Urban battery swapping station (BSS) planning is difficult because site opening, service-module allocation, user assignment, battery degradation pressure, travel burden, and congestion are tightly coupled. A plan that minimizes investment alone may create long queues, whereas a plan that only reduces waiting can overbuild costly and underused capacity. This study formulates the urban BSS siting–sizing problem as an operation-aware mixed-integer nonlinear model and evaluates feasible plans with payoff-table global-criterion normalization. To search this rugged planning space, we propose a Stable Portfolio Hyper-Heuristic (SPHH) that combines Greedy construction, BO-guided large-neighborhood search, simulated annealing, and optimized annealing with reheating, followed by feasible-incumbent preservation and non-worsening post-processing. The formal campaign contains 18 synthetic cases, 10 independent repeats, and five algorithms, yielding 900 optimization records. Across the 18 cases, SPHH produced the lowest mean GC score and reduced the mean GC value on average relative to the baseline algorithms. Nonparametric Friedman and Holm-adjusted Wilcoxon tests confirmed statistically significant differences among methods, although the advantage over the strongest baseline was not universal. These results indicate that SPHH is most useful as an offline planning selector that improves recommendation stability when additional computation is acceptable, rather than as a universally faster optimizer.
Human–robot collaborative flexible job shop scheduling (HRC-FJSP) must coordinate heterogeneous capabilities, mode-dependent processing times, safety feasibility, and carbon constraints. The problem becomes harder when a collaboration mode that is attractive during planning becomes infeasible after a human enters the robot safety separation zone. Unlike conventional dynamic disturbances such as machine breakdown or order insertion, this event changes the feasible collaboration mode of the unfinished operation remainder rather than only delaying a resource or adding a job. This study formulates a carbon-aware dynamic HRC-FJSP and evaluates a carbon-aware multi-agent deep reinforcement learning scheduler (CA-MADRL) with local recovery after safety-proximity-induced collaboration disruption. The objective combines normalized makespan, carbon emission, and human workload imbalance with carbon accounting based on operation energy and time-varying grid carbon intensity. Across the benchmark cases, CA-MADRL obtains the best average global criterion (0.7235), wins nine of 12 cases, and achieves the lowest average carbon emissions among the compared policies (48.991 kg CO2e). Sensitivity analysis shows that stronger carbon preference reduces emissions but increases makespan and tardiness, while adaptive collaboration outperforms fixed human–robot, human-only, and robot-only regimes. The results indicate that dynamic mode adaptation and local rescheduling improve carbon-aware collaborative schedules under safety disruption.
The framework supports data-informed platform governance by linking propagation thresholds, algorithmic down-ranking, reply thread moderation, intervention cost, and robustness bounds within a common threshold control language for practical settings.
