A framework for supply chain optimization for modular manufacturing with production feasibility analysis. (February 2021)

• Record Type: Journal Article
• Authors: Bhosekar, Atharv; Ierapetritou, Marianthi
• Is Part Of: Computers & chemical engineering. Volume 145(2021)
• Publication Date: 2021-02
• Subjects: Economies of numbers -- Modular manufacturing -- Support vector machine -- Enterprise-wide optimization Feasibility analysis Machine learning; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107175
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: Modular manufacturing is gaining popularity in a variety of industrial applications due to potential cost-savings as well as process flexibility that can be achieved with the use of small and standardized modules. In this work, a framework for supply chain optimization is proposed that ensures production feasibility with the help of historical process data for individual process modules and machine learning-based feasibility analysis. A supply chain optimization problem is formulated where the binary variables represent facility locations, and the integer variables correspond to the number of modular equipment installed. Results demonstrate that the tradeoff between centralized and distributed manufacturing and the effect of economies of numbers on the cost of the supply chain can be studied by solving the problem.