🤖 AI Summary
This paper addresses the disconnection between resource scheduling and system modeling in complex large-scale projects. We establish, for the first time, a systematic linkage among the Resource-Constrained Project Scheduling Problem (RCPSP), Model-Based Systems Engineering (MBSE), and Heterogeneous Functional Graph Theory (HFGT). A traceable transformation mechanism is proposed—from RCPSP activity networks to SysML activity diagrams, heterogeneous functional graphs, and operational networks—reformulating RCPSP as a minimum-cost flow problem within a heterogeneous functional network. This enables unified modeling and optimization of both renewable and non-renewable resources. The method preserves classical scheduling performance while yielding interpretable, semantically rich project state representations. As a result, it significantly enhances dynamic monitoring capabilities for complex systems and strengthens enterprise-level decision support.
📝 Abstract
Within the project management context, project scheduling serves as an indispensable component, functioning as a fundamental tool for planning, monitoring, controlling, and managing projects more broadly. Although the resource-constrained project scheduling problem (RCPSP) lies at the core of project management activities, it remains largely disconnected from the broader literature on model-based systems engineering (MBSE), thereby limiting its integration into the design and management of complex systems. The original contribution of this paper is twofold. First, the paper seeks to reconcile the RCPSP with the broader literature and vocabulary of model-based systems engineering and hetero-functional graph theory (HFGT). A concrete translation pipeline from an activity-on-node network to a SysML activity diagram, and then to an operand net is constructed. Using this representation, it specializes the hetero-functional network minimum-cost flow (HFNMCF) formulation to the RCPSP context as a systematic means of HFGT for quantitative analysis and proves that the RCPSP is recoverable as a special case of a broader model. Secondly, on an illustrative instance with renewable and non-renewable operands, the specialized HFNMCF, while producing similar schedules, yields explicit explanations of the project states that enable richer monitoring and control. Overall, the framework preserves the strengths of the classical RCPSP while accommodating real-world constraints and enterprise-level decision processes encountered in large, complex megaprojects.