DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

Zhou, Jiajun; Yao, Xifan

doi:10.1007/s00170-016-9455-x

Cited by 52 publications

(22 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamical variations of service availability are considered. Zhou and Yao (2017a) proposed a context-aware artificial bee colony (ABC) algorithm based on the principle of ABC and service features in cloud manufacturing, which takes into account dynamics of trust QoS.…”

Section: Service Compositionmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

215

View full text Add to dashboard Cite

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

show abstract

Section: Service Compositionmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

215

View full text Add to dashboard Cite

show abstract

“…These works have furthered the research on SCOS problems in CMfg. However, they are inefficient at solving large-scale SCOS problems because they do not consider the SDFs (Zhou and Yao 2017).…”

Section: Related Workmentioning

confidence: 99%

“…There are two types of QoS attributes: positive and negative (Zhou and Yao 2017). Positive attributes mean that the higher the value of the QoS criteria, the better the quality of the MS, for example, availability and reliability.…”

Section: Problem Description and Mathematical Modelmentioning

confidence: 99%

“…Finally, a corresponding service for each subtask is selected to composite an optimal solution for satisfying user requests. Undoubtedly, building such a Quality of Service (QoS)-optimal solution is a typical multi-criterion nondeterministic polynomial (NP)-hard problem (Tao et al 2013;Zhou and Yao 2017).…”

Section: Introductionmentioning

confidence: 99%

“…These SDFs reflect the substantive characteristic and applied probability of services used to meet a certain type of requirement and strongly accelerate the solving of SCOS problems (Xu et al 2017). Unfortunately, SDFs have been used insufficiently and unreasonably to design algorithms for solving SCOS problems (Zhou and Yao 2017), and the solving of SCOS problems is still a key challenge (Zhang et al 2017a).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Wang

2019

J Intell Manuf

View full text Add to dashboard Cite

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service Composition and Optimal Selection (SCOS) is a key technique for efficient manufacturing service composition to create an on-demand Quality of Service (QoS) to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenging research area in CMfg. One effective approach for solving SCOS problems is to use Service Domain Features (SDF) through investigating the probability of services being used for a specific requirement from multiple angles. The approach can result in a division of service space, and then help narrow down the service space with largescale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. In doing so, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDFbased optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayesian theorem. The effectiveness of the proposed algorithm is validated through solving three real-world SCOS problems in a private CMfg.

show abstract

Single‐objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends

Hayyolalam

Pourghebleh

Chehrehzad

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

In recent years, cloud manufacturing (CMfg) has been developed as an intelligent manufacturing system, in which geographically distributed manufacturing resources are available as services in the cloud platform. Choosing and integrating single services into a combined service to fulfill the client's requests requires higher emphasis. However, by increasing customers' trend to utilize CMfg, service providers are encouraged to publish services with various functional and non-functional characteristics. Thus, service composition and optimal selection has become one of the most challenging topics in CMfg. Hence, an inclusive review of current studies on this NP-hard issue is extremely desirable. This article first, selects the recent studies in the field of single-objective service composition in CMfg and classifies and surveys them comprehensively in terms of QoS parameters, energy consumption, user constraint, and so forth. This article aims to provide a useful roadmap for future researchers who are intended to explore novel work in this field. The search for articles was conducted in November 2020.

show abstract

DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

Cited by 52 publications

References 52 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Single‐objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends

Contact Info

Product

Resources

About