The Product Test Scheduling Problem

This research focused on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Often, these products must receive a certification from prescribed regulatory agencies at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the makespan while ensuring that technicians are available when needed to complete in-process and at-completion inspections. We refer to this as the product test scheduling problem. We first formulated a mixed-integer linear programming (MILP) model to identify the optimal solution to this problem and solve it using a commercial optimization package. We also present a genetic algorithm (GA) solution methodology that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the merits and consistency of the MILP and GA solution approaches across a number of scenarios

Project Evaluation and Selection with Task Failures

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154433/1/poms13107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154433/2/poms13107.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154433/3/poms13107-sup-0001-Appendix.pd

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Cost Evaluation and Portfolio Management Optimization for Biopharmaceutical Product Development

The pharmaceutical industry is suffering from declining R&D productivity and yet biopharmaceutical firms have been attracting increasing venture capital investment. Effective R&D portfolio management can deliver above average returns under increasing costs of drug development and the high risk of clinical trial failure. This points to the need for advanced decisional tools that facilitate decision-making in R&D portfolio management by efficiently identifying optimal solutions while accounting for resource constraints such as budgets and uncertainties such as attrition rates. This thesis presents the development of such tools and their application to typical industrial portfolio management scenarios. A drug development lifecycle cost model was designed to simulate the clinical and non-clinical activities in the drug development process from the pre-clinical stage through to market approval. The model was formulated using activity-based object-oriented programming that allows the activity-specific information to be collected and summarized. The model provides the decision-maker with the ability to forecast future cash flows and their distribution across clinical trial, manufacturing, and process development activities. The evaluation model was applied to case studies to analyse the non-clinical budgets needed at each phase of development for process development and manufacturing to ensure a market success each year. These cost benchmarking case studies focused on distinct product categories, namely pharmaceutical, biopharmaceutical, and cell therapy products, under different attrition rates. A stochastic optimization tool was built that extended the drug development lifecycle cost evaluation model and linked it to combinatorial optimization algorithms to support biopharmaceutical portfolio management decision-making. The tool made use of the Monte Carlo simulation technique to capture the impact of uncertainties inherent in the drug development process. Dynamic simulation mechanisms were designed to model the progression of activities and allocation of resources. A bespoke multi-objective evolutionary algorithm was developed to locate optimal portfolio management solutions from a large decision space of possible permutations. The functionality of the tool was demonstrated using case studies with various budget and capacity constraints. Analysis of the optimization results highlighted the cash flow breakdowns across both activity categories and development stages. This work contributed to the effort of providing quantitative support to portfolio management decision-making and illustrated the benefits of combining cost evaluation with portfolio optimization to enhance process understanding and achieve better performance

Planning and scheduling in pharmaceutical supply chains

Master'sMASTER OF ENGINEERIN

Biopharmaceutical drug development modeling and portfolio management.

Current pressures of cost and speed to market are driving the need for more effective means of assessing the value and risks of drug portfolios. This thesis presents research to generate a prototype computer-aided tool to predict the process and business outcomes for portfolios of biopharmaceutical drugs proceeding through the development pathway. The tool was built using a discrete-event simulation package, thus facilitating the dynamic nature of drug development decisions to be captured. The framework uses a hierarchical approach to incorporate the interactions between drug development activities, the available resources and databases of information. In addition to the business and process issues, the risks involved in the process of drug development have also been incorporated into the tool. The application of the tool for assessing drug portfolios under uncertainty is demonstrated via case studies. In the first, the tool was used to perform sensitivity and scenario analysis on the portfolio net present value (NPV). Contour plots were generated that provide the ability to plan for a range of contingencies including uncertainties in manufacturing efficiencies, product demand and the market share captured. The second case study was used to assess the impact of different manufacturing strategies on the portfolio NPV under uncertainty. This example was based on a biopharmaceutical company considering whether to risk building a facility for the commercial manufacture of its antibodies and if so, when to start building, or whether to rely on a contract manufacturer throughout the development cycle and market manufacture. The effects of uncertainties were analysed using Monte Carlo simulation methods. The study highlighted the benefits of incorporating uncertainties when ranking different strategies. The third case study looked at the selection of drug candidates for a drug portfolio. The risk and reward of different portfolios were computed using Monte Carlo simulations. The 'Efficient Frontier' method was used to select an optimal portfolio. The thesis illustrate the benefits of using such a tool to investigate the uncertainty and value of different development strategies and to assist in the process of decision making in the context of both business and process aspects