(136a) Towards a Framework for Resilient-Aware Design of Distributed Manufacturing Networks

Distributed manufacturing networks are made up of small, geographically-distributed manufacturing facilities that are located near feedstock sources and/or demand zones [1]. They are typically used in scenarios where the feedstock or demand is decentralized across a large area, such as in biorefining [2]. Having production divided amongst multiple manufacturing facilities could help minimize performance loss during disasters because surviving facilities can take on additional production capacity during recovery. Therefore, there is incentive to design distributed manufacturing networks with the capability to resist disruptions in mind.

In this study, we proposed a framework for designing a distributed manufacturing network that considers its resilience against a specific disaster scenario. A finite set of potential facility sites with varying production capacities, as well as potential connections between supply and demand nodes are considered. A mixed-integer programming (MIP) model was developed to incorporate the aforementioned alternatives, as well as a disaster scenario that could occur at one or more potential site. A penalty on the annual net profit of the network was introduced to account for the time taken for the network to recover and level of lost performance due to the disaster scenario. The MIP was then optimized to achieve maximum profit given this penalty. This framework allows for the design of resilient distributed manufacturing networks using a metric that directly reflects economic performance. The case of methanol production from refuse-derived fuels is presented to illustrate the proposed framework.

[1] Srai, J.S., et al., Distributed manufacturing: scope, challenges and opportunities. International Journal of Production Research, 2016. 54(23): p. 6917-6935.

[2] Lopez-Molina, A., et al., An Integrated Approach to the Design of Centralized and Decentralized Biorefineries with Environmental, Safety, and Economic Objectives. Processes, 2020. 8(12): p. 1682.