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

Abstract

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. INTRODUCTIONManufacturing companies are experiencing many challenges regarding customer orientation and on-time production, especially in an intensified global business and a digital transformation. In the past, generally, the primary objective was to increase the utilization of production chains towards an economic optimum. However, this goal has increasingly shifted more towards customer-oriented production in recent years. Accordingly, the focus of most companies is now on-time feasibility and adherence to promised delivery dates. This confronts operational production planning with questions about certain batches' latest possible release, so that the product can still be manufactured and delivered on time. A constantly increasing complexity of production systems, in conjunction with a high degree of automation and random events -such as equipment failures and dependencies of immediately upstream or downstream equipment groupsrepeatedly pose challenges for many production companies. As a result, there is an increasing need