discrete event simulation

Rising challenges of horizontal transports on container terminals and of the transports between various handling companies in the port demand a continuous improvement of processes and strategies to meet the requirements by clients and society. A low-risk method to test possible improvements is discrete event simulation. In this publication, horizontal transport on container terminals is compared to drayage transport between different handling companies in the port area. Similarities and contrasts are analyzed to simplify the transfer of knowledge between the two research fields.

Discrete-event simulation of logistics and production systems plays an important role in the context of digital transformation. Its integration into modern planning and control processes is urgently required in order to realize Industry 4.0 concepts. In addition, simulation models will be an important part of the so-called digital twin in the planning and operation. However, the requirements for simulation models and tools are not yet comprehensively defined, and technical solutions have not been adequately implemented. This article presents the fields of action for the implementation.

Planning, design and continuous improvement of today’s complex corporate structures and technical systems require a sophisticated level of extensive know-ledge of technology, processes and IT. To apply planning and simulation tools effectively and efficiently engineers and plant operators have to rise to the challenge to use their knowledge in a goal-oriented way and to expand it within creative processes. Consequently, knowledge is more than ever a key productivity factor and an important component of corporate capital. Against this background, the article discusses possibilities for systematization and standardization in simulation studies and especially approaches to increase productivity in simulation studies by supplying assistance functions as well as systematic evaluation methodologies.

Changeability is the capability of an organization to establish changes with a lasting effect. The possibility to correctly plan and create changeability of an organization already in the phase of plan-ning is an essential factor to be taken into account when considering changeable logistics systems. For this reason there is a need for conceptual change of established planning methods. In the context of discrete-event simulation, as an established planning method, the modular design of simulation models may be a first step to include changeability into model-based analysis. Against this background, This article discusses possibilities to build modular simulation models and shows how this modular design can be used in practice.

The investigation of dynamical properties considering job shop systems often employs discrete-event simulation. How-ever, to apply advanced analysis methods like Fourier and correlation analysis to the temporal evolution of important variables, time series are required, which comprise data taken in equally measured timesteps. Therefore sampling is applied. This paper investigates the definition of an optimal sampling rate, which ensures high quality data at a minimum of sampling efforts. Furthermore, errors occurring due to deviating sampling rates are investigated and summed up in qualitative error curves.

In an increasingly turbulent environment, convincing methods of production planning and control are needed. Many of the necessary decisions are made at shop-floor-level. They depend on the knowledge and the abilities of the workers to react on unpredictable impact and hence are not explicitly described. For a realistic, concomitant plant simulation, however, it is important, to model the control strategies as exactly as possible. This paper presents a method to identify applied control strategies by adopting artificial neural networks to data from the operating and machine data logging.