609533 An Efficient Solution Strategy for Solving Large-Scale Dynamic Supply Chains Problems with Modular Production Units

Monday, November 16, 2020
Computing and Systems Technology Division (10) (PreRecorded+)
R. Cory Allen1,2, Styliani Avraamidou1, Sergiy Butenko3 and Efstratios N. Pistikopoulos1,2, (1)Texas A&M Energy Institute, Texas A&M University, College Station, TX, (2)Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, (3)Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX

Historically, industry has relied upon the age-old adage “the bigger it is, the better it is” for the construction and operation of supply chains. This, in turn, caused supply chains to be developed with large centralized production facilities as opposed to smaller decentralized production facilities. However, recently within certain sectors of the economy there has been a push towards smaller decentralized production facilities that are comprised of modular production units [1]. These modular production units are transportable and can be reallocated within the supply chain to absorb the spatial and temporal changes in raw material availabilities, product demands, and market prices [2,3].

In this work, we present a mixed-integer linear programming (MILP) model for the dynamic supply chain problem with modular production units based upon our previous work [4]. The model includes raw material sources, production facilities, which contain storage units and modular production units, and product demand sinks. The problem is to determine how raw material, products, and the modular production units are routed through the supply chain. In an effort to solve large-scale industrial problems in a time efficient manner, we derive valid inequalities as well as a greedy and local heuristics. The valid inequalities tighten the linear programming relaxation and the heuristics are able to generate good feasible solutions quickly. We then integrate the aforementioned ideas into a branch-and-cut framework. We illustrate the effectiveness of our framework with a computational case study on randomly generated test instances and an industrial case study centering in the Permian Basin.

References:

  1. Baldea, M., Edgar, T. F., Stanley, B. L., & Kiss, A. A. (2017). Modular manufacturing processes: Status, challenges, and opportunities. AIChE journal, 63(10), 4262-4272.
  2. Allman, A., & Zhang, Q. (2020). Dynamic location of modular manufacturing facilities with relocation of individual modules. European Journal of Operational Research.
  3. Allen, R. C., Allaire, D., & El-Halwagi, M. M. (2018). Capacity planning for modular and transportable infrastructure for shale gas production and processing. Industrial & Engineering Chemistry Research, 58(15), 5887-5897.
  4. Allen, R. C., Avraamidou, S., & Pistikopoulos, E. N. (2020). Production Scheduling of Supply Chains Comprised of Modular Production Units. IFAC Proceedings Volumes, Accepted for Publication.

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