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This paper introduces a multi-agent-based simulation system for optimizing resource allocation in production processes with temporal constraints and process interdependencies, aiming to minimize production completion time. The system employs an adaptive, partially distributed Agent-Based Modelling and Simulation framework to simulate real-world process execution logic, incorporating resource limitations and real-time decision-making. A priority-based genetic algorithm is integrated into the multi-agent system to optimize process sequencing and resource distribution, validated through simulation experiments.
Agent-based simulation, enhanced with a genetic algorithm, offers a promising approach to optimizing resource allocation in complex, time-constrained production environments.
Efficient sequencing of processes and resource allocation are critical in production planning scenarios, such as manufacturing workshops and construction projects, to enhance efficiency and reduce operational costs. Resource allocation in such environments is often challenged by temporal constraints, process interdependencies, and resource limitations, which complicate scheduling and increase the risk of delays. This study presents a multi‐agent‐based simulation system to address these challenges. A scheduling optimisation model is developed to simulate and optimise resource allocation in complex processes with network structures and temporal constraints. The primary objective is to minimise production completion time while ensuring effective resource allocation. Additionally, an adaptive, partially distributed Agent‐Based Modelling and Simulation framework is proposed to simulate the execution logic of real‐world processes, integrating key factors such as resource limitations, process interdependencies, and real‐time decision‐making. A priority‐based genetic algorithm is also designed and embedded into the multi‐agent system to further optimise process sequencing and resource distribution. Simulation experiments across varying case scales validate the model and algorithm. This study highlights the potential of agent‐based simulation for solving complex engineering challenges and provides new insights for addressing resource allocation problems in network‐structured, time‐constrained environments.
