Scheduling agricultural machinery across multiple cooperatives is often inefficient because existing rigid, discrete assignment models fail to flexibly coordinate shared resources under tight time windows. To address this limitation, we develop a simulation-based framework for the Multi-cooperative Agricultural Machinery Scheduling Problem (MAMSP) underpinned by a Continuous Collaborative Workload Sharing (CWS) formulation. To mitigate the solution fragmentation inherent in continuous optimization, we propose a Hybrid Particle Swarm Optimization with Sparsity Repair (HPSO-SR). The algorithm integrates a stochastic initialization strategy to enhance global exploration, a mutation injection mechanism to avoid swarm stagnation, and a sparsity repair operator that prunes uneconomical fractional assignments, yielding operationally feasible sparse schedules. A real-world case study from Liyang, China, augmented by synthetic instances of varying scales (small, medium, and large), was conducted to benchmark the proposed approach against a rule-based heuristic, a Genetic Algorithm (GA-CWS), and Simulated Annealing (SA-CWS) under a unified decoding scheme. The results show that HPSO-SR consistently achieves the lowest objective values, reducing the total cost by 74.43% relative to GA-CWS and 59.20% relative to SA-CWS in the medium-scale case. By deliberately trading off minimal additional transfer cost against improved timeliness, the obtained schedules nearly eliminate delay penalties. Sensitivity analysis and mechanism ablation studies further confirm that the sparse solutions exhibit structural resilience and that the proposed repair strategy is essential for algorithmic convergence, supporting the reliability of the proposed approach for time-critical, high-stakes agricultural operations.

Coordinating a shared fleet across multiple owners under tight time windows is a challenging multi-objective problem balancing cost, timeliness, and equity. We study it for multi-cooperative agricultural machinery dispatch, formulating the Multi-Cooperative Agricultural Machinery Scheduling Problem under Continuous Workload Sharing (MAMSP-CWS) as a three-objective model that minimizes inter-area transfer cost, time-window violation, and cross-cooperative workload imbalance. To approximate the Pareto front, we develop a Multi-Objective Hybrid Particle Swarm Optimization with Tabu Search and Sparsity Repair (MO-HPSO-TS-SR), which couples particle-swarm search, tabu-search refinement, and a sparsity-repair operator within an external crowding-distance archive. The method is evaluated on three scales (a real instance from Liyang, China, and two synthetic ones) against NSGA-II-CWS and HTSMOGA-CWS over 20 independent runs each. MO-HPSO-TS-SR attains the best mean value on every metric-by-scale combination, with a decisive convergence advantage (hypervolume and IGD; Holm-adjusted $p<0.001$, Cliff’s $\delta \approx 1$). A mechanism decomposition identifies Sparsity Repair as the dominant contributor to hypervolume, with Tabu Search as a complementary refiner. The advantage over NSGA-II-CWS widens with problem scale, from 19.4% on the Small instance to 74.2% on the Large instance, reflecting the disproportionate degradation of the genetic baseline rather than a growing advantage of the proposed method. Beyond agriculture, the framework extends to other continuous-encoding scheduling problems, providing a transferable decision-support tool.

The efficiency of ammunition support operations in the aircraft carrier intermediate ordnance staging deck is critical to sortie generation rates in naval aviation, yet joint scheduling and personnel allocation in this multistage, resource-constrained environment remains a challenging bi-objective optimization problem. This study develops a framework integrating an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with a marginal-benefit-based iterative feedback mechanism. The intermediate ordnance staging deck support process is decomposed into individual ammunition processing stations and formulated as a processflow model incorporating operation sequencing and personnel specialization constraints. A constraint decision model then dynamically reconciles the minimization of total makespan and personnel workload equilibrium through iterative marginal-benefit comparison across support teams. The NSGA-II is enhanced with an adaptive crossover-mutation mechanism and an improved elitism preservation strategy to strengthen global search capability. Validation on a typical carrier intermediate ordnance staging deck scenario demonstrates that the improved NSGA-II outperforms the conventional NSGA-II in convergence speed and Pareto front quality. Under the optimized configuration, the total makespan remains 3600 s with a workload balance metric of 1075 even as ammunition quantity doubles from two to four units. The proposed framework offers practical decision support for carrier ammunition operations and extends to other resource-constrained multi-objective scheduling domains.