A multi-objective production planning problem with the consideration of time and cost in clinical trials. (15th June 2019)

• Record Type: Journal Article
• Authors: Zhao, Hui; Huang, Edward; Dou, Runliang; Wu, Kan
• Is Part Of: Expert systems with applications. Volume 124(2019)
• Page Start: 25
• Page End: 38
• Publication Date: 2019-06-15
• Subjects: Supply chain management -- Clinical trial -- Production and distribution planning -- Multi-stage stochastic programming -- Progressive hedging algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.01.038
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The proposed algorithm gives better solutions than the direct solution approach. The operational cost decreases with the increase of the production quantity. We quantify the tradeoff between the duration and the total cost of clinical trials. The tradeoff can be used to determine the optimal production quantity. Abstract: Under 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.