Integer Optimization Model and Algorithm for the Stem Cell Culturing Problem. (April 2022)

• Record Type: Journal Article
• Authors: Park, Jongyoon; Han, Jinil; Lee, Kyungsik
• Is Part Of: Omega. Volume 108(2022)
• Publication Date: 2022-04
• Subjects: Stem cell culturing problem -- Resource constrained scheduling -- Integer optimization -- LP-based heuristic algorithm; Management -- Periodicals; 658.4005
• DOI: 10.1016/j.omega.2021.102566
• Languages: English
• ISSNs: 0305-0483

Abstract

Highlights: A novel scheduling problem arising in the stem cell therapy industry is introduced. An integer optimization model based on the concept of a daily mode is proposed. An LP-based heuristic algorithm based on the proposed model is also proposed. Computational tests show the proposed algorithm is both effective and efficient. Abstract: In this paper, we present a novel scheduling problem, the stem cell culturing problem (SCP), which is identified in an attempt to improve the productivity of a manufacturing system producing a commercialized autologous stem cell therapeutic product for treating an incurable disease. For a given therapeutic product along with the corresponding manufacturing process, which is called the stem cell culture process, the SCP is the problem of maximizing the number of produced units of a therapeutic product during a given horizon while satisfying the operational constraints stem from unique characteristics of the stem cell culture process and the related resources. After we formally define the SCP, we show that the SCP is NP-hard in the strong sense. Then, we present an integer optimization model based on the concept of a daily mode. The computational performance of the proposed model is analyzed with a general purpose integer optimization software. Moreover, based on this model, we propose an efficient LP-based heuristic algorithm which yields provably good solutions within a short time. Through computational experiments, we demonstrate that the proposed algorithm is both effective and efficient in solving practically-sized instances.