Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead

A major drawback of most automated warehousing solutions is that fixedly installed hardware makes them inflexible and hardly scalable. In the recent years, numerous robotized warehousing solutions have been innovated, which are more adaptable to varying capacity situations. In this paper, we consider robotized sorting systems where autonomous mobile robots load individual pieces of stock keeping units (SKUs) at a loading station, drive to the collection points temporarily associated with the orders demanding the pieces, and autonomously release them, e.g., by tilting a tray mounted on top of each robot. In these systems, a huge number of products approach the loading station with an interarrival time of very few seconds, so that we face a very challenging scheduling environment in which the following operational decisions must be taken in real time: First, since pieces of the same SKU are interchangeable among orders with a demand for this specific SKU, we have to assign pieces to suitable orders. Furthermore, each order has to be temporarily assigned to a collection point. Finally, we have to match robots and transport jobs, where pieces have to be delivered between loading station and selected collection points. These interdependent decisions become even more involved, since we (typically) do not posses complete knowledge on the arrival sequence but have merely a restricted lookahead of the next approaching products. In this paper, we show that even in such a fierce environment sophisticated optimization, based on a novel two-step multiple-scenario approach applied under real-time conditions, can be a serviceable tool to significantly improve the sortation throughput

SHIPBUILDING PRODUCTION PROCESS DESIGN METHODOLOGY USING COMPUTER SIMULATION

In this research a shipbuilding production process design methodology, using computer simulation, is suggested. It is expected from suggested methodology to give better and more efficient tool for complex shipbuilding production processes design procedure. Within the first part of this research existing practice for production process design in shipbuilding was discussed, its shortcomings and problem were emphasized. In continuing, discrete event simulation modelling method, as basis of suggested methodology, is investigated and described regarding its special characteristics, advantages and reasons for application, especially in shipbuilding production process. Furthermore, simulation modeling basics were described as well as suggested methodology for production process procedure. Case study of suggested methodology application for designing a robotized profile fabrication production process line is demonstrated. Selected design solution, acquired with suggested methodology was evaluated through comparison with robotized profile cutting production line installation in a specific shipyard production process. Based on obtained data from real production the simulation model was further enhanced. Finally, on grounds of this research, results and droved conclusions, directions for further research are suggested

Warehouse robotization with Wheel.me genius: A puzzle-based movable racks system

In this paper, we first introduce a new type of warehouse which combines the properties and operations of puzzle-based storage systems and robotic mobile fulfillment. Thanks to a long-term collaboration between the Logistics 4.0 Lab at NTNU and wheel.me, we have introduced and explored a new configuration called Puzzle-Based Movable Rack (PBMR) system where racks can be moved with autonomous wheels. One additional advantage of such system is that movable racks can move diagonally. We model and analyze the system studying different configurations and showing the impact on density and average cycle time/throughput. We finally introduce potential future research for such system

Simulating change propagation between product architecture and development organization.

International audienceIn order to limit the effects of technological change on product design, concepts like product architecture and modularity have been introduced, in order to support complex product development. In engineering design, numerous works have studied this central issue but change propagation within product architecture has been hardly addressed. Concerning organizational issues, many researchers in the field of industrial engineering have paid careful attention to new organization design but hardly to an incremental evolution of project organization. Galbraith (1977) highlighted that product architecture and development organization were strongly interrelated. However, little research has studied this relationship, and the need for a coherent model of product and organization co-evolution remains. This paper aims at presenting a matrix-based method that should help design managers to simulate change propagation between product architecture and development organization. This method uses a "management by uncertainty" approach and a mathematical model in order to propagate change. An industrial case study illustrates it in case of component changes

Robotized Warehouse Systems: Developments and Research Opportunities

Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses

Sorting with Robots: where to drop off the parcel?

This paper presents a method for assigning destinations to drop off points in robotic sorting systems, taking into account robot congestion

Lungs cancer nodules detection from ct scan images with convolutional neural networks

Lungs cancer is a life-taking disease and is causing a problem around the world for a long time. The only plausible solution for this type of disease is the early detection of the disease because at preliminary stages it can be treated or cured. With the recent medical advancements, Computerized Tomography (CT) scan is the best technique out there to get the images of internal body organs. Sometimes, even experienced doctors are not able to identify cancer just by looking at the CT scan. During the past few years, a lot of research work is devoted to achieve the task for lung cancer detection but they failed to achieve accuracy. The main objective of this piece of this research was to find an appropriate method for classification of nodules and non-nodules. For classification, the dataset was taken from Japanese Society of Radiological Technology (JSRT) with 247 three-dimensional images. The images were preprocessed into gray-scale images. The lung cancer detection model was built using Convolutional Neural Networks (CNN). The model was able to achieve an accuracy of 88% with lowest loss rate of 0.21% and was found better than other highly complex methods for classification

IMPROVABILITY OF THE FABRICATION LINE IN A SHIPYARD

The ship production process is a complex manufacturing system involving numerous working stations mutually interconnected by transport devices and buffers. Such a production system can be efficiently modeled using the stochastic system approach and Markov chains. Once formulated, the mathematical model enables analysis of the governing production system properties like the production rate, work-in-process, and probabilities of machine blockage and starvation that govern the production system bottleneck identification and its continuous improvement. Although the continuous improvement of the production system is a well-known issue, it is usually based on managerial intuition or more complex discrete event simulation yielding sub-optimal results. Therefore, a semi-analytical procedure for the improvability analysis using the Markov chain framework is presented in this paper in the case of the shipyard’s fabrication lines. Potential benefits for the shipyards are pointed out as the main gain of the improvability analysis