Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturingand distribution centers. The modelling of the retrieval– travel time of an AS/RS (expected product delivery time) ispractically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RShas emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses anoperator or a single machine for storage operations, and uses a combination between the free-fall movement and a transportconveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expectedretrieval– travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which isbased on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results showthat the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracyof our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess thesystem throughput, and evaluate different storage policies.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.