Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

Wang, Tianri; Zhang, Pengzhi; Liu, Juan

doi:10.1016/j.asoc.2021.107737

Cited by 28 publications

(6 citation statements)

References 61 publications

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“…In previous researches various models and algorithms have been proposed, there are some researches that focoused on scheduling models for multi-objective or single-objective problems [16], or association analysis approach [17] and Colony based Optimization algorithms like Ant Colony. But there are less studies on CMfg dynamic scheduling [18]. Champati and Liang [19] proposed a huristic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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Cloud manufacturing (CMfg) is a new advanced manucatring model developed with the help of enterprise information technologies under the support of cloud computing, Internet of Things and service-based technologies. CMfg compose multiple manufacturing resources to provide efficient and valuable services. CMfg has a highly dynamic environment. In this environment, many disruptions or events may occur that lead the system to unplanned situations. In CMfg, a series of service providers are scheduled for production. During the production operation, some of them may be damaged, stopped, and out of service. Therefore, rescheduling is necessary for the continuation of the production process according to the concluded contracts and initial schedule. When any disruptions or other events occurred, the rescheduling techniques used to updating the inital schedule. In this paper, the dynamic rescheduling problem in CMfg is analyzed. Then the multi-objective rescheduling in CMfg is modeled and defined as a multi-objective optimization problem. Defining this problem as a multi-objective optimization problem provides the possibility of applying, checking and comparing different algorithms. For solving this problem, previous optimization methods have improved and a multi-objective and elitist algorithm based on the Jaya algorithm, called advanced multi-objective elitist Jaya algorithm (AMEJ) is proposed. Several experiments have been conducted to verify the performance of the proposed algorithm. Computational results showed that the proposed algorithm performs better compared to other multiobjective optimization algorithms.

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Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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“…But, when an alteration in the weights of the input variables or criteria results in a negligible change in the final ranking, then the model is robust to sensitivity analysis. Assessment of a change in weight of key QO factors of a model (i.e., Response Time, Availability, Storage, Security among others) would produce a significant variance in a decision making can verify the decision-making model as scientific (Wang et al, 2021). Given this, the sensitivity analysis was conducted in three ways; Scenario One, Scenario Two, and Scenario Three as shown in Fig.…”

Section: Sensitivity Analysismentioning

confidence: 99%

An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA

al.¹

2022

Int. j. adv. appl. sci.

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Quality of service (QoS) is a very important concept in service-oriented architecture (SOA). While there is a growing body of study on QoS-based service selection based on SOA, there is little research on analyzing QoS factors from the viewpoints of IT workers and researchers. As a result, the purpose of the current study aims to offer an integrated fuzzy VIKOR-TOPSIS-DEMATEL approach framework for evaluating QoS factors of online services from the viewpoint of experts in a fuzzy environment. A numerical assessment of the QoS factors for a case firm in Ghana indicated that the suggested technique is appropriate for the problem. Furthermore, the technique outcomes divided QoS factors into cause-effect variables, ranked QoS factors, and lastly, suggested conflicting QoS factors. The results from the Fuzzy DEMATEL aspect of the proposed approach found integrity, availability, accessibility, compliance, documentation, latency, and adaptability as causal variables. While response time, cost/price, reliability, performance, security, reputation, throughput, best practices, success ability, encryption, portability, storage, and consistency are regarded as influential variables. The Fuzzy TOPSIS aspect of the technique found adaptability, documentation, consistency, transaction, and accessibility are the most ranked QoS factors of online services. The fuzzy VIKOR side of the proposed method discovers integrity, cost, and latency as incommensurable QoS factors. Finally, a sensitivity analysis was carried out, and the results show the model is robust. This study confirms the position of existing knowledge on sensitivity analysis in the QoS literature. In the issue of QoS factor evaluation, this work effectively blended three MCDM techniques. The study's shortcoming stems from its reliance on data from QoS specialists from only one developing nation (i.e. Ghana).

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“…Cloud manufacturing is a service-oriented manufacturing paradigm which employs cloud computing technology to offer customisable manufacturing services. Cloud manufacturing-as-a-service (CMaaS) platforms virtualise physical resources as services in the cloud [26], supporting decision-making tasks such as service composition [1,43], selection and scheduling [39,45]. Despite significant research efforts, there are still implementation challenges.…”

Section: Background and Related Workmentioning

confidence: 99%

An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Scrimieri

Adalat

Afazov

et al. 2022

Int J Adv Manuf Technol

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Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.

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Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

Cited by 28 publications

References 61 publications

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA

An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

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