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3178. Multiobjective robust optimization approaches for personalized medicine supply chain network design under failure rate uncertainty

Invited abstract in session TA-15: Location planning in healthcare, stream OR in Health Services (ORAHS).

Tuesday, 8:30-10:00
Room: 18 (building: 116)

Authors (first author is the speaker)

1. Meng Wan
School of Management, Harbin Institute of Technology
2. Songsong Liu
School of Management, Harbin Institute of Technology
3. Richard Allmendinger
Alliance Manchester Business School, The University of Manchester

Abstract

Nowadays, the development and practice of personalized medicine become strategically important. It focuses on individual patient needs and conditions, and due to the shelf life of personalized medicine, it has high requirements on temperature in production and transportation. Designing a stable personalized medicine supply chain network to efficiently complete the manufacturing and delivery of drugs is crucial to reduce supply chain costs and improve overall efficiency to promote positive industry development. A multi-period, multiobjective production and distribution network of personalized medical supply chain is established in this paper, considering distribution, manufacturing, logistics, time and logic constraints, to minimize cost and time and maximize demand coverage. With the influence of manufacturing failure rate as an uncertainty factor, the robust optimization method is used to deal with the uncertainties. In this paper, two clustering-based decomposition methods are proposed as the solution approaches, which are shown to solve models more efficiently than the proposed MILP model. Monte Carlo simulation is used to validate the performance the robust optimization approaches. Finally, the proposed model is extended to consider the fairness on waiting time among patients. The research outcomes could help to improve the operational efficiency of the personalized medicine supply chain and promote the sustainable development of the personalized medicine industry.

Keywords

Status: accepted

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