A Hybrid Discrete Imperialist Competition Algorithm for Fuzzy Job-Shop Scheduling Problems

Wang, Shuaiqun; Aorigele,; Liu, Guanjun; Gao, Shangce

doi:10.1109/access.2016.2645818

Cited by 27 publications

(8 citation statements)

References 24 publications

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“…As a novel paradigm, the core idea of fog computing is that the computation should be performed near to data source. Many researchers from home and abroad have begun studying in this field [18]- [22].…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

2020

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As an emerging computing model close to the end-user, fog computing can move tasks from the cloud to the fog device to process, and make up for the lack of cloud computing in terms of mobile support, delay and location-aware. Due to the less computing resources and weak processing ability of fog devices, the lightweight computing has been the primary choice, higher requirements are imposed for time and load. Therefore, in the Cloud-fog computing architecture, a new task scheduling method(I-FASC) is proposed for the characteristics of tasks and resources, including an improved firework algorithm (I-FA) is proposed by introducing the explosion radius detection mechanism of fireworks. By two sets of experiments show this method can better reduce the processing time of the task and ensure better overall load balancing of fog devices in a cloud-fog computing system. INDEX TERMS Cloud computing, fog computing, Internet of Things (IoT), task scheduling.

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Section: Related Workmentioning

confidence: 99%

Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

2020

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“…The optimization objective for environmental impacts is to reduce the total carbon emissions of maintenance service. Therefore, the optimization objective for environmental impacts is defined as follows: [44]. Function optimization problems of the manufacturing field have been typical application goals for these intelligent optimization algorithms [45].…”

Section: ) Objective Function For Predictive Maintenance Decisionmentioning

confidence: 99%

Integrated Intelligent Green Scheduling of Predictive Maintenance for Complex Equipment based on Information Services

Feng

Zheng

et al. 2020

IEEE Access

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As an important link to guarantee normal industrial production, equipment maintenance plays an increasingly key role in enhancing the competitiveness of enterprises and supporting green smart manufacturing. This paper aims to promote the implementation of predictive maintenance for complex equipment and improve the green performance of the maintenance service process. A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service. Subsequently, an integrated mathematical problem model that consists of carbon emission objective and extended maintenance cost objective is constructed. Then an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem. In response to deal with the uncertainties of maintenance service environment and inaccuracy of prediction, a data-driven dynamic adjustment strategy is applied. A grinding roll fault case of a large vertical is used to demonstrate the effectiveness of this proposed approach.

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“…For problems combining fuzzy durations and fuzzy due dates, the term agreement index was coined in [69] to refer to the degree to which a job's fuzzy completion time satisfies the flexible due-date, and a genetic algorithm was proposed to maximise the minimum agreement index across all jobs. Maximising the minimum agreement index is also the objective of a random-key genetic algorithm in [44], a scatter search method in [27], a hybrid discrete imperialist competition algorithm in [79] and a memetic algorithm in [61]. This memetic algorithm is also applied to maximise the average minimum index, which is also the objective of the co-evolutive method from [82], here for a fuzzy job shop with multi-process routes, and of the multiobjective genetic algorithm from [29], which also attempts to minimise the number of tardy jobs.…”

Section: Related Workmentioning

confidence: 99%

Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms1

Palacios

Rodríguez

Vela

et al. 2018

ICA

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This paper tackles the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. The objective is to achieve high-service level by maximising due-date satisfaction, considering two different overall satisfaction measures as objective functions. We show how these functions model different attitudes in the framework of fuzzy multicriteria decision making and we define a measure of solution robustness based on an existing a-posteriori semantics of fuzzy schedules to further assess the quality of the obtained solutions. As solving method, we improve a memetic algorithm from the literature by incorporating a new heuristic mechanism to guide the search through plateaus of the fitness landscape. We assess the performance of the resulting algorithm with an extensive experimental study, including a parametric analysis, and a study of the algorithm's components and synergy between them. We provide results on a set of existing and new benchmark instances for fuzzy job shop with flexible due dates that show the competitiveness of our method.

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A Hybrid Discrete Imperialist Competition Algorithm for Fuzzy Job-Shop Scheduling Problems

Cited by 27 publications

References 24 publications

Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

Integrated Intelligent Green Scheduling of Predictive Maintenance for Complex Equipment based on Information Services

Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms1

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