QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system

International Journal of Production Research

et al. 2018

211

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.

Section: Service Compositionmentioning

confidence: 99%

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

Liu

International Journal of Production Research

et al. 2018

211

“…Kara et al [24] Impact assessment of global manufacturing on the embodied energy of products Xiang et al [25] Energy consumption aware service composition and optimal-selection in CMfg Tao et al [26] Energy-aware optimal scheduling of computing resources Utility…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, only the ants with valid routes are selected to update the pheromone concentration, while the ants with invalid routes are eliminated. In other words, we modify Formula (25) to Formula (32).…”

Section: The Aco With a Selection Mechanism (Acos)mentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Int J Adv Manuf Technol

Kang

et al. 2015

148

Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

“…Huang B. et al proposed a new chaos control optimal algorithm to solve the quality of service (QoS)-based service composition selection [10]. Xiang F. proposed furtherly a service optimization model-based QoS and energy consumption and given a group leader algorithm to solve it [11]. F. Tao et al designed a five-layered structure resource intelligent perception and access system based on IoT [12].…”

Section: Introductionmentioning

confidence: 99%

Agent-based manufacturing service discovery method for cloud manufacturing

Guo

Int J Adv Manuf Technol

Kang

et al. 2015

The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.