The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing

Ahn, Gilseung; Park, You‐Jin; Hur, Sun

doi:10.3390/su8121239

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…where Table 3, circled by a bold blue rectangle). e rankings of Z 1 , Z 2 , Z 3 , and Z 4 with regard to χ t [g] [1] are 3, 1, 3, and 2, respectively, and the fitness value is calculated as (1/μ(3, 1, 3, 2)) � 0.444 (see the first row of Table 3, delineated by a dashed red rectangle). Note that the solution with a smaller objective function value takes a higher ranking because they are to be minimized.…”

Section: Chromosome Representationmentioning

confidence: 99%

“…We randomly generate six situations where 5, 10, 15, 20, 25, and 30 tasks are uploaded at a day, and we calculate search time to establish the schedules in each situation. We assume that every task is uploaded at t, and D t i , C t i , Q t i , and R t i are uniformly distributed in the intervals [30,60] [1,3], respectively. e costs of hardware and software services are assumed to be different from each other; that is, c k, v of software and hardware service is Step 1.…”

Section: Efficiency Verificationmentioning

confidence: 99%

“…Cloud manufacturing (CM) is an advanced model that provides a cloud-based manufacturing platform on which enterprises virtualize and share their manufacturing services such as welding, milling, and machining to produce highly customized products [1][2][3]. A task to produce a customized product consists of several activities, and each activity provides a related manufacturing service.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Ahn

Hur

2021

Mathematical Problems in Engineering

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In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

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Section: Chromosome Representationmentioning

confidence: 99%

Section: Efficiency Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Ahn

Hur

2021

Mathematical Problems in Engineering

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“…DF i,j , CS i,j , BS i,j 0.8540 C 10 DF i,j , CS i,j , QU i,j 0.7149 C 11 DF i,j , BS i,j , ME i,j 0.4255 C 12 NP i,j , MS i,j , SD i,j 0.9206 C 13 NP i,j , MS i,j , TC i,j 0.8757 C 14 NP i,j , MA i,j 0.4294 C 15 NP i,j , CS i,j , TC i,j 0.8521 C 16 NP i,j , ME i,j 0.8038 C 17 MS i,j , SD i,j , RU i,j 0.9946 C 18 MS i,j , RS i,j , TC i,j 0.1193 C 19 MS i,j , QU i,j 0.8209 C 20 MS i,j , TC i,j , RU i,j 0.5309 C 21 MA i,j , PE i,j 0.9671 C 22 MA i,j , RU i,j 0.6952 C 23 OD i,j , RU i,j 0.2307 C 24 SD i,j , TE i,j 0.7174 C 25 BS i,j , ME i,j , TE i,j 0.6907 C 26 RS i,j , ME i,j , TE i,j 0.8250 C 27 ME i,j , PE i,j 0.9906 …”

Section: Cliquementioning

confidence: 99%

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

2017

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Cloud manufacturing (CM) is an emerging manufacturing model based on collaboration among manufacturing enterprises in a cloud computing environment. Naturally, collaboration is one of main factors that impacts performance in a variety of ways such as quality, lead time, and cost. Therefore, collaboration levels should be considered when solving operational issues in CM. However, there has been no attempt to estimate these levels between enterprises participating in CM. The collaboration level among enterprises in CM is defined as the ability to produce a manufacturing service that satisfies a customer by means of collaborative production amongst enterprises. We measure it as the conditional probability that collaborative performances are high given collaborative performance factors (e.g., resource sharing, information sharing, etc.). In this paper, we propose a framework for estimating collaboration levels. We adopt a probabilistic graphical model (PGM) to develop the framework, since the framework includes a lot of random variables and complex dependencies among them. The framework yields conditional probabilities that two enterprises will reduce the total cost, improve resource utilization or quality through collaboration between them given each enterprise's features, collaboration possibility, and collaboration activities. The collaboration levels the proposed framework yields will help to handle diverse operational problems in CM.

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“…In general, the attributes or criteria, which have been considered in previous works, could be divided into: cost-related indices [5,16,18,19], time-related indices [5,15,16,18,19], quality-related indices [5,16], risk-related indices [5], reliability-related indices [5,18,19], trust related indices [5,20], environment-related indices [21]. In addition, some others focus on the factors that can reflect the characteristics of cloud manufacturing, such as demand loss probability [22], correlation [23], and tolerance design [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

Jia

Zong³

et al. 2019

Sustainability

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Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.

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The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing

Cited by 6 publications

References 26 publications

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

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