Highly regulated industries such as pharmaceuticals and agrochemicals face the challenge of maintaining a 0continuous stream of new products. This is difficult because of low probabilities of technical success, high development costs, uncertain market impact, a scarcity of good new product ideas, and limited human and capital resources available to develop them. The problem of evaluating and selecting which new products to develop and then of sequencing or of scheduling them is complicated further by the presence of dependencies between products both in the market place and in the development process itself. This study proposes a portfolio management approach that selects a sequence of projects, which maximizes the expected economic returns at an acceptable level of risk for a given level of resources in a new product development pipeline. A probabilistic network model of distinct activities is used to capture all the activities and resources required in the ''process'' of developing a new drug. A prioritization scheme suggesting sequences for developing new independent drug candidates with unlimited resources is generated with a conventional bubble chart approach. These sequences initiate a genetic algorithm (GA)-based search for the optimal sequence in the presence of product dependencies and limited resources. By statistically evaluating the sequences generated during the GA search using a discrete event simulation model, it is possible to construct an economic reward-risk frontier that illustrates the trade-offs between expected rewards and risks. The model ideally is suited to answer various ''what if'' questions relative to changes in the resource level on pipeline performance. The methodology is illustrated with an industrially motivated case study, involving nine interdependent new product candidates targeting three diseases. The dramatic results yield a candidate sequence with an expected return 28 percent higher than the sequence suggested by the bubble chart approach at almost the same level of risk. The synergism among the candidate dependencies, pipeline resources, and economic and technical uncertainties demonstrates the necessity of a computation-ally intensive approach if the best development strategy is to be realized.

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With increased product specialization within the chemical-processing industries