Abstract
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.
Highlights
Rivalry economic circumstances have compelled manufacturing industries to take advantage of modern Information Technology (IT) to reinforce their manufacturing processes’ capabilities and enhance their profitability (Kang et al, 2016; Lasi et al, 2014)
This paper significantly focuses on handling the new service requests in manufacturing and logistics service composition problem in the pre-specified time-intervals that occur while solving the problem
They tackled prevailing deficiencies in Cloud manufacturing (CMfg) service-composition methods, which can be summarized as lack of flexibility regarding dynamic situations such as newly-acquired services or operations, service failures, changes in Quality of Service and so, that may happen during the scheduling and assignment process, complications in handling large-sized problems, and the service domain features that can impact the performance of service-composition like correlation, similarity and so
Summary
Rivalry economic circumstances have compelled manufacturing industries to take advantage of modern Information Technology (IT) to reinforce their manufacturing processes’ capabilities and enhance their profitability (Kang et al, 2016; Lasi et al, 2014). Considering the emergence of new manufacturing paradigms such as Cloud manufacturing and Industry 4.0 has resulted in virtualized factories and outsourcing mechanisms (Tao et al, 2017; Wang et al, 2019; Li et al, 2010) These paradigms are realized in acquiring services, which have shaped the new business models of manufacturing systems and transformed them from process/ product-oriented systems to service-oriented ones (Saldivar et al, 2015; Delaram & Valilai, 2018; Wu et al, 2013). This enables clients to access a wide range of services to select from and compose them to fit best their requirements (Saldivar et al, 2015; Qi & Tao, 2019) Since this service composition problem is discussed as NP-hard (Delaram & Valilai, 2018), heuristic and metaheuristic methods are strongly preferred to obtain the optimal or nearly optimal solutions in a short solving time. The paper has proposed an innovative solution for solving time with particular attention to large-sized problems and the necessity for re-scheduling the newly received service requests
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
