Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection

With the growth and adoption of global supply chains and internet technologies, warehouse operations have become more demanding. Particularly, the number of orders being processed over a given time frame is drastically increasing, leading to more work content. This makes operational tasks, such as material retrieval and storage, done manually more inefficient. To improve system-level warehouse efficiency, collaborating Autonomous Vehicles (AVs) are needed. Several design challenges encompass an AV, some critical aspects are navigation, path planning, obstacle avoidance, task selection decisions, communication, and control systems. The current study addresses the warehouse task selection problem given a dynamic pending task list and considering multiple attributes: distance, traffic, collaboration, and due date, using situational decision-making approaches. The study includes the design and analysis of two situational decision-making approaches for multi-attribute dynamic warehouse task selection: Deep Learning Approach for Multi-Attribute Task Selection (DLT) and Situation based Greedy (SGY) algorithm that uses a traditional algorithmic approach. The two approaches are designed and analyzed in the current work. Further, they are evaluated using a simulation-based experiment. The results show that both the DLT and SGY have potential and are effective in comparison to the earliest due date first and shortest travel distance-based rules in addressing the multi-attribute task selection needs of a warehouse operation under the given experimental conditions and trade-offs

A method for the design of lean human-robot interaction

The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased complexity, thus higher costs. On the other hand, the effectiveness and performance of such systems decrease, explaining the unfulfilled potential of robotcs in sectors such as intralogistics, where the benefits of a robotic solution rarely justify its costs. Taking the distance from the false idea that a task should be either fully automated, or fully manual, this aper presents a method for design of a lean human-robot interaction (HRI) withe the objective of the "right level of automation", where functions are divided among human and automated agends, so that the overall process gains in performances and/or costs. ... The 10 progressive steps of the method are presented and discussed with reference to their graphical tool: the House of Quality Interaction