Service level management of nonstationary supply chain using direct neural network controller

Yoo, Jang Sun; Hong, Seong Rok; Kim, Chang Ouk

doi:10.1016/j.eswa.2008.02.005

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…Examples include service level management of non-stationary supply chain using NN controller (Yoo et al 2009), application of NNs for supply chain demand forecasting (Carbonneau et al 2008), modular NNs for recursive collaborative forecasting in the service chain (Stubbings et al 2008) and comparison of NNs and support vector machines in suppliers' selection (Guosheng & Guohong 2008).…”

Section: Neural Network In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Neural Network In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Jung [26] established a SOA-based business alliance platform to discover, select, and compose resource services to fulfil a sub-task. Yoo et al [27] estimated a direct neural network controller to monitor the resource service of customer demands in a closed loop chain. Li [28] researched a combination method based on multi-granularity resource workflow to calculate the resources composition by business process and activity instance.…”

Section: Mrco-nmmentioning

confidence: 99%

“…In this paper, weighting method is used to convert the multi-objective to single objective for Pareto optimal solution. Because the different dimension of C; T; R; S, the non-dimensional fitness is evaluated by formula (26) with the constraint of formula (27). Variables array f max ðobj 1 Þ, f min ðobj 1 Þ, f max ðobj 2 Þ, f min ðobj 2 Þ, f max ðobj 3 Þ, f min ðobj 3 Þ, f max ðobj 4 Þ, f min ðobj 4 Þ in formula (26) are represent the 4-tuples C; T; R; S Maximum and Minimum of the Net, which are calculated by choosing MR for the Net with the highest and lowest use cost, trading period, credibility and consumption, respectively.…”

Section: Fitness Evaluationmentioning

confidence: 99%

Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Guo¹,

Du²,

Zhao³

et al. 2015

Mechatronics

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“…Li, Kramer, Beulens, and Van Der Vorst (2010) presented a novel framework for early warning and proactive control systems that combine expert knowledge and data mining methods to exploit recorded data. Yoo, Hong, and Kim (2009) proposed a closed loop supply chain control based on a direct neural network controller for the service-level management. In this paper, we study the influence of recycling materials' disturbance and e-channel uncertain lead time disruption (Hua, Wang, & Cheng, 2010) to the quality cycle chain consisting retail channel and e-channel, and how an EWS could be adopt to mitigate the risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Cycle quality chain early warning network with e-channel lead time disruption

Fang

Xiao

2014

International Journal of Systems Science: Operations & Logi

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We established a dual-channel cycle quality chain early warning network to improve the supply chain operation reference model. For a static early warning network, this paper analysed the cost account of a cycle quality chain under the network structure of positive and negative closed-loop control, as well as the structure of retail channel and e-channel control. In addition, an early warning security mechanism was proposed to handle the dynamic early warning network of a dual-channel cycle quality chain under the disturbance of e-channel lead time. Meanwhile, customised early warning security rules were defined to deal with different patterns of market environment with lead time disturbance. To verify the modelling framework and algorithm, we provided solutions to some examples of numerical cycle quality early warning network, which demonstrated that the disturbances have been relieved and controlled effectively.

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Service level management of nonstationary supply chain using direct neural network controller

Cited by 18 publications

References 20 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Cycle quality chain early warning network with e-channel lead time disruption

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