Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 96, Issue -, Pages 462-478Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.10.059
Keywords
Dynamic Manufacturing Network; Big data analytics; Operational planning; Multi objective optimization; Fuzzy inference System; Reliability
Categories
Funding
- Portuguese Foundation for Science and Technology (FCT)through the MIT Portugal [SFRH/BD/33734/2009]
- North Portugal Regional Operational Programme (NORTE), under the PORTUGAL [NORTE-01-0145-FEDER-000020]
- European Regional Development Fund (ERDF)
- Fundação para a Ciência e a Tecnologia [SFRH/BD/33734/2009] Funding Source: FCT
Ask authors/readers for more resources
A Dynamic Manufacturing Network (DMN) is the manufacturing industry application of the Virtual Enterprise (VE) business model based on real time information sharing and process integration. DMNs are normally formed and supported by a collaborative platform previously designed and built by a preexisting strategic partnership. The collaborative platform forms and tracks each DMN through all phases of its life cycle which leads to the accumulation and storage of large historical datasets on partner and customer characteristics and actions. This data holds the key to customer and manufacturer behavioral patterns and performances that can further be used in the decision making processes. In this study, we have focused on tackling this widely neglected research opportunity, by integrating manufacturer, order and customer data and characteristics into DMN formation and planning. The developed big data analytics approach consists of TOPSIS, fuzzy inference system and multi objective optimization techniques. Initially, by integrating the TOPSIS multi criteria decision making technique with a fuzzy inference system (FIS) we have computed indices for Manufacturer reliability and Order priority. Then we developed a multi-objective mixed integer linear programming (MILP) model to generate efficient solutions minimizing cost and assigning more reliable manufacturers to orders with higher priority. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available