Resource Allocation Optimization in Process Petri Nets Based on Bottleneck Identification

Resource allocation systems are considered fundamental to many practical applications, where the challenge is often posed by the need to optimize resource usage under budget constraints to improve system performance. In this paper, a systematic approach to resource allocation in Petri net-based systems is presented. First, a method for system bottleneck identification is analyzed to reveal the structural limitations affecting throughput. Based on this, the minimum number of resources required to maximize system throughput without budget constraints is determined. Subsequently, a heuristic iterative resource allocation strategy is developed, in which mixed-integer linear programming is employed to guide the allocation process considering budget levels. The effectiveness of the proposed method is demonstrated through comparative experiments under different resource constraints. Note to Practitioners - In many practical settings such as automated manufacturing, logistics systems, cloud-based services, and cyber physical infrastructures, the availability and allocation of shared resources directly influence system throughput and responsiveness. Budget limitations often prevent simply adding more capacity, making it critical to allocate limited resources in a way that maximizes performance. This work introduces a structured and computationally efficient resource allocation strategy built on Petri net representations, with explicit consideration of budget limitations. By identifying structural bottlenecks and iteratively assigning resources to the most constraining components, the proposed approach guides decision-making toward cost-effective investments that improve overall throughput. Unlike methods that require complete state exploration or assume generous resource availability, the approach remains applicable under tight budget conditions without suffering from state explosion. Practitioners can use the proposed method as a decision support mechanism during system design or retrofit, enabling improved performance while respecting cost limits in complex discrete-event systems.