Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Liang, Huagang; Wen, Xiaoqian; Liu, Yongkui; Zhang, Haifeng; Zhang, Lin; Wang, Lihui

doi:10.1016/j.rcim.2020.101991

Cited by 96 publications

(29 citation statements)

References 31 publications

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“…Liang et al introduced PD-DQN—a Deep Reinforcement Learning (DRL) algorithm—which utilizes the basic Deep Q-Network (DQN) in the Cloud manufacturing environment, together with the dueling architecture, as well as the prioritized replay mechanism ( Liang et al, 2021 ). In their approach, the Quality of Service and transportation are also regarded.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Aghili

Valilai

Haji

et al. 2021

PeerJ Computer Science

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Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Aghili

Valilai

Haji

et al. 2021

PeerJ Computer Science

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“…Manufacturing process flexibility refers to the ability to produce different types of products in the same manufacturing plant or production line, while the essence of information system flexibility is that the system can be modularized, and the functions of each system can be assembled and disassembled with these modules to meet the changing needs, support customers to participate in the whole process, improve production efficiency, reduce operating costs and resource waste [29]. The key technologies of Industry 4.0 allow dynamic sharing and provide additional manufacturing services in cloud manufacturing environment, which greatly improves the quality of logistics services [2]. Intelligent cloud manufacturing platform including service modeling and dynamic matching module, intelligent resource allocation optimization module and decision support module can improve the resource efficiency of additional manufacturing services [30].…”

Section: Flexibility Of Smismentioning

confidence: 99%

“…Professor Sun [1] pointed out that Service-oriented Manufacturing (SOM) is to achieve highly collaborative, innovative, flexible and efficient distribution and its core competitiveness through the integration of products and services, the integration of IT/IS, the whole process of customer participation and the mutual provision of production services and service production by enterprises. With the emergence of new technologies such as cloud computing, Internet of Things (IOT), big data, artificial intelligence(AI), digital factory and "5G+ Industrial Internet", many changes and new development directions of manufacturing industry have been triggered [2] [3] [4]. IM integrates the data of design, production, warehousing and logistics in the manufacturing process by means of digitalization and informationization, and optimizes the business process, production process and service process by relying on the changes of real-time data flow, thus assisting decision-making and realizing the optimal allocation of resources.…”

Section: Introductionmentioning

confidence: 99%

Customization and Performance of Service-Oriented Manufacturing Information System: The Mediating Effect of Information System Flexibility

Niu¹

2021

IIM

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The purpose of this paper is to provide empirical evidence for the validity of the relationship between service-oriented manufacturing information system (SMIS) customization and performance from three aspects: data flexibility, process flexibility and system flexibility. We select some questionnaires from the third round of High-performance manufacturing (HPM) data to construct the construct, verify the reliability and validity of the construct, extract principal components, and analyze the mediating effect by using multiple chain linear regression and structural equation model. The results show that SMIS customization has a significant impact on its performance, and this effect works through its flexibility. More specifically, it is the multiple chain mediation effect composed of data flexibility, process flexibility and system flexibility. The importance of SMIS customization and flexibility to the organization is made clear, which helps practitioners understand the internal mechanism that affects SMIS performance, so as to use limited resources to improve system performance.

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“…To verify the effectiveness of the suggested models and solution algorithms, the production of automobile engine parts including valve (VAL), Exhaust Gas Re-circulation (EGR) passage, crankcase (CRK), gear housing (GHS), and oil pan (OIP) from reference [27] is used an example. The manufacturing task is decomposed into five subtasks.…”

Section: Case Study For a Small Examplementioning

confidence: 99%

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

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Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

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Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Cited by 96 publications

References 31 publications

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Customization and Performance of Service-Oriented Manufacturing Information System: The Mediating Effect of Information System Flexibility

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

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