A Biased-Randomized Simheuristic for a Hybrid Flow Shop with Stochastic Processing Times in the Semiconductor Industry

Compared to other industries, production systems in semiconductor manufacturing have an above-average level of complexity. Developments in recent decades document increasing product diversity, smaller batch sizes, and a rapidly changing product range. At the same time, the interconnections between equipment groups increase due to rising automation, thus making production planning and control more difficult. This paper discusses a hybrid flow shop problem with realistic constraints, such as stochastic processing times and priority constraints. The primary goal of this paper is to find a solution set (permutation of jobs) that minimizes the production makespan. The proposed algorithm extends our previous work by combining biased-randomization techniques with a discrete-event simulation heuristic. This simulation-optimization approach allows us to efficiently model dependencies caused by batching and by the existence of different flow paths. As shown in a series of numerical experiments, our methodology can achieve promising results even when stochastic processing times are considered.