GOALI: Stochastic Optimization for the Scheduling of Tests in the Development of New Chemical Products

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Abstract - Grossman - 9520153 To introduce a new chemical in the market there are tests that have to be performed by and R&D organization. These are mostly regulatory requirements such as environmental and safety tests. If a potential product fails any test it cannot reach the market. Scheduling the tests for the development of new products has important economic implications. The fundamental trade-off is between greater sales and income that result from longer schedules. One of the complications in the scheduling is its combinatorial nature, especially when the number of potential products is large. In addition, there are uncertainties in the costs of the tasks, probabilities of success of the tasks, in the income of the products. Furthermore, there are precedence constraints for the tests, constrains on resources for performing the tests, and on the capacity and investment capital for the manufacturing facilities. This project is concerned with the study of stochastic optimization models and methods fmr the optimal scheduling of testing tasks for the development of new products. An effective optimization model will be developed for the optimal scheduling of testing tasks that maximizes New Present Value and that can capture the major trade-off and decisions involved in this problem, as well as the relevant constraints. Preliminary work has indicated that one can model the problem as a large-scale mixed -integer linear program that uses continuous time representation and discrete distributions for the stochastic parameters. However, this model can become computationally expensive for modest number of products and tests. Therefore, computational strategies will be investigated that rely on the use of cutting planes, decomposition and logic. The integration of the optimization model for testing with the model of the process network of the manufacturing company in order to assess the effect of plant capacity constraints as well as to identify investment opportunities for mo difying designs or building the new facilities will also be explored. Another problem of interest will be to determine the required modifications in existing facilities to accommodate the production of new produats. While most of the theoretical and computational work will be performed at Carnegie Mellon, there will be extensive interaction with DowElanco. Initially it will consist of information gathering to fully understand the issues involved in this problem. At a later stage, realistic case studies aimed at agricultural chemicals will be developed to test the proposed methods.