A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency

Qian, Cheng; Zhang, Yingfeng; Liu, Yang; Wang, Zhe

doi:10.1016/j.jclepro.2019.118379

Cited by 48 publications

(19 citation statements)

References 51 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This might be facilitated by a direct integration of E-commerce platforms when dealing with customised designs (Jia et al 2016). Technologically, this could either be achieved with block-chain technology to trace the product history (Mandolla et al 2019) or with cloud-based solutions that allow simultaneous access to product and process information (Qian et al 2019).…”

Section: Product Designmentioning

confidence: 99%

Additive manufacturing and supply chains – a systematic review

Kunovjanek

Knofius

Reiner

2020

Production Planning & Control

View full text Add to dashboard Cite

Section: Product Designmentioning

confidence: 99%

Additive manufacturing and supply chains – a systematic review

Kunovjanek

Knofius

Reiner

2020

Production Planning & Control

View full text Add to dashboard Cite

“…The literatures related to resource allocation can be mainly categorized into two perspectives. The first is optimization model, including resource service selection and production decision based on TCQ (time, cost, quality) [24], web service composition optimization for large-scale perceived service quality [25], service-oriented logistics resource scheduling [26], classification-based integration services and composition [27]. These works often lacks the full mining of customer needs, affecting the quality of service.…”

Section: Related Workmentioning

confidence: 99%

Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency

et al. 2021

View full text Add to dashboard Cite

Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods need to be changed: the products in GGRMS should be divided into multiple parts and different parts are treated in different ways to maximize residual value. However, this significantly increases the number of remanufacturing service activities and the complexity of the service activities network. Because a service activity may correspond to multiple service resources, the difficulty of service resources allocating significantly increase as the number of service activities under GGRMS increases. To improve the efficiency of resource matching, we proposed to first merge the redundant service activities in the service activity network, and then allocate the corresponding service resources. Therefore, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-Ⅱ). Finally, we, taking a retired manual gearbox as an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is effective for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of the RMS.

show abstract

“…(1) For any production linel ∈ L, if i and j are processed continuously, then the order completion time is formulated in (6); if iis the first order on l, then see equation (7).…”

Section: Order Delivery Redundant Time and Order Deliverymentioning

confidence: 99%

“…e sharing of manufacturing resources means that raw materials and semifinished products located at different suppliers and customers are transferred widely and frequently [6,7]. e scheduling of parallel batch production adding transportation operations should integrate tight time windows associated with rail transport plans to ensure that timesensitive orders are met.…”

Section: Introductionmentioning

confidence: 99%

Parallel Machine Production and Transportation Operations’ Scheduling with Tight Time Windows

Jiang

Xiong

et al. 2020

Complexity

View full text Add to dashboard Cite

This study addresses a parallel machine production and transportation operations’ scheduling problem with a tight time window associated with the transfer and delivery process. The orders located at the parallel machines need to be delivered to customers by train. Each order must be processed within a limited completion time in order for the product to be matched with the optimal trip to its destination within the delivery period. A mathematical analysis method is used to reveal the impact of tight time windows on the scheduling of production and transportation operations. The order transfer redundancy time and order transfer waiting time are employed to reflect the impact scheduling of production on the transfer process. The order delivery redundancy time and order delivery waiting time are used to describe delivery operations. The goal is to maximize the coordination level of order transfer and delivery, which are reflected in the order transfer time and the order delivery time, respectively. Additionally, a simulated annealing algorithm using the column generation technique was developed to solve this problem. The results show that the use of the system coordination model in this method obviously improves the number of successful transfers and deliveries.

show abstract

A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency

Cited by 48 publications

References 51 publications

Additive manufacturing and supply chains – a systematic review

Additive manufacturing and supply chains – a systematic review

Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency

Parallel Machine Production and Transportation Operations’ Scheduling with Tight Time Windows

Contact Info

Product

Resources

About