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Managing concurrency in cyclical projects under stochastic task environments: Vaccine development projects during
Nagesh N Murthy1, Haikady N Nagaraja2, Hossein Rikhtehgar Berenji3
1Lundquist College of Business University of Oregon Eugene Oregon USA.
Overlapping vaccine development tasks can accelerate project completion, even with potential rework. Analyzing stochastic task durations reveals optimal overlap strategies to minimize overall project time.
Area of Science:
- Operations Research
- Project Management
- Biopharmaceutical Development
Background:
- Accelerated vaccine development, exemplified by COVID-19, necessitates overlapping project activities.
- Overlapping tasks in cyclical projects can lead to rework if downstream activities begin before upstream information is finalized.
- Existing models for cyclical projects often overlook the stochastic nature of task durations and its impact on overlap efficiency.
Purpose of the Study:
- To develop a framework for analyzing the relationship between stochastic activity overlap and rework in cyclical projects.
- To optimize project completion time by determining the impact of overlap duration on stochastic activities.
- To apply this framework to vaccine development planning, considering unique project scenarios.
Main Methods:
- Developed a model to analyze the interplay of stochastic overlap duration and rework.
- Investigated the impact of overlap on minimizing expected project completion time.
- Determined optimal task start times based on net gain/loss from overlap and stochastic dominance.
Main Results:
- Overlapping tasks, potentially inefficient in deterministic settings, can be beneficial with stochastic durations.
- Optimal overlap strategies depend on net gain/loss from overlap and task stochastic dominance.
- Project rescheduling flexibility is beneficial in scenarios with net loss from overlap and under specific conditions with net gain.
Conclusions:
- Stochastic analysis provides a more accurate approach to planning overlapping activities in vaccine development.
- The framework offers insights into optimizing project timelines by managing task overlap and rework.
- A scalable heuristic is proposed for effective planning in complex project networks.
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