To provide efficient and flexible material transport, inplant milk-run systems are used by many companies. However, the large number of influencing factors makes in-plant milk-run systems complex, while dependencies and blockages, e.g. overtaking or stopping, require a dynamic approach to examination. Thus we developed a generic simulation model focusing on traffic situations in in-plant road systems, and defined meaningful performance figures, e.g. capacity efficiency and cycle time. The application to a system of industrial practice enabled us to derive recommendations for planning inplant milk-run systems. Space for overtaking, the number of trains passing or supplying a stop as well as the directions a stop is approached from were found to be main influencing factors.

Abstract Order picking is one of the most important process steps in logistics. Because of their flexibility, human beings cannot be replaced by machines. But if workers, in order, picking systems are equipped with a head‐mounted display, Augmented Reality can improve the information visualization. In this paper, the development of such a system—called Pick‐by‐Vision—is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as the subjective strain were measured. The results show that a Pick‐by‐Vision system can considerably improve industrial order picking processes.

Planning a new logistics center is a quite difficult job. Depending on the requirements there are a great number of possible design versions. The planner has to consider not only the present but also the future requirements. Up to now, the planner decides on the basis of experience which system will perform the requested services best. This paper illustrates the redesign of a new order-picking system in industry supported by a simulation tool of our institute designed especially for the rough planning. With the described project the road capability of the applied simulation concept is underlined. Several versions based on the requirements were investigated and evaluated. Key figures are calculated and a recommendation depending on performance and arising expenses is given.

In this paper we report on our ongoing studies around the application of augmented reality methods to support the order picking process of logistics applications. Order picking is the gathering of goods out of a prepared range of items following some customer orders. We named the visual support of this order picking process using head-mounted displays ldquopick-by-visionrdquo. This work presents the case study of bringing our previously developed pick-by-vision system from the lab to an experimental factory hall to evaluate it under more realistic conditions. This includes the execution of two user studies. In the first one we compared our pick-by-vision system with and without tracking to picking using a paper list to check picking performance and quality in general. In a second test we had subjects using the pick-by-vision system continuously for two hours to gain in-depth insight into the longer use of our system, checking user strain besides the general performance. Furthermore, we report on the general obstacles of trying to use HMD-based AR in an industrial setup and discuss our observations of user behaviour.