A Proposal of Resource Allocation Method Based on Combinatorial Double Auction Technique in Crowdsourced Manufacturing

2020 ◽  
Author(s):  
Daisuke Kokuryo ◽  
Yoshiaki Harada ◽  
Toshiya Kaihara ◽  
Nobutada Fujii
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Computational Experiment ◽  
Manufacturing System ◽  
Cloud Service ◽  
Smart Manufacturing ◽  
Double Auction ◽  
Allocation Method ◽  
Production Resources

Abstract With the development of the IoT (Internet of Things), smart manufacturing system with cloud service and computing techniques has gained worldwide attention. Crowdsourced manufacturing system is a production styles that connects among different companies and factories to share the production resources. In this system, it is important to distribute resources appropriately to increase the productivity. In this proceeding, a resource allocation method based on combinatorial double auction technique is proposed. In the computational experiment, a characteristic of the proposed resource allocation method is evaluated.

Internet of Things for Smart Manufacturing System: Trust Issues in Resource Allocation

2018 ◽  
Vol 5 (6) ◽  
pp. 4418-4427 ◽  
Author(s):  
Seohyeon Jeong ◽  
Woongsoo Na ◽  
Joongheon Kim ◽  
Sungrae Cho
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Manufacturing System ◽  
Smart Manufacturing

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

scholarly journals A cloud priority-based dynamic online double auction mechanism (PB-DODAM)

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
S. M. Reza Dibaj ◽  
Ali Miri ◽  
SeyedAkbar Mostafavi
Keyword(s):  
Resource Allocation ◽  
Service Providers ◽  
Supply And Demand ◽  
Cloud Service ◽  
Time Constraints ◽  
Double Auction ◽  
Auction Mechanism ◽  
Double Auctions ◽  
Auction Models ◽  
Scheduling Mechanisms

AbstractDouble auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.

scholarly journals IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Li-Nan Zhu ◽  
Peng-Hang Li ◽  
Xiao-Long Zhou
Keyword(s):  
New Product Development ◽  
Service Composition ◽  
Manufacturing System ◽  
Cloud Service ◽  
Optimization Method ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Parallel Hybrid ◽  
Manufacturing Service Composition ◽  
Cloud Service Composition

Cloud manufacturing (CMfg) is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on-demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

scholarly journals Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Han Zhong ◽  
Ruize Sun ◽  
Fengcheng Mei ◽  
Yong Chen ◽  
Fan Jin ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Resource Utilization ◽  
Smart Cities ◽  
Dynamic Resource Allocation ◽  
Smart Manufacturing ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Environment Model ◽  
Dynamic Resource

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.

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