DEVS is a sound Modeling and Simulation (M&S) framework that describes a model in a modular and hierarchical way. It comes along with an abstract simulation algorithm which defines its operational semantics. Many variants of such an algorithm have been proposed by DEVS researchers. Yet, the proper interpretation and analysis of the computational complexity of such approaches have not been systematically addressed and defined. As systems become larger and more complex, the efficiency of the DEVS simulation algorithms in terms of time complexity measure becomes a major issue. Therefore, it is necessary to devise a method for computing this complexity. This paper proposes a generic method to address such an issue, taking advantage of the recursion embedded in the triggered-by-message principle of the DEVS simulation protocol. The applicability of the method is shown through the complexity analysis of various DEVS simulation algorithms.

Strategic modelling with a panoramic view plays an important role in decision-making problems. It offers the possibility of generating different solutions before making a decision. This is particularly relevant in critical situations. This article addresses the problem of allocating resources, whether financial, material or human, so that it is optimal under a given set of constraints and inter-dependencies with other systems. To do this, existing strategies such as those of Colonel Blotto are studied in order to evaluate them according to some criteria, including the heterogeneity or homogeneity of resources and/or battlefields. Based on the results of these configurations, we propose distributed strategic learning methods to find better resource allocation strategies. The proposed algorithms are implemented under various scenarios, including incomplete information. Case studies are carried out to test the effectiveness of these new strategies compared to previous ones. A complexity analysis of the different algorithms is also presented.