Inspired by the biologic nature of flying geese, process goose queue (PGQ) approaches toward plantwide process optimization are explicitly introduced in this paper along with applications in real-time optimization (RTO). Taking advantage of ad-hoc PGQ metrics, process variables associated with a process unit could be accordingly identical with geese positions of a PGQ Motivated by the self-organization in flight formation of geese, a process unit can achieve such an optimum formation that every goose in the PGQ benefits from the maximum upwash. In this sense, adjustment rules invoked to track the ideal PGQ formulation are accommodated. Followed by this idea, a plantwide process is first decomposed into several hierarchically connected multilayer PGQs. Subsequently, a plantwide PGQ which includes a PGQ:objective and several multilayer PGQs is constructed, which contributes to solving complex plantwide process optimization problems in a novel way. As applications of PGQ approaches, we initially address a supervision-driven RTO issue concerning economic performance deterioration caused by process supervision. A process unit whose variables are shifted by human operators can be regarded as an ill-PGQ which would trigger the autonomous adjustments of the plantwide PGQ Enabling algorithms concerning adjustment sequence of the plantwide PGQ with an ill-PGQ are constructed, which are generally characterized by ill-PGQ detection, PGQ follow-up, and PGQ:objective achievement. To demonstrate the feasibility and validity of this contribution, the Tennessee Eastman (TE) benchmark process is employed as an extensive case study, showing that the proposed approaches particularly enjoy considerable computational simplicity in contrast with traditional global optimization strategies.