Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching
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...
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doaj-1f2c2683674441448320a14928d0fb442021-04-25T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922021-04-017e46110.7717/peerj-cs.461Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matchingSeyed Ali Sadeghi Aghili0Omid Fatahi Valilai1Alireza Haji2Mohammad Khalilzadeh3Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Mathematics & Logistics, Jacobs University Bremen, Bremen, Bremen, GermanyDepartment of Industrial Engineering, Sharif University of Technology, Tehran, Tehran, IranDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranRecently, 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.https://peerj.com/articles/cs-461.pdfCloud manufacturingXaaSService composition problemIndustry 4.0Reinforcement learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seyed Ali Sadeghi Aghili Omid Fatahi Valilai Alireza Haji Mohammad Khalilzadeh |
spellingShingle |
Seyed Ali Sadeghi Aghili Omid Fatahi Valilai Alireza Haji Mohammad Khalilzadeh Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching PeerJ Computer Science Cloud manufacturing XaaS Service composition problem Industry 4.0 Reinforcement learning |
author_facet |
Seyed Ali Sadeghi Aghili Omid Fatahi Valilai Alireza Haji Mohammad Khalilzadeh |
author_sort |
Seyed Ali Sadeghi Aghili |
title |
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
title_short |
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
title_full |
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
title_fullStr |
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
title_full_unstemmed |
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
title_sort |
dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching |
publisher |
PeerJ Inc. |
series |
PeerJ Computer Science |
issn |
2376-5992 |
publishDate |
2021-04-01 |
description |
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. |
topic |
Cloud manufacturing XaaS Service composition problem Industry 4.0 Reinforcement learning |
url |
https://peerj.com/articles/cs-461.pdf |
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