Optimal sequencing and resource allocation in research and development projects

Article Abstract:

An R&D model for facilitating the study of activity sequencing and resource allocation is presented. The basic characteristics of R&D are incorporated into the model, namely, the dynamic selection of the R&D project's activities and technological specifications, and the use of stopping time as a decision variable to be calculated endogenously. The group of optimal sequencing of activities maximizing the project's expected net present value is characterized, the potential for including learning aspects into the model is assessed, and an associated resource allocation problem is analyzed. The model demonstrates that a greedy-optimal resource allocation procedure that increases the project's expected net present value can be implemented efficiently in certain instances.

Industrial research, Sequential analysis

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A stochastic optimization model to improve production planning and R&D resource allocation in biopharmaceutical production processes

Article Abstract:

A stochastic optimization approach based on engineering design and aggregate production planning models is proposed for optimizing bioprocess operations. Specifically, a Markov decision process model of the relationship between process changes and financial returns is developed to minimize the manufacturing costs and enhance research and development planning of production-scale biopharmaceutical processes, in this case, cell culture systems for tissue plasminogen activator. The model can also be applied to other types of cell culture systems via parameter modifications. Since it integrates physical process control and operating performance control, the hybrid modeling scheme facilitates a concurrent process design approach.

Operations research, Research, Management science, Case studies, Production processes, Process control, Tissue plasminogen activator, Markov processes, Production planning, Stochastic programming

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Resource allocation models with risk aversion and probabilistic dependence: offshore oil and gas bidding

Article Abstract:

The bidding for offshore oil and gas leases is a resource allocation problem that involves very high risks and uncertainties. Organizations can facilitate the bidding process by using two new decision analysis-models: the block-on-prospect-dependence model, and the proportional-prospect-net-present-value-dependence model. The models take into account the lease values' risk aversion and probabilistic dependence. The decision variables are bid levels and partnership shares. The comparison of the models' optimal solutions provides insight into the use of independence and conditional dependence as dependence approximations.

Crude petroleum and natural gas, Management, Petroleum industry, Gas industry, Oil and gas leases

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