Parseh Designers Transportation Research Institute Quarterly Journal of Transportation Engineering 2008-6598 10 4 2019 06 22 A robust bi-objective model for integrated blood supply chain network design considering transshipment between facilities under uncertainty A robust bi-objective model for integrated blood supply chain network design considering transshipment between facilities under uncertainty 737 770 63214 FA Sara Cheraghi MSc Student, School of industrial engineering, Iran University of science and technology, Tehran, Iran Seyed Mehdi Hoseini Motlagh Iran Science and Technology University Mohammadreza Ghatreh Samani Phd Student, School of industrial engineering, Iran University of science and technology, Tehran, Iran Journal Article 2017 03 16 Possessing a considerable portion of the cost of any health system, blood supply chain is one of the most critical parts of the system. Accordingly, any improvement in the performance of a blood supply chain can result in significantly improved performance as well as cost of the health systems. To have an efficient blood supply chain, appropriate planning is of great significance. This paper puts forward a bi-objective mixed-integer linear programming model for designing blood collection, production, inventory control and distribution under uncertainty with the need for making both strategic and tactical decisions simultaneously over multiple periods of planning. Moreover, blood transshipment between regional blood centers is accounted for in this work. To deal with the proposed bi-objective model, the epsilon-constraint method is devised, and to capture demand uncertainty, a Light robust approach is applied. The usefulness of the concerned model and its solution technique, which is a combination of epsilon-constraint and Light robust methods, is then evaluated via a set of numerical examples, and also, the sensitivity analysis is provided. We close the research while comparing the performance of both deterministic and robust models based on the network total cost, a performance measure including both constraint violation cost (feasibility robustness) and objective functions (optimality robustness) under a specific realization. The results imply that the robust approach strongly outperforms the deterministic one. Possessing a considerable portion of the cost of any health system, blood supply chain is one of the most critical parts of the system. Accordingly, any improvement in the performance of a blood supply chain can result in significantly improved performance as well as cost of the health systems. To have an efficient blood supply chain, appropriate planning is of great significance. This paper puts forward a bi-objective mixed-integer linear programming model for designing blood collection, production, inventory control and distribution under uncertainty with the need for making both strategic and tactical decisions simultaneously over multiple periods of planning. Moreover, blood transshipment between regional blood centers is accounted for in this work. To deal with the proposed bi-objective model, the epsilon-constraint method is devised, and to capture demand uncertainty, a Light robust approach is applied. The usefulness of the concerned model and its solution technique, which is a combination of epsilon-constraint and Light robust methods, is then evaluated via a set of numerical examples, and also, the sensitivity analysis is provided. We close the research while comparing the performance of both deterministic and robust models based on the network total cost, a performance measure including both constraint violation cost (feasibility robustness) and objective functions (optimality robustness) under a specific realization. The results imply that the robust approach strongly outperforms the deterministic one. https://jte.sinaweb.net/article_63214_4636e611824778b40ab4243c0a1549f4.pdf