A mesoscopic approach to the simulation of logistics systems

This paper presents a mesoscopic simulation approach in addition to the prevailing continuous and discrete event simulation approaches used for logistics systems. In terms of level of detail, the mesoscopic approach falls between these two approaches. Mesoscopic models represent logistics flow processes through piecewise constant flow rates. The resulting linearity of the cumulative flows allows for event scheduling and the use of mathematical formulas for recalculating the system's state variables at every simulation time step. The simulation time step is variable and the step size depends on the occurrence of scheduled events. This leads to a high computational performance. The mesoscopic approach distinguishes between different parallel product types. The modeling components are multichannel funnels, delays, assemblies and disassemblies

Logistikforschung und -training durch ausländische Forschungsinstitute in China

Aufgrund der aktuellen und zukünftig zu erwartenden wirtschaftlichen Entwicklung in China mit jährlichen Wachstumsraten von ca. 10 % und der Öffnung des chinesischen Markts für Logistikdienstleistungen rechnen ausländische Logistikdienstleister mit einem jährlichen Wachstum von ca. 30 % im oberen besonders renditeträchtigen Marktsegment. Eine der größten Herausforderungen stellt dabei das Fehlen hinreichend qualifizierter einheimischer Logistik-Fachkräfte dar. Hier liegt eine Chance für ausländische Forschungsinstitute und Unternehmen in China tätig zu werden. Der Beitrag beschreibt die Aktivitäten des ILM und des Fraunhofer IFF in diesem Bereich und geht dabei beispielhaft auf durchgeführte Trainingsmaßnahmen mit Logistikplanspielen und Forschungsaktivitäten innerhalb von EU-Projekten ein

Towards Learning- and Knowledge-Based Methods of Artificial Intelligence for Short-Term Operative Planning Tasks in Production and Logistics: Research Idea and Framework

Driven by the increasing digitalization, experts estimate a major change concerning the planning and operation of production systems. The trends indicate a shift from centrally controlled and fixed interlinked production resources to a decentralized production consisting of self-managing cyber-physical systems. This article describe the resulting challenges for the short-term operative production and logistics planning as well as the limitations of current methods. In the further course, the article discusses application potentials of artificial neural networks and fuzzy logic to tackle short-term operative planning tasks in production and logistics. The article concludes with a research framework, which outlines our future steps

Simulation-based planning and optimization of an automated laundry warehouse using a genetic algorithm

The planning of logistics systems is a complex task with important decisions to make. Simulation models can help already in the early planning process of these systems. Usually they only provide a visualization of the different planned concepts but with modern genetic algorithm it is possible to provide even more support. Numerous parameters are not yet fixed at this point of a planning process. A dynamic model with flexible parameters and a genetic algorithm (GA) can already deliver good approximated solutions. An exemplary procedure for using this GA in the context of the research and development project "LOCSys" (Laundry Order Consolidation System) is presented in this paper. We use the genetic algorithm to find good approximated parameters for an optimised throughtput by changing the dimensions and control parameters of a warehouse with an automated picking and retrieval unit