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Configuration design in System of Systems (SoS) environments presents substantial challenges due to the autonomy, heterogeneity, and complex interdependencies of constituent systems. This paper investigates configuration designs in SoS through a multi-agent simulation approach. The automated quarry system is used as an illustrative case study. We identify and characterize parameters, constraints, and interdependencies that shape configuration options in an automated quarry, including factors such as dump trucks carrying capacity, loaders efficiency, and road conditions. To address the high-dimensional and dynamic nature of the configuration space, we develop a twophase design space exploration (DSE) methodology combining sensitivity analysis with multi-objective evolutionary optimization. Our simulation framework models the operational dynamics of an automated quarry, capturing both static constraints (e.g., fleet size, loading capacity) and dynamic environmental factors (e.g., road condition, queues, and delays). Results demonstrate that the proposed approach efficiently narrows the configuration space and identifies Pareto-optimal solutions that significantly improve mission outcomes in terms of increasing throughput and delay reduction. The analysis reveals that parameters like dump trucks carrying capacity, queues and delay dominate system performance.

The Unified Mission Ontology (UMO), previously introduced as a comprehensive mission ontology, aims to formalize mission concepts, offering a unified approach to mission representation, design, and analysis. This paper analyzes the UMO through three research questions: its effectiveness in representing domain-specific applications, its impact on improving mission-centric processes, and its facilitation of design space exploration. Our analysis is demonstrated in the context of an automated quarry application that exhibits key characteristics of a system of systems (SoS). Our findings provide valuable insights into UMO's potential for domain-specific applications, offering recommendations for its refinement, and highlighting its contributions to the development of efficient and interoperable ontologies for mission-centric design and analysis.

The concept of a mission is important to system design and development, especially in system of systems (SoS) engineering. However, the diverse usage of the term 'mission' across disciplines often results in ambiguity regarding its role in practical applications in mission-centric engineering tasks. Clearly defined and precisely represented missions improve communication among stakeholders and help bridge interdisciplinary gaps. This study aims to investigate and analyze the state of the art for mission conceptualizations and representations and proposes a unified mission ontology (UMO) that improves semantic interoperability across various domains. To achieve this goal, we conducted a systematic literature review (SLR) to examine how missions are conceptualized and represented, analyzed the findings to obtain insight about cross-domain concepts related to missions, and developed a UMO that can be adapted to domain specific applications. The UMO facilitates semantic interoperability across domains through a high-level abstraction of shared concepts. To validate the comprehensiveness and adaptability of the UMO, we conducted coverage analysis using semantic similarity estimates to assess the equivalence of ontological concepts. This evaluation quantified the extent to which concepts from various domain-specific ontologies, including the mission engineering guideline, align with those in the UMO.

In its broadest sense, collaboration can be defined as a united effort to accomplish activities that ultimately lead to the achievement of shared objectives. This requires the sharing of information, planning, risks, and rewards between collaborating entities to a certain extent. In the case of systems of systems (SoS) where diverse and autonomous constituent systems (CS) interact to achieve shared goals, collaboration presents multifaceted challenges resulting from its distinct characteristics, including interconnectedness among the CS, heterogeneity, scalability concerns, dynamic environments, emergent behavior, stakeholder alignment challenges, and intricate decision-making processes. In order to enhance the achievement of the SoS goals, we propose a policy-guided collaboration approach. In this regard, we establish a learning-based policy generation process with the goal of guiding the decision-making behavior of CS. The practicality of the proposed approach is illustrated through a focused analysis of a high-rise building fire incident response system. Based on simulation results, the proposed approach performs better than the conventional approach in terms of SoS specific task completion time, performance with changes in simulation inputs, and efficiency. We also conducted a sensitivity analysis of task completion time by varying independent decision variables such as the number of CS instances and the size of collaborative tasks.