Scheduling optimization is generally concerned with finding a schedule that minimizes makespan, tardiness cost, earliness cost, completion time variance and other time based measures. In this instance, we model a system where the objective is to maximize production level. Here, the cost of tardiness is loss of production. In other words, production delayed is production lost for this system. The problem models a semi-automated chrome plating system in the same manner as a flow-shop but with some crucial differences.  A robust solution is most important in this situation as changes to the schedule are inevitable in the day-to-day working environment. A controlled descent in performance is desirable as the real schedule varies from the computer generated one. Monte Carlo Simulation and Cayley distance based search algorithms are used to find near-optimal solutions