Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments

Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments

Maria Leonilde R. Varela, André S. Santos, Ana M. Madureira, Goran D. Putnik, Maria Manuela Cruz-Cunha
Copyright: © 2014 |Volume: 6 |Issue: 3 |Pages: 19
ISSN: 1938-0194|EISSN: 1938-0208|EISBN13: 9781466657427|DOI: 10.4018/IJWP.2014070103
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MLA

Varela, Maria Leonilde R., et al. "Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments." IJWP vol.6, no.3 2014: pp.33-51. http://doi.org/10.4018/IJWP.2014070103

APA

Varela, M. L., Santos, A. S., Madureira, A. M., Putnik, G. D., & Cruz-Cunha, M. M. (2014). Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments. International Journal of Web Portals (IJWP), 6(3), 33-51. http://doi.org/10.4018/IJWP.2014070103

Chicago

Varela, Maria Leonilde R., et al. "Collaborative Framework for Dynamic Scheduling Supporting in Networked Manufacturing Environments," International Journal of Web Portals (IJWP) 6, no.3: 33-51. http://doi.org/10.4018/IJWP.2014070103

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Abstract

Scheduling continues to play an important role in manufacturing systems. It enables the production of suitable scheduling plans, considering shared resources between several different products, through several manufacturing environments including networked ones. High levels of uncertainty characterize networked manufacturing environments. Processes have specific and complex requirements and management requisites, along with diversified objectives, which are dynamic and often conflicting. Dynamic adaptation and a real-time response for manufacturing scheduling is still possible and is critical in this new manufacturing environments, which have a flexible nature, where disturbances on working conditions occur on a continuous and even unexpected basis. Therefore, scheduling systems should have the ability of automatically and intelligently maintain a real-time adaptation and optimization of orders production, to effectively and efficiently adapt these manufacturing environments to the inherent dynamic of markets. In this paper a collaborative framework for supporting dynamic scheduling in networked manufacturing environments is proposed, based on a hyper-organization model and on hyper-heuristics, in order to obtain feasible and robust scheduling plans.

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