Majsa Ammouriova, Madlene Leißau, Javier Panadero, Christin Schumacher, Christoph Laroque, Angel A. Juan • Winter Simulation Conference 2022, Singapore

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.

ResearchGate

Christoph Laroque, Madlene Leißau, Wolfgang Scholl, Germar Schneider • CIRP CMS 2022, Lugano (Switzerland)

Manufacturing companies are experiencing many challenges with regard to customer-oriented and on-time production, especially in the context of an intensified global business and while advancing the digital transformation. Accordingly, the ongoing development and deployment of Industry 4.0 solutions for customisable products in small batch sizes, not only pose new problems for work preparation, but also for operative production planning in connection with high cost, time and quality pressure. Modern, complex and highly automated production systems (in this case from the semiconductor domain) must be operated close to an optimal operating state in order to be economically reasonable. Promised delivery dates and throughput times specified in contracted service agreements must be ensured and require a permanent and effective adjustment of production planning and control in daily execution. All other general conditions of an economic production remain unchanged. Today, the question of whether the production target is realistic and whether all promised delivery dates are met are today still answered with rather simple backward-oriented approaches, mostly without taking into account uncertainties, stochastic behaviour of the manufacturing system or alternatives that arise during operation. As shown in previous publications, these questions can be answered in more detail and more resilient using a backward-oriented discrete event-based simulation approach (SimBack). This article presents additional findings. The results show and deepen the impression, that the SimBack-approach can be successfully solve scheduling questions for customer-specific orders in a real-world environment.

ResearchGate

Christoph Laroque, Madlene Leißau, Pedro Copado, Christin Schumacher, Javier Panadero, Angel A. Juan • 26. Algorithms 15(2)

Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.

ResearchGate

Deike Gliem, Ulrich Jessen, Sigrid Wenzel, Wibke Kusturica, Christoph Laroque • Logistics Research 15(5)

Due to severe global competition, the project management process for one-of-a-kind production must aim for better risk management and higher efficiency. Despite being critical in order to forecast the duration of logistics processes, intra-enterprise knowledge, historical data from past projects as well as simulation models are not sufficiently utilized in today´s practices. To improve the project planning for one-of-a-kind production, a systematic modeling concept and a technical solution approach are developed, which are based on logistics reference processes and an ontology, as a major part of the overall systematic. This paper focuses on the development of the basic structure of the ontology. A use case applies the ontology within a project management toolset to highlight the potentials for process duration forecasting in project management.

ResearchGate