Full-Scale Discrete Event Simulation of an Automated Modular Conveyor System for Warehouse Logistics

Ashrafian, Alireza; Pettersen, Ole-Gunnar; Kuntze, Kristian Nordberg; Franke, Jacob D.; Alfnes, Erlend; Henriksen, Knut F.; Spone, Jakob

doi:10.1007/978-3-030-29996-5_4

Cited by 17 publications

(6 citation statements)

References 5 publications

(5 reference statements)

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“…Eq ( 6 ) is then transformed into a matrix to obtain the motion differential equation of the whole system, as follows [ 28 ]: …”

Section: Methodsmentioning

confidence: 99%

Influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors

Yao

Bi-sheng

2020

PLoS ONE

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The implementation of multiple drives for belt conveyors can solve the problems associated with motor overpower and the excessive tension of conveyor belts powered by a single drive. However, multiple drives can suffer from uneven driving power allocation. Among various factors, the selection of the type of conveyor belt particularly affects the power allocation. The current study aims to investigate the influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors. Based on the Kelvin-Voigt viscoelastic model, a discrete model of an entire machine is established. Kelvin-Voigt software is used to simulate the working conditions of conveyor belts with different elastic moduli under full loads. The driving forces of individual rollers are obtained and then compared. Compared to other types of belts, a steel wire core conveyor belt, whose elastic modulus is relatively high, effectively improves the stability of the conveyor belt under a full load after start-up to achieve a reasonable power allocation. The results of this study provide a foundation for conveyor belt selection for multi-drive conveyors.

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“…Eq ( 6 ) is then transformed into a matrix to obtain the motion differential equation of the whole system, as follows [ 28 ]: …”

Section: Methodsmentioning

confidence: 99%

Influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors

Yao

Bi-sheng

2020

PLoS ONE

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“…The approach can virtually represent the dynamic characteristics of the logistics model in terms of function and cost and can also accurately capture the complex behavior and changes in the model. The simulated model can be revised through data collected from the IoT devices, thereby improving the performance of the system, and increasing cost effectiveness [28]. For example, Kim [29] set up the optimal warehouse interception and pick-up time based on customer order response capabilities and priority-based job scheduling, using a streamlined operating model to help warehouse managers make optimal decisions.…”

Section: Digital Twins and Smart Warehouse Managementmentioning

confidence: 99%

Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries

Tang

Lau

et al. 2022

Machines

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In the context of the global economic slowdown, demand forecasting, and inventory and production management have long been important topics to the industries. With the support of smart warehouses, big data analytics, and optimization algorithms, enterprises can achieve economies of scale, and balance supply and demand. Smart warehouse and manufacturing management is considered the culmination of recently advanced technologies. It is important to enhance the scalability and extendibility of the industry. Despite many researchers having developed frameworks for smart warehouse and manufacturing management for various fields, most of these models are mainly focused on the logistics of the product and are not generalized to tackle the specific manufacturing problem facing in the cyclical industry. Indeed, the cyclical industry has a key problem: the big risk which high sensitivity poses to the business cycle and economic recession, which is difficult to foresee. Despite many inventory optimization approaches being proposed to optimize the inventory level in the warehouse and facilitate production management, the demand forecasting technique is seldom focused on the cyclic industry. On the other hand, management approaches are usually based on the complex logistics process instead of integrating the inventory level of the stock, which is very crucial to composing smart warehouses and manufacturing. This research study proposed a digital twin framework by integrating the smart warehouse and manufacturing with the roulette genetic algorithm for demand forecasting in the cyclical industry. We also demonstrate how this algorithm is practically implemented for forecasting the demand, sustaining manufacturing optimization, and achieving inventory optimization. We adopted a small-scale textile company case study to demonstrate the proposed digital framework in the warehouse and demonstrate the results of demand forecasting and inventory optimization. Various scenarios were conducted to simulate the results for the digital twin. The proposed digital twin framework and results help manufacturers and logistics companies to improve inventory management. This study has important theoretical and practical significance for the management of the cyclical industry.

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“…In addition, if this is made completely data-driven, a cost-effective approach is given to increase performance. This is the future of a stable standalone system of decision support enabled by dynamic digital twin recreations [55].…”

Section: Application Of Digital Twin Technology In the Warehouse Oper...mentioning

confidence: 99%

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

et al. 2021

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Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

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Full-Scale Discrete Event Simulation of an Automated Modular Conveyor System for Warehouse Logistics

Cited by 17 publications

References 5 publications

Influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors

Influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors

Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

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