The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments.

To address the multi-objective optimization of stamping process parameters for vehicle components, a hybrid framework coupling Dynaform simulations, Orthogonal Experimental Design, BPNN, NSGA-II, and TOPSIS was proposed. Taking car door accessories as a case study, OED was utilized to investigate the interacting effects of blank holder force, friction coefficient, stamping speed, and die clearance on the maximum thinning and thickening rates. The simulated data trained a BPNN to construct the highly non-linear mapping relationships. Subsequently, the NSGA-II algorithm generated the Pareto optimal frontier, and TOPSIS objectively selected the best compromise process parameters: a blank holder force of 43,113 N, a friction coefficient of 0.13, a stamping speed of 2496 mm/s, and a die clearance of 1.0 mm. Applying this combination effectively constrained the maximum thinning and thickening rates to 41.4% and 16.8% respectively. The BPNN relative prediction errors against numerical validation were merely 0.0691% and 6.9930%, fully verifying the high fidelity and effectiveness of the proposed optimization methodology.