Automotive Production Systems: A Diophantine Simulation Framework with Genetic Algorithm-Driven Stochastic Data Generation

Discrete production planning under integer constrained resource framework is a challenging issue which requires simultaneous consideration of output maximization, resource efficiency, and balanced resource. This research focuses on simulation of an integer-driven production planning model for an automotive production system. It combines the genetic algorithm-based stochastic data generator with a precise Diophantine feasibility enumeration. The genetic algorithm is used as a constraint-aware stochastic specification generator to generate feasible production parameter sets within certain operational constraints. Its main purpose is to create representative production environments for feasibility analysis and not to optimize production. A normalized multi-objective scoring function is presented to address the imbalance in the scales of economic and operational measures. A total of 64,518 feasible automotive production plans were enumerated under engine, tire, labor and budget constraints using the proposed framework. The Pareto-efficient solutions to the cost–output space that were identified, formed a discrete, piecewise Pareto frontier. The best production plan had a total of 83 units with 99% of the labor and tire resources exploited, whereas the budget and engine capacities were not binding. The optimal strategy implies full saturation of the labor capacity (>99%) due to the binding nature of labor as an objective. In practice, a safety buffer can be imposed through the introduction of an upper-bound utilization policy (e.g., 95%), which moves the optimal solution marginally inwards along the Pareto frontier. The analysis of sensitivity to changes in resources of ±10% showed the preservation of the Pareto structure and resilient adaptability in the output, which validated the usefulness of the suggested strategy in discrete manufacturing decision support.