Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Liu, Yongkui; Liang, Huagang; Xiao, Yingying; Zhang, Haifeng; Zhang, Jingxin; Zhang, Linlin; Wang, Lihui

doi:10.1016/j.rcim.2022.102323

Cited by 40 publications

(12 citation statements)

References 34 publications

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“…With the promotion of national industrial Internet applications in China, over 100 cloud manufacturing service platforms of different sizes and characteristics have emerged successively, such as Aerospace Cloud Network and Intelligent Cloud "iSESOL Cloud", which integrate a large number of service resources and transaction needs. At present, extensive research has been conducted on cloud manufacturing architecture [13,14], virtualization technology [15], manufacturing service combination, and service matching [16]. However, few studies have focused on low-carbon and green decision-making in cloud manufacturing.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Game Analysis of Providers’ and Demanders’ Low-Carbon Cooperation in Cloud Manufacturing Mode

Han,

Lu,

Zhang

et al. 2024

Sustainability

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The low-carbon cooperation between providers and demanders is one of the ways to achieve sustainable development in cloud manufacturing, which has become an important issue. However, the effective ways for the cloud platform to encourage such cooperation are unclear. Considering the low-carbon strategies of the supply and demand sides and the regulation of the cloud platform, an evolutionary game model involving service providers, service demanders, and the cloud platform is established, and the tripartite evolutionary stability is discussed. Further, the impacts of important factors, such as regulatory costs, on the tripartite strategies are analyzed through numerical simulation. The results illustrate that the cloud platform reasonably optimizes the rewards and penalties for low-carbon cooperation to promote the enthusiastic participation of service providers and demanders. The cloud platform can set penalties (rewards) for providers based on their low-carbon costs and rewards (penalties). Additionally, the low-carbon costs of service providers and the additional costs of demanders negatively affect the low-carbon cooperation between providers and demanders. Meanwhile, the low-carbon costs and additional costs for the providers and demanders to engage in low-carbon cooperation are affected by the rewards and penalties of the cloud platform. The results could provide insights into the game decisions of the supply and demand sides and the cloud platform, facilitating sustainable supply chain advancement.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Game Analysis of Providers’ and Demanders’ Low-Carbon Cooperation in Cloud Manufacturing Mode

Han,

Lu,

Zhang

et al. 2024

Sustainability

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“…To deal with extensive state and action space issues, we present DDPG to optimize the normalized computational cost of UAV. Despite the fact that DQN effectively solved issues in high-dimensional state spaces but continuous action spaces are still difficult to handle by DQN [64]. DDPG is proposed to expand DRL algorithms to continuous action spaces [65].…”

Section: A Mdp Modelmentioning

confidence: 99%

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

et al. 2023

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Advances in Unmanned Air Vehicle (UAV) technology have paved a way for numerous configurations and applications in communication systems. However, UAV dynamics play an important role in determining its effective use. In this article, while considering UAV dynamics, we evaluate the performance of a UAV equipped with a Mobile-Edge Computing (MEC) server that provides services to End-user Devices (EuDs). The EuDs due to their limited energy resources offload a portion of their computational task to nearby MEC-based UAV. To this end, we jointly optimize the computational cost and 3D UAV placement along with resource allocation subject to the network, communication, and environment constraints. A Deep Reinforcement Learning (DRL) technique based on a continuous action space approach, namely Deep Deterministic Policy Gradient (DDPG) is utilized. By exploiting DDPG, we propose an optimization strategy to obtain an optimal offloading policy in the presence of UAV dynamics, which is not considered in earlier studies. The proposed strategy can be classified into three cases namely; training through an ideal scenario, training through error dynamics, and training through extreme values. We compared the performance of these individual cases based on cost percentage and concluded that case II (training through error dynamics) achieves minimum cost i.e., 37.75 %, whereas case I and case III settles at 67.25% and 67.50% respectively. Numerical simulations are performed, and extensive results are obtained which shows that the advanced DDPG based algorithm along with error dynamic protocol is able to converge to near optimum. To validate the efficacy of the proposed algorithm, a comparison with state-of-the-art Deep Q-Network (DQN) is carried out, which shows that our algorithm has significant improvements.

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“…In addition, an increasing number of scholars have noticed that logistics planning is an important research issue in the cloud manufacturing environment (Liu et al, 2022;Zhou et al, 2020) network logistics planning and manufacturing service composition in the cloud environment (Aghamohammadzadeh et al, 2019). Zhou et al analyzed the logistics service scheduling problem in cloud manufacturing and proposed an optimization method to reduce the average delivery time from manufacturers to customers and to guarantee just-in-time service (Zhou et al, 2020).…”

Section: Cloud Manufacturing and Cloud Servicesmentioning

confidence: 99%

“…In addition, an increasing number of scholars have noticed that logistics planning is an important research issue in the cloud manufacturing environment (Liu et al ., 2022; Zhou et al ., 2020). For example, Aghamohammadzadeh et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

Wang

Hu²,

Wang³

2023

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PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

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Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Cited by 40 publications

References 34 publications

Evolutionary Game Analysis of Providers’ and Demanders’ Low-Carbon Cooperation in Cloud Manufacturing Mode

Evolutionary Game Analysis of Providers’ and Demanders’ Low-Carbon Cooperation in Cloud Manufacturing Mode

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

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