Unternehmen sehen sich mit einem zunehmend turbulenteren Umfeld konfrontiert. Diese Anforderung bedingt eine permanente Bereitschaft zur effizienten Fabrikanpassung. Dieser Beitrag stellt auf Basis systemimmanenter Flexibilitätskorridore und vorgehaltener Wandlungsfähigkeit das Leitbild des Graduiertenkollegs 2193 für eine anpassungsintelligente Fabrik vor. Dieses beruht auf den Befähigern Anpassungsprozess, Anpassungsobjekt und Anpassungsteam und verfolgt einen betont interdisziplinären Forschungsansatz mit dem Ziel einer holistischen Anpassungsplanung.*)

Operators of parcel transshipment terminals face the challenge of sorting and transferring a large number of parcels efficiently. In order to meet customer expectations, short sorting intervals are required. In this paper we present a technical framework that combines metaheuristics with discrete-event simulation (DES) to provide robust solutions for problems at the operational level of parcel transshipment terminals. First, the framework applies metaheuristics such as local search solve the problem of scheduling incoming trucks as well as allocations at the loading gates taking into account the characteristics of the internal sorting system. Next, detailed conclusions on the real system behavior are drawn by testing the solutions in multiple DES experiments with stochastic processing times. The paper presents first results using our framework and investigates the procedure with regard to the solutions quality and runtime requirements.

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.

In modern production systems, scheduling problems have to be solved in consideration of frequently changing demands and varying production parameters. This paper presents a approach combining forecasting and classification techniques to predict uncertainty from demands, and production data with heuristics, metaheuristics, and discrete event simulation for obtaining machine schedules. The problem is a hybrid flow shop with two stages, machine qualifications, skipping stages, and uncertainty in demands. The objective is to minimize the makespan. First, based on the available data of past orders, jobs that are prone to fluctuations just before or during the production phase are identified by clustering algorithms, and production volumes are adjusted accordingly. Furthermore, the distribution of scrap rates is estimated, and the quantiles of the resulting distribution are used to increase corresponding production volumes to prevent costly rescheduling resulting from unfulfilled demands. Second, Shortest Processing Time (SPT), tabu search, and local search algorithms are developed and applied. Third, the best performing schedules are evaluated and selected using a detailed simulation model. The proposed approach is validated on a real-world production case. The results show that the price for a very robust schedule that avoids underproduction with a high probability can significantly increase the makespan.

In production systems, scheduling problems need to be solved under complex environmental conditions. In this paper, we present a comprehensive scheduling approach that is applicable in real industrial environments. To cope with the parameter uncertainty of real world problems, forecasting, classification, and simulation techniques are combined with heuristic optimization algorithms. Thus, the approach allows for identifying and including demand fluctuations and scrap rates, and offers a selection of suitable schedules depending on particular demand constellations in scheduling. Furthermore, we adapt seven optimization algorithms for two-stage hybrid flow shops with unrelated machines, machine qualifications, and skipping stages with the objective to minimize the makespan. The combination of methods is validated on a real production case of the automobile industry. The paper shows for the application case that metaheuristics provide significantly better results than SPT and safety factors, above a certain size, can reduce their effect preventing incomplete demand positions.