A two-phase MILP approach to integrate order, customer and manufacturer characteristics into Dynamic Manufacturing Network formation and operational planning

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Senay Sadic (Author) Other; Jorge Pinho de Sousa (Author) FEUP; José António Crispim (Author) Other

Journal

Title: Expert Systems with Applications

Vol. 96

Pages: 462-478

ISSN: 0957-4174

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 8 Citations

INSPEC

Other information

Authenticus ID: P-00N-KKR

DOI: 10.1016/j.eswa.2017.10.059

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17