Success of companies in the scientific services sector highly relies on the effective scheduling of operations as large numbers of samples from customers are received and analyzed and reports are generated for each sample. Therefore, it is extremely important to efficiently use all the various resources (labor and machine) for such facilities to remain competitive. This study focuses on the development of an algorithm to schedule operations in an actual large scale scientific services plant using models based on multicommodity flow (MCF) and integer linear programming (IP) techniques. The proposed scheduling algorithm aims to minimize the total turnaround time of the operations subject to capacity, resource, and flow constraints. The basic working principles of the optimization-based algorithm are illustrated with a small representative case study, while its relevance and significance are demonstrated through another case study of a real large scale plant. In the latter case study, the algorithms results are compared against historical data and results obtained by simulating the current policy implemented in the real plant, i.e., first-come, first-served. Besides obtaining significantly better results in terms of turnaround time, the results of the algorithm also displayed less variance when compared to historical data.