Using Simulation Systems for Decision Support

This chapter describes the use of simulation systems for decision support in support of real operations, which is the most challenging application domain in the discipline of modeling and simulation. To this end, the systems must be integrated as services into the operational infrastructure. To support discovery, selection, and composition of services, they need to be annotated regarding technical, syntactic, semantic, pragmatic, dynamic, and conceptual categories. The systems themselves must be complete and validated. The data must be obtainable, preferably via common protocols shared with the operational infrastructure. Agents and automated forces must produce situation adequate behavior. If these requirements for simulation systems and their annotations are fulfilled, decision support simulation can contribute significantly to the situational awareness up to cognitive levels of the decision maker

Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes

Stability and Sensitivity Measures for Solutions in Complex, Intelligent, Adaptive and Autonomous Systems

Simulation has become a pivotal tool for the design, analysis, and control of complex, intelligent, adaptive and autonomous systems and its components. However, due to the nature of these systems, traditional evaluation practices are often not sufficient. As the components follow adaptive rules, the cumulative events often exploit bifurcation enabling events, leading to clusters of solutions that do not follow the usual rules for standard distributed events. When using simulation for design, analysis, and control of such systems, the evaluation needs to be richer, applying bifurcation and cluster analysis to understand the distribution, applying factor analysis to understand the important factors for the necessary sensitivity analysis, and take not only point estimates for the solution and the sensitivity analysis into account, but contact a statistical stability analysis. The full exploitation of gaining numerical insights into the dynamic behavior and its deviations is needed. This paper introduces the pitfalls and recommends applicable methods and heuristics

Merging Two Worlds: Agent-Based Simulation Methods for Autonomous Systems

This chapter recommends the increased use of agent-based simulation methods to support the design, development, testing, and operational use of autonomous systems. This recommendation is motivated by deriving taxonomies for intelligent software agents and autonomous robotic systems from the public literature, which shows their similarity: intelligent software agents can be interpreted as the virtual counterparts of autonomous robotic systems. This leads to examples of how simulation can be used to significantly improve autonomous system research and development in selected use cases. The chapter closes with observations on the operational effects of possible emergent behaviour and the need to align the research agenda with other relevant organisations facing similar challenges

Special Issue: M&S Optimization Applications in Industry and Engineering, Part 2

(First paragraph) Welcome to the second part of the special issue on modeling and simulation (M&S) optimization applications in industry and engineering. This issue incorporates additional publications that reflect various applications and areas of simulation and optimization