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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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Autores principales: Sadeghi Aghili, Seyed Ali, Fatahi Valilai, Omid, Haji, Alireza, Khalilzadeh, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080429/
https://www.ncbi.nlm.nih.gov/pubmed/33981835
http://dx.doi.org/10.7717/peerj-cs.461
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author Sadeghi Aghili, Seyed Ali
Fatahi Valilai, Omid
Haji, Alireza
Khalilzadeh, Mohammad
author_facet Sadeghi Aghili, Seyed Ali
Fatahi Valilai, Omid
Haji, Alireza
Khalilzadeh, Mohammad
author_sort Sadeghi Aghili, Seyed Ali
collection PubMed
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.
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spelling pubmed-80804292021-05-11 Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching Sadeghi Aghili, Seyed Ali Fatahi Valilai, Omid Haji, Alireza Khalilzadeh, Mohammad PeerJ Comput Sci Adaptive and Self-Organizing Systems 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. PeerJ Inc. 2021-04-23 /pmc/articles/PMC8080429/ /pubmed/33981835 http://dx.doi.org/10.7717/peerj-cs.461 Text en © 2021 Sadeghi Aghili et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Adaptive and Self-Organizing Systems
Sadeghi Aghili, Seyed Ali
Fatahi Valilai, Omid
Haji, Alireza
Khalilzadeh, Mohammad
Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching
title 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_short 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
topic Adaptive and Self-Organizing Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080429/
https://www.ncbi.nlm.nih.gov/pubmed/33981835
http://dx.doi.org/10.7717/peerj-cs.461
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