(708b) Multi-Mode Resource Constrained Project Scheduling for Pharmaceutical R&D
The multi-mode resource constrained project scheduling problem (MMRCPSP), a very well-studied in the literature archetypal optimization problem, has a broad application in planning and process level enterprise decision problems . The MMRCPSP was first introduced in the context of optimizing pharmaceutical research projects by Kolisch et al. , where the authors proposed two heuristics. Other works later implemented MILP models based on MMRCPSP within a heuristic simulation-optimization framework for addressing R&D pipeline management [3,4]. While some studies viewed the single product development as a resource constrained problem , other studies on portfolio-wide optimization are using stochastic programming methods . For example, Colvin et al.  addressed the similarities shared between R&D planning and RCPSP, while they used a stochastic programming framework to account for the success or not of various clinical trials.
Having appropriately codified the R&D activities that must be carried out before any new pharmaceutical product can undergo its associated clinical trials and planned product launch, we propose here a new mixed integer linear optimization model to solve the portfolio-wide scheduling and resourcing problem for pharmaceutical R&D. The model augments the archetypal MMRCPSP to account for important realities faced by practitioners in this space, including among others inter-activity overlaps beyond standard precedence relationships, resources of mixed (both intensive and extensive) type, as well as optional activities. We demonstrate the tractability of this model and its ability to address portfolio-wide instances, while we deploy a decision support tool for the systematic and largely automated derivation of optimal activity schedules and resource allocations. By utilizing the tool under different input instances, we can also conduct various strategic analyses to assess the systemâs ability to cope with sudden changes in portfolio size and/or resource availability.
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