Co-Optimization of Supply Chain Reconfiguration and Assembly Process Planning for Factory-in-a-Box Manufacturing

Author:

Nwodu Arriana1,Pasha Junayed2,Jiang Zhengqian1,Guo Weihong3,Dulebenets Maxim2,Wang Hui1,Minor Kayla1

Affiliation:

1. Department of Industrial and Manufacturing Engineering, Florida A&M University-Florida State University College of Engineering, Tallahassee, FL 32310

2. Department of Civil and Environmental Engineering, Florida A&M University-Florida State University College of Engineering, Tallahassee, FL 32310

3. Department of Industrial and Systems Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854

Abstract

Abstract Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services.

Funder

Directorate for Engineering

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

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