In this paper, we describe the use of mathematical programming methods for automated schedule generation. Our method creates production schedules that encompass all necessary process constraints and span a sufficiently large time scale to produce statistically meaningful results. We illustrate the approach using a new industrial biologics facility. In Part 1 of the article, we describe the process and results of the study. In Part 2, we will summarize the formulation, compare our approach to discrete event simulation, and discuss the algorithmic methods used to produce high quality production schedules.
A large-scale biologics facility represents an enormous capital investment. When the cost of the underlying research effort required to discover and gain approval for a drug is considered, this investment can be considerably higher. The ability to predict, analyze, and improve the performance of such a large capital investment represents a huge business opportunity. At a minimum, predicting the expected plant capacity with some level of confidence is necessary to ensure the quality of the design. Far more valuable is the ability to actually study the details of everyday plant operation while still in the design phase. If potential operational problems or bottlenecks can be identified at this point, the design can be improved to mitigate or even eliminate anticipated problems. This level of detailed analysis also can determine whether the anticipated need for a newly approved drug can be met by time sharing production at an existing facility. Thus, the economic payback for developing a high fidelity model capable of performing detailed analysis of process operations can be many times the cost of the development effort.
Even when the chemistry of a process is reliably known, it is not possible to effectively analyze the performance of a large-scale biologics facility unless detailed operational schedules can be produced. Understanding the dynamic behavior of the process requires fine resolution of the timeline, although, as shown in the results in this article, some of the phenomena of interest only emerges over long time scales. This is because of the batch nature of the processes, intermittent material storage in process vessels, biological variability, and the need to perform certain periodic maintenance operations on critical pieces of equipment. Thus, unless detailed schedules can be produced— schedules that satisfy all of the constraints associated with the process—even such basic properties as plant capacity cannot be accurately predicted.
(Bristol-Myers Squibb, Inc. )
In this paper, we describe the results of using mathematical programming methods for automated schedule generation to address the above goals. Our method creates production schedules that encompass all necessary process constraints and that span a sufficiently long time scale to produce statistically meaningful results. Furthermore, the algorithm used to solve the mathematical programming formulation uses a Monte Carlo selection of mutually exclusive terms that represents a sampling of stochastic parameters. This provides a means of addressing process uncertainty on realistic problem sizes. We illustrate the approach using a new industrial biologics facility. The data have been altered to protect proprietary technology without changing the intellectual significance of the results. In Part 1, we describe the process and results of the study. In Part 2, we will summarize the formulation, compare our approach to discrete event simulation, and discuss the algorithmic methods used to produce high quality production schedules.