Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control problems are essentially optimization problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimization problem will vary in the different business and operation phases, it is important to think about optimization in a dynamic mechanism and in a number of interlinked sub-problems at the same time.Cloud manufacturing has the potential to offer decision support as a service and medium of communication in production planning and control. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.

Big data analytics in supply chain management: A state-of-the-art literature review

“…To be more specific, logistics/transportation, manufacturing, and warehousing domains are the major contributors of prescriptive analytics, thanks to the increasing adoption of various state-of-the-art systems such as CPS in Industry 4.0 Helo and Hao, 2017) and ITS . On the other hand, predictive analytics is still the primary actor in demand management and procurement, especially for demand forecasting and sourcing risk management, while prescriptive analytics is still rarely discussed (Ghedini Ralha and Sarmento Silva, 2012).…”

“…Product R&D Bae and Kim, 2011;Do, 2014;Lei and Moon, 2015;Opresnik and Taisch, 2015;Zhang et al, 2017 Production planning & control Chien et al, 2014;Krumeich et al, 2016;Zhang et al, 2017;Shu et al, 2016;Dai et al, 2012;Zhong et al, 2016;Helo and Hao, 2017 Quality management Krumeich et al, 2016;Zhang et al, 2017; Maintenance & diagnosis Shu et al, 2016;Guo et al, 2016;Kumar et al, 2016;Zhang et al, 2017;Wang et al, 2015 Warehousing Storage assignment Chuang et al, 2014;Li, Moghaddam, et al, 2016;Tsai and Huang, 2015;Chiang et al, 2011;Chiang et al, 2014 Order picking Ballestín et al, 2013;Chuang et al, 2014; Inventory control Alyahya et al, 2016;Hofmann, 2015;Huang and Van Mieghem, 2014;Lee et al, 2016;Stefanovic, 2015 Logistics and transportation Cui et al, 2016;Li et al, 2015;Shi and Abdel-Aty, 2015;StAubin et al, 2015;Toole et al, 2015;Xia et al, 2016;Yu and Abdel-Aty, 2014;Zangenehpour et al, 2015;…”

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

“…Cloud-manufacturing means that the principles of cloud computing will be transferred to manufacturing concerns, so related manufacturing resources are offered as services which lead to a network of exchanging needed resources and products. This application of DT can optimize processes which is shown in an example of sheet metal processing (Helo and Hao 2017). Frameworks for building a cloud manufacturing solution (Cheng et al 2016;Lu and Xu 2017) and the design of the network architecture (Škulj et al 2015) are presented and discussed.…”

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

“…The third group studied on service composition, evaluation, optimization algorithm, and cloud service prototype system. Taking sheet metal processing in cloud environment as an example, the minimum cost and maximum profit as the optimization objectives, and considering multiple related subfactors, P. Helo and Y. Hao [24] found a new optimization method for cloud-based production planning and control, proposed a cloud-based dynamic optimization model, and developed an optimization algorithm-based scheduling prototype system. V. B. Souza et al [25] studied the distributed service placement strategy in the mixed fogcloud scenarios, proposed a concurrent service execution scheme, and improved the service response time.…”

“…According to the above selection probability, the individual u is selected by roulette selection method and put into the population. P u is judged according to (24), and a new probability is given to the individual u.…”