Multiobjective Robust Optimization of Personalized Medicine Supply Chain under Transportation Time Uncertainty

Meng Wang, Songsong Liu, Yuxin Gao, Richard Allmendinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Currently, personalized medicine drugs have better therapeutic outcomes, but their large-scale delivery remains a challenge. With the changing social environment, supply chain network design and operations are facing greater uncertainties and risks. Therefore, how to design a supply chain network that can effectively cope with temporal uncertainty perturbations is highly crucial for the long-term and steady development of the industry. Thus, this work addresses the production and distribution of a personalized medical supply chain under transportation time uncertainty. A multiobjective optimization model for supply chains is developed considering three objectives simultaneously, including minimizing total cost and time, and maximizing demand coverage, and constraints, such as shelf life, order allocation, and transportation time. Meanwhile, a clustering-based algorithm is developed with prior allocation between facilities and demand orders to reduce the computational complexity. Finally, an example is investigated to demonstrate the adaptability of the proposed model and the solution method.
Original languageEnglish
Title of host publication2023 International Conference on Intelligent Transportation and Logistics with Big Data & The Eleventh International Forum on Decision Sciences
Publication statusAccepted/In press - 2 Aug 2023

Keywords

  • personalized medicine supply chain
  • multiobjective optimization
  • uncertainty
  • mixed integer linear programming
  • K-means algorithm

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