| description abstract | Abstract. Managing modern engineering processes is increasingly challenging as they evolve into socio-technical systems where human experts and artificial intelligence (AI) agents coexist. The efficiency of such environments is highly vulnerable to unanticipated stochastic interruptions—such as urgent design changes, field issues, and equipment malfunctions—which share a common operational signature: they are unplanned, time-critical, and induce cascading bottlenecks that increase project makespan, exposing the limitations of conventional static scheduling. This study proposes a generalized human–AI collaborative dynamic reallocation framework to minimize the makespan and enhance the operational flexibility of disruption-prone engineering workflows. The proposed framework addresses dynamic workflow characteristics through two complementary policies: (i) task offloading from overloaded human experts to AI agents, and (ii) resource sharing to lend computational capacity to bottlenecked agents. These two policies are operationalized through an integrated dynamic allocation process that derives resource distribution via a long short-term memory-based workload predictor refined by a reinforcement learning calibration layer. The framework is evaluated via simulation using industrial issue-log data from large-scale software development, where urgent design changes serve as the representative interruption class. The proposed method reduced the makespan by approximately 12% compared to a first-in-first-out, first-available baseline heuristic under uncertain interruptions, and its robustness was verified across 27 diverse workload scenarios. This study presents a new dynamic resource allocation model that strengthens operational resilience by mitigating bottlenecks and proactively utilizing AI resources in engineering collaboration systems. | |
