] about an approach for dynamic DMS, supported by a dynamic multicriteria decision model (DMCDM), and by further integrating strategies that enable trade-offs between diverse kind of performance measures. Moreover, there are many different kind of approaches, algorithms, tools, or systems and platforms to support GRM or, more precisely, global manufacturing scheduling, that can be further implemented. These vary from purely centralized up to fully decentralized architectures, for instance for further integrating other management functions, besides manufacturing scheduling, such as process planning, batching, system balancing, and layout definitions, namely referred to in the following sources [7; 12; 39; 40]. In [41] a simulation model is proposed that implements a dynamic scheduling scheme to generate training scheduling examples, considered by the authors to be good schedules. Their search training was performed by using a proposed genetic algorithm, along with a tolerance-based learning algorithm requiring the acquisition of general scheduling rules from the scheduling training examples, and further adapting to new perceived examples, enabling knowledge modification. According to the authors, their experimental results showed that the dynamic scheme meaningfully outperformed a static one when integrating a simple dispatching rule for performing the distributed scheduling. In [42] an agent-based approach is proposed for distributed manufacturing programming, which enables companies to solve a global combinatorial optimization schedule, by integrating a jobs process plan in a distributed production environment. Their approach was adapted from a particle swarm optimization (PSO) algorithm, through which the agents move towards a schedule to find a best global makespan. [PERSON] et al.