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dc.contributor.authorZhao, Huien_US
dc.contributor.authorHuang, Edwarden_US
dc.contributor.authorDou, Runliangen_US
dc.contributor.authorWu, Kanen_US
dc.identifier.citationZhao, H., Huang, E., Dou, R. & Wu, K. (2019). A multi-objective production planning problem with the consideration of time and cost in clinical trials. Expert Systems With Applications, 124, 25-38.
dc.description.abstractUnder increasingly challenging circumstances, pharmaceutical companies try to reduce the overproduction of clinical drugs, which is commonly seen in the pharmaceutical industry. When the overproduction is simply reduced without an efficient coordination of the inventories in the supply chain, the stock-outs at clinical sites and clinical trial delay can hardly be avoided. In this study, we propose a multi-objective model to optimize the production quantity, where the clinical trial duration and the total production and operational costs are minimized. The problem is formulated as a multi-stage stochastic programming model to capture the dynamic inventory allocation process in the supply chains. Since this problem's solving time and required memory can increase significantly with the increase of the stage and scenario numbers, the progressive hedging algorithm is applied as the solution approach in this paper. In the numerical experiments, we study this algorithm's performance and compare the solving efficiency with the direct solution approach. In addition, we identify the optimal production quantity of clinical drugs and give a discussion about the tradeoffs between the clinical trial delay and total cost.en_US
dc.description.sponsorshipEconomic Development Board (EDB)en_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA multi-objective production planning problem with the consideration of time and cost in clinical trialsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.subject.keywordsSupply Chain Managementen_US
dc.subject.keywordsClinical Trialen_US
dc.description.acknowledgementThis research is supported in part by the GSK-Singapore Partnership for Green and Sustainable Manufacturing under Grant M406884.en_US
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