Application of modern warehouse technology in the Slovenian automotive industry

The objective of this paper is to demonstrate the usefulness and reliability of modern warehouse technology for suppliers in the automotive industry and to identify the potential causes preventing it from being used more in practice. In recent years, we have seen great progress made in the development of modern warehouse solutions, as well as a dramatic rise in research on the issues related to the introduction of modern technology and interaction between operators and automation. However, a look at some of the existing studies on the usefulness of the use of modern technology reveals that their conclusions are often contradictory. The results of statistical analysis of Slovenian companies that operate as suppliers in the international automotive industry show that modern warehousing technology is reliable and safe to use, but more than 60% of companies fail to take advantage of its benefits. These companies are also using basic warehouse technology, despite the fact that such technology does not ensure a sense of safety in the warehouse. In the medium term, this could put them at a disadvantage against competitors in the demanding sector of the automotive industry. The results of the study provide additional starting points for understanding human use of modern warehouse technology, which can lead to improvements in how warehouse systems are designed, more effective employee training methods and a reasonable and balanced policy for the automation of the warehousing processes

A Highway-Driving System Design Viewpoint using an Agent-based Modeling of an Affordance-based Finite State Automata

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the Affordance-based Finite State Automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the Highway-Lane-Driver System. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers??? characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This study is also expected to benefit a designed control for autonomous/self-driving car in the future

An Affordance-Based Model of Human Action Selection in a Human-Machine Interaction System with Cognitive Interpretations

Current technology is not sufficient to automate all desired tasks. Human-machine interaction (HMI) has thus become a key control and design factor for tasks requiring human-level decision-making or information synthesis. Such processes require a formal representation of human actions (including decision-making) when modeling HMI systems; however, successful prescriptive approaches to this end have still been elusive. This article extends the affordance-based finite state automata model, conditioning human prior experience and natural memory decay of task knowledge (or skill decay). The new model draws upon both reinforcement learning and natural memory decay for decision-making on action choice. An empirical study is carried out to specify how action choice is affected or updated by reinforcement learning based on past experience, and Wickelgren???s decay function is jointly employed to predict human decision-making behavior.clos