Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform

Wu, Qiao; Xie, Naiming; Zheng, Shaoxiang; Bernard, Alain

doi:10.1016/j.jmsy.2022.02.007

Cited by 33 publications

(11 citation statements)

References 45 publications

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“…Previous work usually studies collaboration elements related to cloud platforms from a single perspective, such as resource or business scheduling, and fails to examine the collaboration of the service value chain from a system perspective. Wu et al (2022),an online scheduling scenario is proposed, and a mixed integer linear programming model for multiple 3D printing tasks based on an additive manufacturing cloud platform is constructed to minimize the average cost per volume of material. conducted a system engineering analysis from three dimensions of product cycle, core service business and stakeholders, and proposed a collaborative service model, expounding the multi-stakeholder collaboration and multi-collaboration process.…”

Section: Intelligent Collection and Scheduling Inmentioning

confidence: 99%

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Gan

Zeng

Liu

et al. 2023

IJAHUC

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Power Load Monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed IoT(Internet of Things) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including Data Acquisition, Load Disaggregation, Load Forecasting and so on. Finally, combined with various optimization methods, future research directions are prospected, including data and network security issues, challenges faced by cloud/edge architecture, adaptive fine-grained Load Disaggregation, Load Forecasting.

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Section: Intelligent Collection and Scheduling Inmentioning

confidence: 99%

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Gan

Zeng

Liu

et al. 2023

IJAHUC

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“…Equation ( 9) is used to calculate the time for the mth device to replace the printing material. Equation (10) was the minimized completion time to print a job. Equation (11) represented the total production time of the device m when the requirement to be produced by device m consisted of a single job or multiple jobs.…”

Section: Constraint Condition Establishmentmentioning

confidence: 99%

“…From the perspective of methodology, since the 3DP workshop scheduling problem is an NP-hard problem with no fixed solution, different scholars have adopted different methods in the research on 3D printing scheduling. Wu et al [ 10 ] and Kucukkoc [ 6 ] adopted mixed-integer linear programming and a heuristic algorithm to optimize the scheduling problem in different situations. Zhou et al [ 11 ] established a task matching and scheduling model for distributed 3DP services in a cloud manufacturing environment and obtained the optimal solution by improving the genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this study attempts to develop an improved genetic algorithm (IGA) to solve the 3D printing shop scheduling problem. From the perspective of optimization goals, existing studies have focused on the minimization of cost or the maximization of profit [ 4 , 8 , 10 , 12 , 19 ], minimization of maximum completion time [ 2 , 6 – 8 , 14 , 15 ], and minimization of total energy consumption [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the “intelligent manufacturing” process of M-3DP mass customization.

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“…CMfg is customer-oriented manufacturing and in order to satisfy customer, the research papers focused on the influences of timeliness [5]. Wu et al [6] develop a mixed linear programming model of 3D printing based on additive manufacturing cloud platform to minimize average cost per volume of material. According to [7], a cloud-based system is capable of facilitating adaptive shop floor scheduling and condition-based maintenance.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Cloud-Based Scheduling Using Heavy Traffic Limit Theorem in Queuing Process

Rashidifar

Chen

Bouzary

et al. 2022

Lecture Notes in Mechanical Engineering

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Cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm that distributes resources in an on-demand business model. In the cloud manufacturing environment, scheduling is considered as an effective tool for satisfying customer requirements which has attracted attention from researchers. In this case, quality of service (QoS) in the scheduling plays a vital role in assessing the impacts of the distributed resources in operation on the performance of scheduling functions. In this paper, a queuing system is employed to model the scheduling problem with multiple servers and then scheduling in cloud manufacturing is classified based on various QoS requirements. Moreover, a set of heavy traffic limit theorems is introduced as a new approach to solving this scheduling problem in which different heavy traffic limits are provided for each of QoS-based scheduling classes. Finally, the number of operational resources in the scheduling is determined by considering the results obtained in the numerical analysis of the heavy traffic limit with different queue disciplines. The results show that different numbers of active machines in various QoS requirements classes play a vital role in that the required QoS metrics such as the expected waiting time and the expected completion time which are critical performance indicators of the cloud’s service are intimately related.

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Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform

Cited by 33 publications

References 45 publications

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

A Mathematical Model for Cloud-Based Scheduling Using Heavy Traffic Limit Theorem in Queuing Process

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